text:
CAA commissioner Tom Yeager: Realignment rumors a "non-story"
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By Patrick Stevens The Washington Times - March 26, 2012, 05:11PM
Tom Yeager didn't seem particularly interested in veiling his disdain Monday afternoon.
A day after a CBSSports.com report asserted two members of the CAA (George Mason and Virginia Commonwealth) were in discussions to join the Atlantic 10, the CAA commissioner said it was a "non-story" during a media teleconference.
"We're in pretty good shape," Yeager said. "We're not grasping at straws. Short of of the lottery people coming in and telling them they won the lottery, I don't see any of our teams moving."
Both Mason and Virginia Commonwealth have reached the Final Four since 2006 and consistently rank among the top three teams in the conference. Mason has reached three NCAA tournaments in the last seven seasons, while VCU won tournament games in 2007, 2011 and 2012.
Earlier in the day, Mason athletic director Tom O'Connor said in a statement his school "has not had any formal discussions nor have we been invited to join the Atlantic 10."
Yeager acknowledged postseason success made both schools attractive, but said he had not heard from Atlantic 10 commissioner Bernadette McGlade on the subject.
"If there was any truth that this was an in-depth process, she would have initiated contact a long time ago," Yeager said.
Yeager said the possibility of conference membership change "generated the most conversation" during an already scheduled conference call between the league's athletic directors on Monday.
"Both schools indicated there were no truth to the rumors," Yeager said. "They are not exploring other options. In combination with conversations over the last couple months and weeks, they're not looking to go anywhere else and they don't have an offer that would turn their heads. We didn't hold hands and take blood oaths or anything. I take them for their word."
The 12-school CAA has had the same membership since the 2005-06 school year, when Georgia State and Northeastern were added. The 14-school Atlantic 10 will lose Temple to the Big East in the 2013-14 school year.
In addition to George Mason and Virginia Commonwealth, Butler was linked to the Atlantic 10 in the CBSSports.com report as well.
Conference realignment emerged as an omnipresent possibility in the last two years, with several schools changing leagues. Much of that's occurred in the six most powerful football conferences, with a possible trickle-down now reaching other leagues.
"Every time you guys tweet something or somebody else tweets something, it sends off ripples through everything. …," Yeager said. "Our jobs would be a lot easier if all these things weren't coming out of left field, which seems to be every 25 minutes."
– Patrick Stevens
Patrick Stevens has covered Maryland and other Mid-Atlantic college sports for more than a decade. You can reach him at [email protected].
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PlayerUnknown's Battlegrounds Vikendi map is huge and finding the best loot locations can be tiring. In this guide we bring you the best loot locations with the map image, they are Dobro Mesto, Krichas and Movatra in West, Zabava, Cosmodrome and Trevno in North, Cement Factory, Cantra, Volnova, Winery and Milnar in the South and Goroka, Podvosto, Castle and Mount Kreznic in the center area. Among all these locations few are very important one that offers you the highest amount of loots.
PUBG Vikendi Map important loot locations offer you the highest quality loots, the map is divided into four regions, Western, Northern, Southern and Center. You can read below and find which the most important places to find loot are.
If you are heading west then Dobro Mesto should be your first choice to land, this place has a lot of houses you can explore to get the best loots in Vikendi. Land near the fountain and explore the houses on it right. A lot of loots are inside the houses, you will get Vest, Shotguns, Rifles, etc. Buildings marked with a red arrow in the map are important to look first. The second arrow in the above image takes you to an upper floor of big building which a lot of loot has scattered on the floor and in racks. Keep this a priority if you land in this region.
Krichas is on the North-west side, another amazing location to find lots of loots in PUBG Vikendi map.
Movtra is located on the South-west side of the map near Dino Park. Explore this region for loots.
The West part is filled with rich areas that offer you high-quality loots, you can explore all three marked locations to increase your chances of winning.
Zabava falls in the Northern region of Vikendi between Port and Cosmodrome. There is an intersection surrounded by buildings, explore both sides.
Towards the East, after crossing the bridge if you are walking from Zabava you will reach Cosmodrome. It is a pretty big area to explore.
Towards the East End of the map near the sea you can easily find Trevno. There is a bunch of buildings that can offer you really good and high-quality loots.
Among the three regions above Zabava and Trevno are the most important ones, they offers you high quality loots.
Further down towards the South-East side you will spot Cement Factory. This place is a good destination to explore if you are landing nearby.
Cantra is a small town located near the seaside in the southeast section of the map. It is pretty easy to spot just follow the road towards the south of Cement Factory.
Volnova is near to Cantra, you cannot ignore this region. It has high-quality loots, you can ignore Cantra if you want and visit Vonova first.
Winery is located on the south end of the map, you can follow the road from Volnova to reach this spot.
Continue following the end road from Winery and you will reach Milnar another good spot to hunt down some nice loot.
Among all four regions Volnova and Milnar are the most important ones, try exploring these regions first.
This is place is located on the east side of Dobro Mesto, look for a settlement near a small lake. It is an important location to loot so check all the buildings.
Further towards the east side after crossing Mount Kreznic you will find Podvosto, a small line of a house in this region is near the river. Look into these places for loot.
Castle is located on the south of Podvosto and it is an important location to search, it is very easy to locate because it is situated on an island. It is also popular among players so there are max chances if you reach late you will have to fight a sniper.
I am adding this location in the end, go here if you land nearby. It will give you loot but it is not the important one, there are some building between the mountains.
The center area of Vikendi has more important locations similar to the western region. You can explore Goroka, Podvosto, and Castle, these three have high-quality loots. Remember one thing the location marked Important one is also the riskiest ones, you will be encounter a lot of players heading to these places. So if you land late you can try the less important ones and get decent amount of loot to progress in the game.
For more updates on PUBG you can scan our PUBG Wiki guide which covers tons of topics on the game.


text:
\section{Introduction}

\begin{figure}[t]
   \begin{center}
      \includegraphics[width=0.9\linewidth]{figure/intro.pdf}
   \end{center}
      \caption{Interpretation of our progressive referring segmentation method. (a) Input referring
      expression and image. (b) The model first perceives all the entities
      described in the expression based on entity words and attribute words,
      e.g., ``man'' and ``white frisbee'' (orange masks and blue outline). (c) After finding out all the
      candidate entities that may match with input expression, relational word
      ``holding'' can be further exploited to highlight the entity involved with the
      relationship (green arrow) and suppress the others which are not involved.
      (d) Benefiting from the relation-aware reasoning process, the referred entity is found
      as the final prediction (purple mask). (Best viewed in color).}
   \label{fig:intro}
\end{figure}

As deep models have made significant progresses in vision or language tasks~\cite{liu2020parsing}\cite{liao2019gps}\cite{jiang2019psgan}\cite{gao2019adversarialnas}\cite{vaswani2017attention}, fields combining them~\cite{ren2020scene}\cite{liao2019ppdm}\cite{zheng2019reasoning} have drawn great attention of researchers.
In this paper, we focus on the \textit{referring image segmentation} (RIS)
problem whose goal is to segment the entities described by a natural
language expression. Beyond traditional semantic segmentation, RIS is a more challenging problem since the expression can
refer to \emph{objects} or \emph{stuff} belonging to any category in various language
forms and contain diverse contents including entities, attributes and
relationships. As a relatively new topic that is still far from being solved, this
problem has a wide range of potential applications such as interactive
image editing, language-based robot controlling, etc.
Early works~\cite{hu2016segmentation}\cite{liu2017recurrent}\cite{margffoy2018dynamic}\cite{li2018referring}
tackle this problem using a straightforward concatenation-and-convolution
scheme to fuse visual and linguistic features.
Later works~\cite{shi2018key}\cite{chen2019see}\cite{ye2019cross} further
utilize inter-modality attention or self-attention to learn only visual embeddings
or visual-textual co-embeddings for context modeling. However, these methods still
lack the ability of exploiting different types of informative words in the
expression to accurately align visual and linguistic features, which is crucial to
the comprehension of both expression and image.

As illustrated in Figure~\ref{fig:intro} (a) and (b),
if the referent, i.e., the entity referred to by the expression, is described by
``The man holding a white frisbee'', a reasonable solution is to tackle the referring
problem in a progressive way which can be divided into two stages. First, the model is supposed to
perceive all the entities described in the expression according to entity words and
attribute words, e.g., ``man'' and ``white frisbee''. Second, as multiple entities of the
same category may appear in one image, for example, the three men in Figure~\ref{fig:intro} (b),
the model needs to further reason relationships among entities to highlight the referent
and suppress the others that are not matched with the relationship cue given in the expression.
In Figure~\ref{fig:intro} (c), the word ``holding'' which associates ``man'' with ``white frisbee''
powerfully guides the model to focus on the referent who holds a white frisbee rather than the other two men, which assists in making correct prediction in Figure~\ref{fig:intro} (d).

Based on the above motivation, we propose a Cross-Modal Progressive Comprehension (CMPC)
module which progressively exploits different types of words in the expression to segment the referent
in a graph-based structure. Concretely, our CMPC module consists of two stages.
First, linguistic features of entity words and attribute words
(e.g., ``man'' and ``white frisbee'') extracted from
the expression are fused with visual features extracted from the image to form
multimodal features where all the entities considered by the expression are perceived.
Second, we construct a fully-connected spatial graph where each vertex corresponds to an
image region and feature of each vertex contains multimodal information of the entity.
Vertexes require appropriate edges to communicate with each other. Naive edges treating
all the vertexes equally will introduce abundant information and fail to distinguish the referent from other candidates.
Therefore, our CMPC module employs relational words (e.g., ``holding'') of the expression as a group of routers
to build adaptive edges to connect spatial vertexes, i.e., entities, that are involved with
the relationship described in the expression. Particularly, spatial vertexes (e.g., ``man'') that have
strong responses to the relational words (e.g., ``holding'') will exchange information with others (e.g., ``frisbee'') that also correlate with the
relational words. Meanwhile, spatial vertexes that have weak responses to the relational words
will have less interaction with others. After relation-aware reasoning on the multimodal
graph, feature of the referent can be highlighted while those of the irrelevant
entities can be suppressed, which assists in generating accurate segmentation.

As multiple levels of features can complement each other~\cite{li2018referring}\cite{ye2019cross}\cite{chen2019see},
we also propose a Text-Guided Feature Exchange (TGFE) module to exploit
information of multimodal features refined by our CMPC module from different levels.
For each level of multimodal features, our TGFE module utilizes linguistic features
as guidance to select useful feature channels from other levels to realize information
communication. After multiple rounds of communication, multi-level features are further
fused by ConvLSTM~\cite{xingjian2015convolutional} to comprehensively integrate
low-level visual details and high-level semantics for precise mask prediction.

Our contributions are summarized as follows:
(1) We propose a Cross-Modal Progressive Comprehension (CMPC) module which first
perceives all the entities that are possibly referred by the expression, then
utilizes relationship cues of the input expression to highlight the referent while
suppressing other irrelevant ones, yielding discriminative feature representations for the referent.
(2) We also propose a Text-Guided Feature Exchange (TGFE) module to conduct
adaptive information communication among multi-level features under the guidance of
linguistic features, which further enhances feature representations for mask prediction.
(3) Our method achieves new state-of-the-art results on four referring segmentation benchmarks,
demonstrating the effectiveness of our model.

\begin{figure*}[t]
   \begin{center}
      \includegraphics[width=0.85\linewidth]{figure/pipeline.pdf}
   \end{center}
      \caption{Overview of our proposed method. Visual features and linguistic features are first progressively aligned by
      our Cross-Modal Progressive Comprehension (CMPC) module. Then multi-level multimodal features are fed into our Text-Guided Feature Exchange (TGFE) module for information communication across different levels. Finally, multi-level features are fused with ConvLSTM for final prediction.}
   \label{fig:pipeline}
\end{figure*}

\vspace{-1mm}
\section{Related Work}
\vspace{-1mm}

\subsection{Semantic Segmentation}
Semantic segmentation has made a huge progress based on
Fully Convolutional Networks (FCN)~\cite{long2015fully}.
FCN replaces fully-connected layers in original classification
networks with convolution layers and becomes the standard
architecture of the following segmentation methods.
DeepLab~\cite{chen2014semantic}\cite{chen2017deeplab}\cite{chen2017rethinking}
introduces atrous convolution with different atrous rates into FCN
model to enlarge the receptive field of filters and aggregate multi-scale context.
PSPNet~\cite{zhao2017pyramid} utilizes pyramid pooling operations to extract
multi-scale context as well. Recent works such as DANet~\cite{fu2019dual} and
CFNet~\cite{zhang2019co} employ self-attention mechanism~\cite{wang2018non} to
capture long-range dependencies in deep networks and achieve notable performance.
In this paper, we tackle the more generalized and challenging semantic segmentation
problem whose semantic categories are specified by natural language referring expression.

\subsection{Referring Expression Comprehension}
The goal of referring expression comprehension is to localize the entities in the image
which are matched with the description of a natural language expression. Many works
conduct localization in bounding box level.
Liao \textit{et al.}~\cite{liao2019real} performs cross-modality correlation filtering to match multimodal features in real time.
Relationships between vision and language modalities~\cite{hu2017modeling}\cite{yang2019cross}
are also modeled to match the expression with most related objects. Modular networks are
explored in~\cite{yu2018mattnet} to decompose the referring expression into subject, location
and relationship so that the matching score is more finely computed.

Beyond bounding box, the referred object can also be localized more precisely with segmentation
mask. Hu \textit{et al.}~\cite{hu2016segmentation} first proposes the referring segmentation problem and generates
the segmentation mask by directly concatenating and fusing multimodal features from CNN and
LSTM~\cite{hochreiter1997long}. In~\cite{liu2017recurrent}, multimodal LSTM is employed to
sequentially fuse visual and linguistic features in multiple time steps. Based on~\cite{liu2017recurrent},
dynamic filters~\cite{margffoy2018dynamic} for each word further enhance multimodal features.
Fusing multi-level visual features is explored
in~\cite{li2018referring} to recurrently refine the local details of segmentation mask. As
context information is critical to segmentation task, Shi \textit{et al.}~\cite{shi2018key} utilizes word
attention to aggregate only visual context to enhance visual features.
For multimodal context extraction, cross-modal self-attention is exploited
in~\cite{ye2019cross} to capture long-range dependencies between each image region and
each referring word. Visual-textual co-embedding is explored in~\cite{chen2019see} to measure
compatibility between referring expression and image.
Adversarial learning~\cite{qiu2019referring} and cycle-consistency~\cite{chen2019referring}
between referring expression and its reconstructed caption are also investigated to boost
the segmentation performance. In this paper, we propose to progressively
highlight the referent via entity perception and relation-aware reasoning for
accurate referring segmentation.

\subsection{Graph-Based Reasoning}
It has been shown that graph-based models are effective for context reasoning in many tasks.
Dense CRF~\cite{chandra2017dense} is a widely used graph model for post-processing in image
segmentation. Recently, Graph Convolution Networks (GCN)~\cite{chandra2017dense} becomes popular
for its superiority on semi-supervised classification. Wang \textit{et al.}~\cite{wang2018videos}
construct a spatial-temporal graph using region proposals as vertexes and conduct context reasoning with GCN,
which performs well on video recognition task. Chen \textit{et al.}~\cite{chen2019graph} propose
a global reasoning module which projects visual feature into an interactive space and conducts graph
convolution for global context reasoning. The reasoned global context is projected back to the coordinate space
to enhance original visual feature. There are several concurrent
works~\cite{li2018beyond}\cite{liang2018symbolic}\cite{zhang2019latentgnn} sharing the same idea of
projection and graph reasoning with different implementation details. In this paper, we propose to
regard image regions as vertexes to build a spatial graph where each vertex saves multimodal feature
vector as its state. Information flow among vertexes is routed by relational words in the referring
expression and implemented using graph convolution. After the graph reasoning, image regions can
generate accurate and coherent responses to the referring expression.

\begin{figure*}[htbp]
   \begin{center}
      \includegraphics[width=0.8\linewidth]{figure/reason.pdf}
   \end{center}
      \caption{Illustration of our Cross-Modal Progressive Comprehension module which consists of two stages.
      First, visual features $X$ are bilinearly fused with linguistic features $q$ of entity words and
      attribute words for Entity Perception (EP) stage. Second, multimodal features $M$ from EP stage are fed
      into Relation-Aware Reasoning (RAR) stage for feature enhancement. A multimodal fully-connected graph
      $\mathcal{G}$ is constructed with each vertex corresponds to an image region on $M$. The adjacency matrix
      of $\mathcal{G}$ is defined as the product of the matching degrees between vertexes and relational words in
      the expression. Graph convolution is utilized to reason among vertexes so that the referent could be
      highlighted during the interaction with correlated vertexes.}
   \label{fig:reason}
\end{figure*}

\vspace{-1mm}
\section{Method}
\vspace{-1mm}

Given an image and a natural language expression, the goal of our model is to segment the
corresponding entity referred to by the expression, i.e., the referent.
The overall architecture of our model is illustrated in Figure~\ref{fig:pipeline}.
We first extract the visual features of the image with a CNN backbone and the linguistic features
of the expression with a text encoder.
A novel Cross-Modal Progressive Comprehension (CMPC) module is proposed to progressively highlight
the referent and suppress the others via entity perception and subsequent relation-aware
reasoning on spatial region graph.
The proposed CMPC module is applied to multiple levels of visual features respectively and the corresponding
outputs are fed into a Text-Guided Feature Exchange (TGFE) module to communicate information under the guidance of linguistic modality.
After the communication, multi-level features are finally fused with ConvLSTM~\cite{xingjian2015convolutional}
to make the prediction.
We will elaborate each part of our method in the rest subsections.

\vspace{-1mm}
\subsection{Visual and Linguistic Feature Extraction}
\vspace{-1mm}

As shown in Figure~\ref{fig:pipeline}, our model takes an image and an expression as inputs.
The multi-level visual features are extracted with a CNN backbone and respectively fused with an $8$-D
spatial coordinate feature $O \in \mathbb{R} ^ {H \times W \times 8}$ using a $1 \times 1$ convolution
following prior works~\cite{liu2017recurrent}\cite{ye2019cross}.
After the convolution, each level of visual features are transformed to the
same size of $\mathbb{R} ^ {H \times W \times C_v}$,
with $H$, $W$ and $C_v$ being the height, width and channel dimension of the visual features.
The transformed visual features are denoted as \{$X_3$, $X_4$, $X_5$\} corresponding to the output of
the $3$rd, $4$th and $5$th stages of CNN backbone (e.g., ResNet-101~\cite{he2016deep}).
For ease of presentation, we denote a single level of visual features as $X$ in Sec.~\ref{sec:cmpc}.
The linguistic features $L = \{l_1, l_2, ..., l_T\}$ is extracted with a language
encoder (e.g., LSTM~\cite{hochreiter1997long}), where $T$ is the length
of expression and $l_i \in \mathbb{R} ^ {C_l} (i \in \{1, 2, ..., T\})$ denotes
feature of the $i$-th word.

\subsection{Cross-Modal Progressive Comprehension}
\label{sec:cmpc}
\vspace{-1mm}

As many entities may exist in the image, it is natural to progressively narrow down the candidate set
from all the entities to the actual referent.
In this section, we propose a Cross-Modal Progressive Comprehension (CMPC) module which consists of
two stages, as illustrated in Figure~\ref{fig:reason}.
The first stage is entity perception. We associate linguistic features of entity words and attribute words
with the correlated visual features of spatial regions using bilinear fusion~\cite{BenMUTAN} to obtain the multimodal
features $M\in \mathbb{R} ^ {H \times W \times C_m}$. All the candidate entities are perceived by the fusion.
The second stage is relation-aware reasoning. A fully-connected multimodal graph is constructed
over $M$ with relational words serving as a group of routers to connect vertexes.
Each vertex of the graph represents a spatial region on $M$.
By reasoning among vertexes of the multimodal graph, the responses of the referent
 matched with the relationship cue are highlighted while those of non-referred
ones are suppressed accordingly.
Finally, the enhanced multimodal features $\bar{M_g}$ are further fused with visual and linguistic features.

\textbf{Entity Perception. }Similar to~\cite{yang2019cross}, we classify the words into $4$ types, including entity, attribute, relation and unnecessary word.
A $4$-D vector is predicted for each word to indicate the probability of it being the four types respectively.
We denote the probability vector for word $t$ as $p_t = [p_{t}^{ent}, p_{t}^{attr}, p_{t}^{rel}, p_{t}^{un}] \in \mathbb{R} ^ 4$ and calculate it as:
\begin{equation}
   \label{eq:prob_pred}
   p_t = softmax(W_2\sigma(W_1l_t+b_1)+b_2),
\end{equation}
where $W_1 \in \mathbb{R} ^ {C_n \times C_l}$, $W_2 \in \mathbb{R} ^ {4 \times C_n}$, $b_1 \in \mathbb{R} ^ {C_n}$ and $b_2 \in \mathbb{R} ^ {4}$ are learnable parameters, $\sigma(\cdot)$ is sigmoid function,
$p_{t}^{ent}$, $p_{t}^{attr}$, $p_{t}^{rel}$ and $p_{t}^{un}$ denote the probabilities of word $t$ being the entity, attribute, relation and unnecessary word respectively.
Then the global language context of entities $q \in \mathbb{R} ^ {C_l}$ could be calculated as a weighted combination of the all the words in the expression:
\begin{equation}
   \label{eq:setence_feat}
   q = \sum\limits_{t=1}^{T}{(p_t^{ent}+p_t^{attr})l_t}.
\end{equation}

Next, we adopt a simplified bilinear fusion strategy~\cite{BenMUTAN} to associate $q$ with the visual feature of each spatial region:
\begin{equation}
   \label{eq:mutan}
   M_i = (qW_{3i})\odot (XW_{4i}),
\end{equation}
\begin{equation}
   \label{eq:ta}
   M = \sum\limits_{i=1}^{r}{M_i}
\end{equation}
where $W_{3i} \in \mathbb{R}^{C_l \times C_m}$ and $W_{4i} \in \mathbb{R}^{C_v \times C_m}$ are learnable parameters, $r$ is a hyper-parameter and $\odot$ denotes element-wise product.
By integrating both visual and linguistic context into the multimodal features, all the entities that might be referred to by the expression are perceived appropriately.

\textbf{Relation-Aware Reasoning. }To selectively highlight the referent, we construct a fully-connected graph over the mutimodal features $M$ and conduct reasoning over the graph according to relational cues in the expression.
Formally, the multimodal graph is defined as $\mathcal{G}  = (\mathcal{V}, \mathcal{E}, M_g, A)$ where $\mathcal{V}$ and $\mathcal{E}$ are the sets of vertexes and edges, $M_g = \{m_i\}_{i=1}^{N} \in \mathbb{R} ^ {N \times C_m}$ is the set of vertex features, $A \in \mathbb{R} ^ {N \times N}$ is the adjacency matrix and $N$ is number of vertexes.

Details of relation-aware reasoning is illustrated in the right part of Figure~\ref{fig:reason}.
As each location on $M$ represents a spatial region on the original image, we regard each region as a vertex of the graph and the multimodal graph is composed of $N = H \times W$ vertexes in total.
After the reshaping operation, a linear layer is applied to $M$ to transform it into the features of vertexes $M_g$.
The edge weights depend on the affinities between vertexes and relational words in the referring expression.
Features of relational words $R = \{r_t\}_{t=1}^T \in \mathbb{R} ^{T \times C_l}$ are calculated as:
\begin{equation}
   \label{eq:relation-extract}
   r_t = p_t^{rel}l_t,~~~~t=1, 2, ..., T.
\end{equation}
As shown in Figure~\ref{fig:reason}, adjacency matrix $A$ is formulated as:
\begin{equation}
   \label{eq:inter}
   B = (M_gW_5)(RW_6)^T,
\end{equation}
\begin{equation}
   \label{eq:u1}
   B_1 = softmax(B),
\end{equation}
\begin{equation}
   \label{eq:u2}
   B_2 = softmax(B^T),
\end{equation}
\begin{equation}
   \label{eq:adj_matrx}
   A = B_1B_2,
\end{equation}
where $W_5 \in \mathbb{R} ^{C_m \times C_h}$ and $W_6 \in \mathbb{R} ^{C_l \times C_h}$ are learnable parameters. $B \in \mathbb{R} ^{N \times T}$ is the affinity matrix between $M_g$ and $R$. We apply the softmax function along the second and first dimension of $B$ to obtain $B_1 \in \mathbb{R} ^ {N \times T}$ and $B_2 \in \mathbb{R} ^ {T \times N}$ respectively.
$A$ is obtained by matrix product of $B_1$ and $B_2$.
Each element $A_{ij}$ of $A$ represents the normalized magnitude of information flow from the spatial region $i$ to the region $j$, which depends on their affinities with relational words in the expression.
In this way, relational words of the expression can be leveraged as a group of routers to build adaptive edges connecting vertexes.

After the construction of multimodal graph $\mathcal{G}$, we apply graph convolution~\cite{kipf2016semi} to it as follow:
\begin{equation}
   \label{eq:gcn}
   \bar{M_g} = (A+I)M_gW_7,
\end{equation}
where $W_7 \in \mathbb{R} ^{C_m \times C_m}$ is a learnable weight matrix.
$I$ is identity matrix serving as a shortcut to ease optimization.
The graph convolution reasons among vertexes, i.e., image regions, so that the referent is selectively highlighted according to the relationship cues while other irrelevant ones are suppressed, which assists in generating more discriminative feature representations for referring segmentation.

Afterwards, reshaping operation is applied to obtain the enhanced multimodal
features $\bar{M_g} \in \mathbb{R} ^ {H \times W \times C_m}$.
To incorporate the textual information, we first combine features of all necessary
words into a vector $s \in \mathbb{R} ^ {C_l}$ with the predefined probability vectors:
\begin{equation}
   \label{eq:sentence}
   s = \sum_{t=0}^{T}{(p_t^{ent}+p_t^{attr}+p_t^{rel})l_t}.
\end{equation}
We repeat $s$ for $H \times W$
times and concatenate it with $X$ and $\bar{M_g}$ along channel dimension following with a $1 \times 1$ convolution to get the output features $Y \in \mathbb{R} ^ {H \times W \times C_m}$, which is equipped with multimodal context for the referent.

\begin{table*}[t]
   \centering
   \begin{tabular}{c|ccc|ccc|c|c}
       \hline
        Method & & UNC & & & UNC+ & & G-Ref & ReferIt \\
        & val & testA & testB & val & testA & testB & val & test \\
       \hline
       LSTM-CNN~\cite{hu2016segmentation} & - & - & - & - & - & - & 28.14 & 48.03 \\
       RMI~\cite{liu2017recurrent} & 45.18 & 45.69 & 45.57 & 29.86 & 30.48 & 29.50 & 34.52 & 58.73 \\
       DMN~\cite{margffoy2018dynamic} & 49.78 & 54.83 & 45.13 & 38.88 & 44.22 & 32.29 & 36.76 & 52.81 \\
       KWA~\cite{shi2018key} & - & - & - & - & - & - & 36.92 & 59.09 \\
       ASGN~\cite{qiu2019referring} & 50.46 & 51.20 & 49.27 & 38.41 & 39.79 & 35.97 & 41.36 & 60.31 \\
       RRN~\cite{li2018referring} & 55.33 & 57.26 & 53.95 & 39.75 & 42.15 & 36.11 & 36.45 & 63.63 \\
       MAttNet~\cite{yu2018mattnet} & 56.51 & 62.37 & 51.70 & 46.67 & 52.39 & 40.08 & n/a & - \\
       CMSA~\cite{ye2019cross} & 58.32 & 60.61 & 55.09 & 43.76 & 47.60 & 37.89 & 39.98 & 63.80 \\
       CAC~\cite{chen2019referring} & 58.90 & 61.77 & 53.81 & - & - & - & 44.32 & - \\
       STEP~\cite{chen2019see} & 60.04 & 63.46 & 57.97 & 48.19 & 52.33 & 40.41 & 46.40 & 64.13 \\
       \hline
       Ours & \textbf{61.36} & \textbf{64.53} & \textbf{59.64} & \textbf{49.56} & \textbf{53.44} & \textbf{43.23} & \textbf{49.05} & \textbf{65.53} \\
       \hline
   \end{tabular}
   \caption{Comparison with state-of-the-art methods on four benchmark datasets using \textit{overall IoU} as metric. ``n/a'' denotes MAttNet does not use the same split as other methods.}
   \label{tab:sota}
\end{table*}

\subsection{Text-Guided Feature Exchange}
As previous works~\cite{li2018referring}\cite{ye2019cross} show that multi-level
semantics are essential to referring segmentation, we further introduce a Text-Guided
Feature Exchange (TGFE) module to communicate information among multi-level features
based on the visual and language context.
As illustrated in Figure~\ref{fig:pipeline}, the TGFE module takes $Y_3, Y_4, Y_5$ and
word features $[l_1, l_2, ..., l_T]$ as input. After $n$ rounds of feature exchange,
$Y_3^{(n)}, Y_4^{(n)}, Y_5^{(n)}$ are produced as outputs.

To get $Y_i^{(k)}, i \in \{3, 4, 5\}, k \geq 1$, we first extract a global vector $g_i^{(k-1)} \in \mathbb{R} ^ {C_m}$
of $Y_i^{(k-1)}$ by weighted global pooling:
\begin{equation}
   \label{eq:global_vec}
   g_i^{(k-1)} = \Lambda_i^{(k-1)} Y_i^{(k-1)},
\end{equation}
where the weight matrix $\Lambda_i^{(k-1)} \in \mathbb{R}^{HW}$ is derived from:
\begin{equation}
   \label{eq:pool_weight}
   \Lambda_i^{(k-1)} = (sW_8) (Y_i^{(k-1)}W_9)^T,
\end{equation}
where $W_8 \in \mathbb{R}^{C_l \times C_h}$ and $W_9 \in \mathbb{R}^{C_m \times C_h}$ are transforming matrices.
Then a context vector $c_i^{(k-1)}$ which contains multimodal context of $Y_i^{(k-1)}$ is
calculated by fusing $s$ and $g_i^{(k-1)}$ with a fully connected layer.
We finally select information correlated with level $i$ from features
of other two levels to form the refined features of level $i$ at round $k$:
\begin{equation}
   \label{eq:extract}
   Y_i^{(k)} =
      \begin{cases}
         Y_i^{(k-1)} + \sum\limits_{j \in \{3, 4, 5\} \backslash \{i\}}{\sigma(c_i^{(k-1)}) \odot Y_j^{(k-1)}}, k \geq 1 \\
         \\
         Y_i, k = 0
      \end{cases}
\end{equation}
where $\sigma(\cdot)$ denotes the sigmoid function.
After $n$ rounds of feature exchange, features of each level are mutually refined to
fit the context referred to by the expression.
We further fuse the output features $Y_3^{(n)}$, $Y_4^{(n)}$ and $Y_5^{(n)}$ with
ConvLSTM~\cite{xingjian2015convolutional} for harvesting the final prediction.

\section{Experiments}
\subsection{Experimental Setup}
\label{sec:setup}
\textbf{Datasets. }We conduct extensive experiments on four benchmark datasets for referring image segmentation including UNC~\cite{yu2016modeling}, UNC+~\cite{yu2016modeling}, G-Ref~\cite{mao2016generation} and ReferIt~\cite{kazemzadeh2014referitgame}.

UNC, UNC+ and G-Ref datasets are all collected based on MS-COCO~\cite{lin2014microsoft}. They contain
$19,994$, $19,992$ and $26,711$ images with $142,209$, $141,564$ and $104,560$ referring expressions
for over $50,000$ objects, respectively. UNC+ has no location words and G-Ref contains much longer
sentences (average length of $8.4$ words) than others (less than $4$ words), making them more challenging
than UNC dataset. ReferIt dataset is collected on IAPR TC-12~\cite{escalante2010segmented} and contains
$19,894$ images with $130,525$ expressions for $96,654$ objects (including stuff).

\textbf{Implementation Details. }We adopt DeepLab-101~\cite{chen2017deeplab} pretrained on PASCAL-VOC
dataset~\cite{everingham2010pascal} as the CNN backbone following prior works~\cite{ye2019cross}\cite{li2018referring}
and use the output of Res$3$, Res$4$ and Res$5$ for multi-level feature fusion.
Input images are resized to $320 \times 320$.
Channel dimensions of features are set as $C_v = C_l = C_m = C_h = 1000$ and the cell size of ConvLSTM~\cite{xingjian2015convolutional} is set to $500$.
When comparing with other methods, the hyper-parameter $r$ of bilinear fusion is set to $5$ and the
number of feature exchange rounds $n$ is set to $3$. GloVe word embeddings~\cite{pennington2014glove}
pretrained on Common Crawl 840B tokens are adopted following~\cite{chen2019see}.
Number of graph convolution layers is set to $2$ on G-Ref dataset and $1$ on others.
The network is trained using Adam optimizer~\cite{kingma2014adam} with the initial learning rate of $2.5e^{-4}$ and weight decay of $5e^{-4}$.
Parameters of CNN backbone are fixed during training.
The standard binary cross-entropy loss averaged over all pixels is leveraged for training.
For fair comparison with prior works, DenseCRF~\cite{krahenbuhl2011efficient} is adopted to refine the segmentation masks.

\textbf{Evaluation Metrics. }Following prior works~\cite{hu2016segmentation}\cite{ye2019cross}\cite{chen2019see}, overall Intersection-over-Union (Overall IoU) and Prec@X are adopted as metrics to evaluate our model.
Overall IoU calculates total intersection regions over total union regions of all the test samples.
Prec@X measures the percentage of predictions whose IoU are higher than the threshold $X$ with $X \in \{0.5, 0.6, 0.7, 0.8, 0.9\}$.

\begin{table*}[t]
   \centering
   \begin{tabular}{l|cccc|ccccc|c}
       \hline
        & EP & RAR & TGFE & GloVe & Prec@0.5 & Prec@0.6 & Prec@0.7 & Prec@0.8  & Prec@0.9  & Overall IoU \\
       \hline
       1& & & & &  48.01 & 37.98 & 27.92 & 16.30 & 3.72 & 47.36 \\
      2 & $\surd$ & & & & 49.76 & 40.35 & 30.15 & 17.84 & 4.16 & 49.06 \\
       3 & & $\surd$ & & & 59.32 & 51.16 & 40.59 & 26.50 & 6.66 & 53.40 \\
       4 & $\surd$ & $\surd$ & & & 62.86 & 54.54 & 44.10 & \textbf{28.65} & \textbf{7.24} & 55.38 \\
       5 & $\surd$ & $\surd$ & & $\surd$ & \textbf{62.87} & \textbf{54.91} & \textbf{44.16} & 28.43 & 7.23 & \textbf{56.00} \\
      \hline
      6* & & &  & & 63.12 & 54.56 & 44.20 & 28.75 & 8.51 & 56.38 \\
       7 & & & $\surd$ & & 67.63 & 59.80 & 49.72 & 34.45 & 10.62 & 58.81 \\
       8 & $\surd$ & & $\surd$ & & 68.39 & 60.92 & 50.70 & 35.24 & 11.13 & 59.05 \\
      9 & & $\surd$ & $\surd$ & & 69.37 & 62.28 & 52.66 & 36.89 & 11.27 & 59.62 \\
        10 & $\surd$ & $\surd$ & $\surd$ & & 71.04 & 64.02 & 54.25 & 38.45 & 11.99 & 60.72 \\
        11 & $\surd$ & $\surd$ & $\surd$ & $\surd$ & \textbf{71.27} & \textbf{64.44} & \textbf{55.03} & \textbf{39.28} & \textbf{12.89} & \textbf{61.19} \\
       \hline
   \end{tabular}
   \caption{Ablation studies on UNC val set. *Row $6$ is the multi-level version of row $1$ using only ConvLSTM for fusion.
   EP and RAR indicate entity perception stage and relation-aware reasoning stage in our CMPC module respectively.}
   \label{tab:cmpc}
\end{table*}

\subsection{Comparison with State-of-the-arts}
To demonstrate the superiority of our method, we evaluate it on four referring segmentation benchmarks.
Comparison results are presented in Table~\ref{tab:sota}.
We follow prior works~\cite{ye2019cross}\cite{chen2019see} to only report overall IoU due to the limit of pages.
Full results are included in supplementary materials.
As illustrated in Table~\ref{tab:sota}, our method outperforms all the previous state-of-the-arts on
four benchmarks with large margins.
Comparing with STEP~\cite{chen2019see} which densely fuses $5$ levels of features for $25$ times, our method
exploits fewer levels of features and fusion times while consistently achieving $1.40\%$-$2.82\%$ performance gains
on all the four datasets, demonstrating the effectiveness of our modules.
In particular, our method yields $2.65\%$ IoU boost against STEP on G-Ref val set, indicating that our method could better handle long sentences than those lack the ability of progressive comprehension.
Besides, ReferIt is a challenging dataset and previous methods only have marginal improvements on it. For example,
STEP and CMSA~\cite{ye2019cross} obtain only $0.33\%$ and $0.17\%$ improvements on
ReferIt test set respectively, while our method enlarges the performance gain to $1.40\%$,
which shows that our model can well generalize to multiple datasets with different characteristics.
In addition, our method also outperforms MAttNet~\cite{yu2018mattnet} by a large margin in Overall IoU.
Though MAttNet achieves higher precisions (e.g., $75.16\%$ versus $71.72\%$ in Prec@0.5 on UNC val set) than ours, it relies on Mask R-CNN~\cite{he2017mask} pretrained on noticeably more COCO~\cite{lin2014microsoft} images ($110K$) than ours pretrained on PASCAL-VOC~\cite{everingham2010pascal} images ($10K$). Therefore, it may not be completely fair to directly compare performances of MAttNet with ours.

\subsection{Ablation Studies}
We perform ablation studies on UNC val set and G-Ref val set to testify the effectiveness of each proposed module.

\textbf{Components of CMPC Module. }We first explore the effectiveness of each component of our proposed CMPC module and the experimental results are shown in Table~\ref{tab:cmpc}.
EP and RAR denotes the entity perception stage and relation-aware reasoning stage in CMPC module respectively.
GloVe means using GloVe word embeddings~\cite{pennington2014glove} to initialize the embedding layer,
which is also adopted in~\cite{chen2019see}.
Results of rows $1$ to $5$ are all based on single-level features, i.e. Res$5$.
Our baseline is implemented as simply concatenating the visual feature extracted with DeepLab-$101$ and linguistic feature extracted with an LSTM and making prediction on the fusion of them.
As shown in row $2$ of Table~\ref{tab:cmpc}, including EP brings $1.70\%$ IoU improvement over the baseline, indicating the perception of candidate entities are essential to the feature alignment between visual and linguistic modalities.
In row $3$, RAR alone brings $6.04\%$ IoU improvement over baseline, which demonstrates that leveraging relational words as routers to reason among spatial regions could effectively highlight the referent in the image, thus boosting the performance notably.
Combining EP with RAR, as shown in row $4$, our CMPC module could achieve $55.38\%$ IoU with single level features, outperforming baseline with a large margin of $8.02\%$ IoU.
This indicates that our model could accurately identity the referent by progressively comprehending the expression and image.
Integrated with GloVe word embeddings, the IoU gain further achieves $8.64\%$ with the aid of large-scale corpus.

We further conduct ablation studies based on multi-level features in rows $6$ to $11$ of Table~\ref{tab:cmpc}.
Row $6$ is the multi-level version of row $1$ using ConvLSTM to fuse multi-level features.
The TGFE module in rows $7$ to $11$ is based on single round of feature exchange.
As shown in Table~\ref{tab:cmpc}, our model performs consistently with the single level version, which well proves the effectiveness of our CMPC module.

\begin{figure*}[t]
   \begin{center}
      \includegraphics[width=0.8\linewidth]{figure/qualitative.pdf}
   \end{center}
      \caption{Qualitative results of referring image segmentation. (a) Original image. (b) Results predicted by the
      multi-level baseline model (row 6 in Table~\ref{tab:cmpc}). (c) Results predicted by our full model
      (row 11 in Table~\ref{tab:cmpc}). (d) Ground-truth.}
   \label{fig:qualitative}
\end{figure*}

\begin{figure*}[htbp]
   \begin{center}
      \includegraphics[width=0.7\linewidth]{figure/vis_attn.pdf}
   \end{center}
      \caption{Visualization of affinity maps between images and expressions in our model. (a) Original image. (b)(c) Affinity maps of only entity words and full expressions in the test samples. (d) Ground-truth. (e) Affinity maps of expressions manually modified by us.}
   \label{fig:attn_map}
\end{figure*}

\textbf{TGFE module. }Table~\ref{tab:tgfe} presents the ablation results of TGFE module. $n$ is the number of feature exchange rounds. The experiments are based on multi-level features with CMPC module.
It is shown that only one round of feature exchange in TGFE could improve the IoU from $59.85\%$ to $60.72\%$.
When we increase the rounds of feature exchange in TGFE, the IoU increases as well, which well proves the effectiveness of our TGFE module.
We further evaluate TGFE module on baseline model and the comparing results are shown in row $6$ and row $7$ of Table~\ref{tab:cmpc}.
TGFE with single round of feature exchange improves the IoU from $56.38\%$ to $58.81\%$, indicating that our TGFE module can effectively utilize rich contexts in multi-level features.

\vspace{-2mm}
\begin{table}[!htbp]
   \centering
   \begin{tabular}{|c|c|c|c|}
      \hline
      \multirow{2}*{CMPC only} & \multicolumn{3}{|c|}{+TGFE} \\
      \cline{2-4}
      & $n=1$ & $n=2$ & $n=3$ \\
      \hline
      59.85 & 60.72 & 61.07 & \textbf{61.25} \\
      \hline
   \end{tabular}
   \caption{Overall IoUs of different numbers of feature exchange rounds in TGFE module on UNC val set. $n$ denotes the number of feature exchange rounds.}
   \label{tab:tgfe}
\end{table}
\vspace{-6mm}

\begin{table}[!htbp]
   \centering
   \begin{tabular}{|c|c|c|c|c|}
         \hline
         \multirow{2}*{Dataset}  & \multicolumn{4}{|c|}{CMPC} \\
         \cline{2-5}
         & $n=0$ & $n=1$ & $n=2$ & $n=3$ \\
         \hline
         UNC val & 49.06 & \textbf{55.38} & 51.57 & 50.70 \\
         \hline
         G-Ref val & 36.50 & 38.19 & \textbf{40.12} & 38.96 \\
         \hline
   \end{tabular}
   \caption{Experiments of graph convolution on UNC val set and G-Ref val set in terms of \textit{overall IoU}.
   $n$ denotes the number of graph convolution layers in our CMPC module.
   Experiments are all conducted on single level features.}
   \label{tab:gcn}
\end{table}

\vspace{-3mm}
\textbf{Number of Graph Convolution Layer. }In Table~\ref{tab:gcn}, we explore the number of graph convolution layers in CMPC module based on single-level features. $n$ is the number of graph convolution layers in CMPC.
Results on UNC val set show that more graph convolution layers leads to performance degradation.
However, on G-Ref val set, $2$ layers of graph convolution in CMPC achieves better performance than $1$ layer while $3$ layers decreasing the performance.
As the average length of expressions in G-Ref ($8.4$ words) is much longer than that of UNC ($<4$ words), we suppose that stacking more graph convolution layers in CMPC can appropriately improve the reasoning effect for longer referring expressions.
However, too many graph convolution layers may introduce noises and harm the performance.

\textbf{Qualitative Results. }We presents qualitative comparison between the multi-level baseline model and our full model
in Figure~\ref{fig:qualitative}. From the top-left example we can observe that the baseline model fails to make clear judgement
between the two girls, while our full model is able to distinguish the correct girl having relationship with the phone,
indicating the effectiveness of our CMPC module. Similar result is shown in the top-right example of
Figure~\ref{fig:qualitative}. As illustrated in the bottom row of Figure~\ref{fig:qualitative}, attributes and location
relationship can also be well handled by our full model.

\textbf{Visualization of Affinity Maps. }We visualize the affinity maps between multimodal feature and the first word in the expression in Figure~\ref{fig:attn_map}. As shown in (b) and (c), our model is able to progressively produce more concentrated responses on the referent as the expression becomes more informative from only entity words to the full sentence. Interestingly, when we manually modify the expression to refer to other entities in the image, our model is still able to correctly comprehend the new expression and identify the referent.
For example, in the third row of Figure~\ref{fig:attn_map}(e), when the expression changes from ``Donut at the bottom'' to ``Donut at the left'', high response area shifts from bottom donut to the left donut according to the expression.
It indicates that our model can adapt to new expressions flexibly.

\vspace{-1mm}
\section{Conclusion and Future Work}
\vspace{-1mm}

To address the referring image segmentation problem, we propose a Cross-Modal Progressive Comprehension (CMPC) module which first perceives candidate entities considered by the expression using entity and attribute words, then conduct graph-based reasoning with the aid of relational words to further highlight the referent while suppressing others.
We also propose a Text-Guided Feature Exchange (TGFE) module which exploits textual information to selectively integrate features from multiple levels to refine the mask prediction.
Our model consistently outperforms previous state-of-the-art methods on four benchmarks, demonstrating its effectiveness.
In the future, we plan to analyze the linguistic information more structurally and explore more compact graph formulation.

\vspace{-5mm}
\paragraph{Acknowledgement} This work was partially supported by the National Natural Science Foundation of China (Grant 61572493, Grant 61876177, Grant 61976250, Grant 61702565), Beijing Natural Science Foundation (L182013, 4202034), Fundamental Research Funds for the Central Universities and Zhejiang Lab (No. 2019KD0AB04).

{\small
\bibliographystyle{ieee_fullname}

\section{Introduction}

Please follow the steps outlined below when submitting your manuscript to
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Please number all of your sections and displayed equations.  It is
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Many authors misunderstand the concept of anonymizing for blind
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Saying ``this builds on the work of Lucy Smith [1]'' does not say
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An example of a bad paper just asking to be rejected:
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\begin{center}
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\end{center}

   In this paper we present a performance analysis of our
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\end{quote}

An example of an acceptable paper:

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\begin{center}
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   In this paper we present a performance analysis of the
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\medskip

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\begin{figure}[t]
\begin{center}
\fbox{\rule{0pt}{2in} \rule{0.9\linewidth}{0pt}}

\end{center}
   \caption{Example of caption.  It is set in Roman so that mathematics
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\label{fig:long}
\label{fig:onecol}
\end{figure}

\subsection{Miscellaneous}

\noindent
Compare the following:\\
\begin{tabular}{ll}
 \verb'$conf_a$' &  $conf_a$ \\
 \verb'$\mathit{conf}_a$' & $\mathit{conf}_a$
\end{tabular}\\
See The \TeX book, p165.

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\begin{figure*}
\begin{center}
\fbox{\rule{0pt}{2in} \rule{.9\linewidth}{0pt}}
\end{center}
   \caption{Example of a short caption, which should be centered.}
\label{fig:short}
\end{figure*}

\section{Formatting your paper}

All text must be in a two-column format. The total allowable width of the
text area is $6\frac78$ inches (17.5 cm) wide by $8\frac78$ inches (22.54
cm) high. Columns are to be $3\frac14$ inches (8.25 cm) wide, with a
$\frac{5}{16}$ inch (0.8 cm) space between them. The main title (on the
first page) should begin 1.0 inch (2.54 cm) from the top edge of the
page. The second and following pages should begin 1.0 inch (2.54 cm) from
the top edge. On all pages, the bottom margin should be 1-1/8 inches (2.86
cm) from the bottom edge of the page for $8.5 \times 11$-inch paper; for A4
paper, approximately 1-5/8 inches (4.13 cm) from the bottom edge of the
page.

\subsection{Margins and page numbering}

All printed material, including text, illustrations, and charts, must be kept
within a print area 6-7/8 inches (17.5 cm) wide by 8-7/8 inches (22.54 cm)
high.
Page numbers should be in footer with page numbers, centered and .75
inches from the bottom of the page and make it start at the correct page
number rather than the 4321 in the example.  To do this fine the line (around
line 23)
\begin{verbatim}
\setcounter{page}{4321}
\end{verbatim}
where the number 4321 is your assigned starting page.

Make sure the first page is numbered by commenting out the first page being
empty on line 46
\begin{verbatim}
\end{verbatim}

\subsection{Type-style and fonts}

Wherever Times is specified, Times Roman may also be used. If neither is
available on your word processor, please use the font closest in
appearance to Times to which you have access.

MAIN TITLE. Center the title 1-3/8 inches (3.49 cm) from the top edge of
the first page. The title should be in Times 14-point, boldface type.
Capitalize the first letter of nouns, pronouns, verbs, adjectives, and
adverbs; do not capitalize articles, coordinate conjunctions, or
prepositions (unless the title begins with such a word). Leave two blank
lines after the title.

AUTHOR NAME(s) and AFFILIATION(s) are to be centered beneath the title
and printed in Times 12-point, non-boldface type. This information is to
be followed by two blank lines.

The ABSTRACT and MAIN TEXT are to be in a two-column format.

MAIN TEXT. Type main text in 10-point Times, single-spaced. Do NOT use
double-spacing. All paragraphs should be indented 1 pica (approx. 1/6
inch or 0.422 cm). Make sure your text is fully justified---that is,
flush left and flush right. Please do not place any additional blank
lines between paragraphs.

Figure and table captions should be 9-point Roman type as in
Figures~\ref{fig:onecol} and~\ref{fig:short}.  Short captions should be centred.

\noindent Callouts should be 9-point Helvetica, non-boldface type.
Initially capitalize only the first word of section titles and first-,
second-, and third-order headings.

FIRST-ORDER HEADINGS. (For example, {\large \bf 1. Introduction})
should be Times 12-point boldface, initially capitalized, flush left,
with one blank line before, and one blank line after.

SECOND-ORDER HEADINGS. (For example, { \bf 1.1. Database elements})
should be Times 11-point boldface, initially capitalized, flush left,
with one blank line before, and one after. If you require a third-order
heading (we discourage it), use 10-point Times, boldface, initially
capitalized, flush left, preceded by one blank line, followed by a period
and your text on the same line.

\subsection{Footnotes}

Please use footnotes\footnote {This is what a footnote looks like.  It
often distracts the reader from the main flow of the argument.} sparingly.
Indeed, try to avoid footnotes altogether and include necessary peripheral
observations in
the text (within parentheses, if you prefer, as in this sentence).  If you
wish to use a footnote, place it at the bottom of the column on the page on
which it is referenced. Use Times 8-point type, single-spaced.

\subsection{References}

List and number all bibliographical references in 9-point Times,
single-spaced, at the end of your paper. When referenced in the text,
enclose the citation number in square brackets, for
example~\cite{Authors14}.  Where appropriate, include the name(s) of
editors of referenced books.

\begin{table}
\begin{center}
\begin{tabular}{|l|c|}
\hline
Method & Frobnability \\
\hline\hline
Theirs & Frumpy \\
Yours & Frobbly \\
Ours & Makes one's heart Frob\\
\hline
\end{tabular}
\end{center}
\caption{Results.   Ours is better.}
\end{table}

\subsection{Illustrations, graphs, and photographs}

All graphics should be centered.  Please ensure that any point you wish to
make is resolvable in a printed copy of the paper.  Resize fonts in figures
to match the font in the body text, and choose line widths which render
effectively in print.  Many readers (and reviewers), even of an electronic
copy, will choose to print your paper in order to read it.  You cannot
insist that they do otherwise, and therefore must not assume that they can
zoom in to see tiny details on a graphic.

When placing figures in \LaTeX, it's almost always best to use
\verb+\includegraphics+, and to specify the  figure width as a multiple of
the line width as in the example below
{\small\begin{verbatim}
   \usepackage[dvips]{graphicx} ...
   \includegraphics[width=0.8\linewidth]
                   {myfile.eps}
\end{verbatim}
}

\subsection{Color}

Please refer to the author guidelines on the CVPR 2020 web page for a discussion
of the use of color in your document.

\section{Final copy}

You must include your signed IEEE copyright release form when you submit
your finished paper. We MUST have this form before your paper can be
published in the proceedings.

Please direct any questions to the production editor in charge of these
proceedings at the IEEE Computer Society Press:
\url{https://www.computer.org/about/contact}.

{\small
\bibliographystyle{ieee_fullname}

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The Supreme Court recognised the fundamental right to privacy in 2017, the same year the country witnessed some of the most large scale violations of privacy to have ever occurred.
The Supreme Court recognised the fundamental right to privacy in 2017, the same year the country witnessed some of the most large-scale violations of privacy to have ever occurred.
You can describe people in words, or you can describe them in numbers. As a rule, neither system is perfect, but in general, words tend to create bonds while numbers tend to create distance. Technology, and its poorer cousin e-governance, though seems to tilt towards numbers and in 2017, in India, the tilt got a lot steeper.
A deep vein of technological determinism has always run within Narendra Modi's Digital India. Over the last year, it has made itself more clear than ever. From demonetisation, to the Goods and Services Tax (GST) to 360-degree-Aadhaar projects, there appears to be nothing that cannot be solved by a string of numbers in a centralised database.
The messiness involved with governance has stopped being viewed as a problem brought on by contending and noxious interests. Instead, there is a subtle, if consistent, push towards handing over the process of administration towards a technological system that can supposedly fulfil a set of explicit specifications.
This is why in 2017, nearly a billion Indians were forcefully nudged into re-verifying their SIMs with Aadhaar – a development that was brought on over concerns of terrorists getting their hand on SIM cards – even though the government knew the underlying enrolment system was broken enough for Pakistani spies to be able to get their hands on legitimate Aadhaar numbers.
Or why the GST network was pushed out as a crippled system – in Modi sarkar, some technology is always better than no technology – even as its crucial goal of reducing tax evasion was hamstrung by unwieldy rates, lobbying and political flip-flops.
The "governance-as-an-engineering-problem" isn't a new idea globally or even in India. From the 1970s to the 1990s, Indira Gandhi and Rajiv Gandhi were both infatuated with idea of shaping India as a country and its bureaucracy with computers and home-grown technology. While they had mild success, both their efforts were ultimately overshadowed by a number of developments from 1999 onward.
However, even back then, the threat of computerised governance leading to a surveillance state was voiced loudly.
In August 1985, Rajiv Gandhi wanted his party's MPs to digitise and formalise their work with the help of computers. The Indian Express flashed the news with the headline "Computer to Watch Cong MPs", and went onto report that the computer would now give "periodic warnings" about the lapses of MPs and that it would also store details of their "adverse activities".
The Times of India even went to bat over whether it would violate the privacy of MPs over potential privacy violations, with an editorial that called the move "wrong and dangerous". The implication, as India Today wrote in late 1985, was that Rajiv did not trust his party and that it would make the Congress's MPs "even more docile".
In 2017, the Supreme Court recognised that all Indians have a fundamental right to privacy.
However, in the year that was, the country also witnessed some of the most egregious and large scale violations of privacy to have ever occurred.
The sprawling and viral proliferation of the Aadhaar programme into every walk of life lead to over 100 million Aadhaar numbers being leaked by central and state government agencies in direct violation of the Aadhaar Act. What's worse is that nobody was held responsible for the abysmal information security practices that lead to the numbers being made public.
Private and corporate entities tried their best to match Indian government. As The Wire reported and tracked, data breaches from Reliance Jio, Zomato and McDonalds exposed in some way, shape or form the personal details of (at the very minimum) 30 million Indians. Again, no one really held responsible.
Online surveillance manifested in small ways (In Kerala, school authorities reportedly gained unauthorised access to an Instagram account, leading to the suspension of a boy and girl) and the bizarre – The Wire reported in mid-2017 how Indian police allegedly helped a division of UK's Scotland Yard hack into and monitor the email accounts of Greenpeace activists.
India's formal surveillance infrastructure, which boomed after the 26/11 attacks, crystallised in 2017. As we reported, the centrepiece of the Indian government's surveillance efforts, the Central Monitoring System (CMS), was made fully operational by the first quarter of 2017.
The functioning of the CMS, however, still remains exceedingly opaque. All we have are stray data points – in 2014, a SFLC report, which quoted an RTI reply, revealed that up to 9,000 telephone interception orders are issued by the central government every month..
The story of surveillance in 2017 was also one of state governments realising that they should not be outdone by the Centre. In West Bengal, chief minister Mamata Banerjee amended an outdated set of laws to create a near-Orwellian world of surveillance that would track and monitor dissenters and protestors.
In Andhra Pradesh, a tender floated earlier this year for the state's "Integrated People Information Hub" (IPIH) gave us a glimpse of how local surveillance networks by India's police will evolve across the country.
Hyderabad's IPIH, while billed a smart policing mechanism, is a natural outcome of two developments over the last few years. One, 'Digital India' (in this case, the rapid creation and storage of citizen data) is growing at a pace that is not contained by legal safeguards, a serious information security culture and a proper regard for civil liberties. Two, centralised databases that were slowly being created and populated over the last few years are now starting to talk to each other.
Consider the data sources that IPIH mixes and matches: CCTV footage, fingerprint data, call data records (CDR) using entity matching and other data mining algorithms. It also takes into account other variables such as family demographics, address, date of birth, mobile number, contact number, driving license, voter ID, Aadhaar number and a 'crime number'.
As The Wire reported at the time: "While there are indeed serious issues for the police to take care of, smart and predictive policing can easily turn into total blanket surveillance from all forms".
It's strange how India's technology-driven development agendas are carried out in pairs. In the 1990s, Rajiv Gandhi had Sam Pitroda.
Today, Modi has Nandan Nilekani – an odd partnership considering that in the run-up to the 2014 elections, the Gujarat chief minister slammed the Infosys founder, calling him a "person who has 1000 crore but no Aadhaar".
"They have a person who has 1000 crore but no Aadhaar. They made him candidate. He must answer what has been done with hundreds of crores of rupees meant for Aadhaar card. In the end the Supreme Court had to intervene,'' Modi said of Nilekani and the Aadhaar project in April 2014.
By August 2014, Modi had changed his mind. Perhaps what prompted a re-think were Nilekani's ideas for what Aadhaar, after propping up India's welfare system (which has shown middling success so far), could go onto radically change how Indians interact with their central and state government.
Also read: Should States Have the Power to Enact Their Own Data Protection Laws?
In a world filled with post-facts, the truth is approached through repetition. The message that is transmitted and repeated most often is the message that wins. Today, Indians wake up everyday to SMS-es that threaten the shutdown of basic services (their mobile phone connections, their bank accounts) if they don't start connecting Aadhaar to every part of their lives.
Nilekani's last book, Rebooting India, is an illuminating if startling read on how the UID project can slowly link the rest of India's society together, layer within layer. The book, which was released in 2015, is prescient only if how much of it has gone onto become a part of the Modi government's agenda. The national health policy released in March 2017, which proposed the creation of a national health information network that would be linked to Aadhaar as a means of sharing patient data is pure Nilekani.
What should India's citizens – who are wary of how Aadhaar, the interconnection of massive centralised databases and an utter lack of legal safeguards are starting to snowball – think about doing?
There are, of course, tech hacks that help ward off the dangers of using Silicon Valley services. And WhatsApp's decision to switch on end-to-end encryption last year has probably done more to help safeguard the personal privacy of most Indians than anything else in the last two decades.
However, the personal information of most Indians still remains shockingly leaky – the going-rate in 2017 for the personally identifiable information of up to one lakh people was anywhere between Rs 10,000 to Rs 15,000.
But what has become clear in 2017, is that we need to push our elected representatives into doing more, into caring. Most government officials and members of parliament are exceedingly aware of India's surveillance state. In private interactions, they warn you to switch off your phone or leave it in a room outside their office. Services like Gmail, which come with the stigma of being run by a foreign company, are still preferred to local alternatives offered by the National Information Centre.
"Surveillance doesn't affect only criminals, it affects everyone individually, and collectively. Question it. Currently, if the government eavesdrops on you illegally, you would never know. The government should notify all subjects of surveillance after the surveillance is done, so that citizens can challenge illegal surveillance. Ask for better protection of your rights," said Chinmayi Arun, Executive Director at the Centre for Communication Governance.
The first venue that the fight for our rights will be tested next year is at India's data protection committee. Set up towards the end of this year, with public consultation meetings scheduled in the first week of January, the committee is off to a decent start. However, it will still only scratch the surface of what needs to be done.


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text:
State House Daybook: Feb. 20, 2020
Gavin Jackson
State House Daybook: March 12, 2020
State House Daybook provides you with the information you need to navigate the South Carolina General Assembly. Each edition provides a snapshot of the legislative day with meeting schedules, agendas, news and other resources in one place.
Follow reporter Gavin Jackson's Twitter for the latest legislative coverage.
The Senate gavels in at 11:00 a.m. Watch live here. Calendar. Journal.
The House gavels in at 10:00 a.m. Watch live here. Calendar. Journal.
Combined meeting schedule
9:00 a.m. Senate Finance Committee, Gressette Room 105, Agenda Available, Live Broadcast
9:00 a.m. House E.P.W. Higher Education Subcommittee on S.35 and H.4668, Blatt Room 433, Agenda Available
9:00 a.m. House Business, Commerce, and Administrative Subcommittee of the Regulations and Administrative Procedures Committee, Blatt Room 321, Agenda Available, Live Broadcast
10:00 a.m. House of Representatives, State House, House Chamber, Live Broadcast
11:00 a.m. Conference Committee on S.455, 3rd Floor Conference Room State House, Agenda Available
11:00 a.m. Senate, State House, Senate Chamber, Live Broadcast
12:00 p.m. House Committee Hearing on H.3017 and H.3125, Blatt Room 110, Agenda Available, Live Broadcast
Upon adjournment of the Senate or adjournment of Senate Finance, whichever is later, Subcommittee Meeting on the Legal Review of the Department of Administration's Process regarding Act 95 of 2019 and Legislation proposed by NextEra related to its Bid to Purchase Santee Cooper, Gressette Room 105, Agenda Available, Live Broadcast
At the call of the Chair, House Ways and Means Full Committee, Blatt Room 521, Agenda Available
9:30 a.m. Gov. McMaster will speak at the Downtown Charleston's Waterfront Hotel & the groundbreaking expansion of Joe Riley Waterfront Park, 176 Concord Street, Charleston, S.C.
South Carolina Lede
Episodes of This Week In South Carolina
Millions for rural roads, prisons and teachers included in proposed SC House budget The State
$92 million hotel, retail and office project planned for important downtown corner The State
2nd push to end debate on SC education overhaul bill fails AP
Against agency policy, SC prison officials shackled woman giving birth, doctor says The State
SC Supreme Court says Charleston cruise terminal opponents should be heard The Post and Courier
SC rural roads could get $100 million boost even after gas tax hike The Post and Courier
South Carolina says top 467 income tax debtors owe state nearly $120 million The Post and Courier
S.C. State House
Find your representative and senator
Archive State House video
South Carolina Judicial Department
Gavin Jackson graduated with a visual journalism degree from Kent State University in 2008 and has been in the news industry ever since. He has worked at newspapers in Ohio, Louisiana and most...
Murdaugh, She Wrote
Raucous Caucus

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In raking, just the fingertips are used to rake over the skin in firm, pulling movements. You need to keep them stiffly bent at the joints and held so that only the fingertips touch the skin.
Rake back towards you, using both hands together or pulling one hand after the other.


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