  Region-based convolutional neural networks
(R-CNN)~\cite{fast_rcnn,faster_rcnn,mask_rcnn} have largely dominated object
detection. Operators defined on RoIs (Region of Interests) play an important
role in R-CNNs such as RoIPooling~\cite{fast_rcnn} and
RoIAlign~\cite{mask_rcnn}. They all only utilize information inside RoIs for
RoI prediction, even with their recent deformable
extensions~\cite{deformable_cnn}. Although surrounding context is well-known
for its importance in object detection, it has yet been integrated in R-CNNs in
a flexible and effective way. Inspired by the auto-context
work~\cite{auto_context} and the multi-class object layout
work~\cite{nms_context}, this paper presents a generic context-mining RoI
operator (i.e., \textit{RoICtxMining}) seamlessly integrated in R-CNNs, and the
resulting object detection system is termed \textbf{Auto-Context R-CNN} which
is trained end-to-end. The proposed RoICtxMining operator is a simple yet
effective two-layer extension of the RoIPooling or RoIAlign operator. Centered
at an object-RoI, it creates a $3\times 3$ layout to mine contextual
information adaptively in the $8$ surrounding context regions on-the-fly.
Within each of the $8$ context regions, a context-RoI is mined in term of
discriminative power and its RoIPooling / RoIAlign features are concatenated
with the object-RoI for final prediction. \textit{The proposed Auto-Context
R-CNN is robust to occlusion and small objects, and shows promising
vulnerability for adversarial attacks without being adversarially-trained.} In
experiments, it is evaluated using RoIPooling as the backbone and shows
competitive results on Pascal VOC, Microsoft COCO, and KITTI datasets
(including $6.9\%$ mAP improvements over the R-FCN~\cite{rfcn} method on COCO
\textit{test-dev} dataset and the first place on both KITTI pedestrian and
cyclist detection as of this submission).

Computer Vision and Pattern Recognition