Recurrent Layer Attention Network
Capturing long-range feature relations has been a central issue on convolutional neural networks(CNNs). To tackle this, attempts to integrate end-to-end trainable attention module on CNNs are widespread. Main goal of these works is to adjust feature maps considering spatial-channel correlation inside a convolution layer. In this paper, we focus on modeling relationships among layers and propose a novel structure, *Recurrent Layer Attention network,* which stores the hierarchy of features into recurrent neural networks(RNNs) that concurrently propagating with CNN and adaptively scales feature volumes of all layers. We further introduce several structural derivatives for demonstrating the compatibility on recent attention modules and the expandability of proposed network. For semantic understanding on learned features, we also visualize intermediate layers and plot the curve of layer scaling coefficients(i.e., layer attention). Recurrent Layer Attention network achieves significant performance enhancement requiring a slight increase on parameters in an image classification task with CIFAR and ImageNet-1K 2012 dataset and an object detection task with Microsoft COCO 2014 dataset.
This paper (Recurrent Layer Attention Network) describes a method for aggregating information over layers, using a combination of RNNs *controlling* the scaling of feature volumes from CNN activations. The experiments and ablation studies in this paper are detailed, and show the workings of the proposed mechanism. Given comments on the ICLR submission page, codifying the relationship between DIANet and this work inside the paper itself would be beneficial. While the baselines chosen are sensible, it would be of great interest to see this method applied to a nearer-to-SOTA architecture (likely a ResNet variant) as well, given the plethora of open source implementations to choose from for the benchmarks, and the direct applicability of the proposed method. The same holds for the instance segmentation experiments, although I do understand the cost of running experiments in that domain - the proposed method should be easily applied to *any* segmentation architecture utilizing a ResNet trunk. *Recurrent Layer Attention network achieves significant performance enhancement* seems a bit of an over-reach given the existing experiments, the more modest claims in the introduction *RLA network achieves similar or superior results* make more sense. The primary issue with this paper is flawed grammar and generally difficult-to-read *flow* of the writing itself. Rewriting with a careful eye to these problems has potential to raise my score - as it stands I cannot recommend the paper for acceptance, even though the core work, experiments, and architecture all seem promising to me. More work on the writing portion of this paper would greatly improve things, along with experiments pushing the edge of performance based on recent open source codebases.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Recurrent Layer Attention Network
Capturing long-range feature relations has been a central issue on convolutional neural networks(CNNs). To tackle this, attempts to integrate end-to-end trainable attention module on CNNs are widespread. Main goal of these works is to adjust feature maps considering spatial-channel correlation inside a convolution layer. In this paper, we focus on modeling relationships among layers and propose a novel structure, *Recurrent Layer Attention network,* which stores the hierarchy of features into recurrent neural networks(RNNs) that concurrently propagating with CNN and adaptively scales feature volumes of all layers. We further introduce several structural derivatives for demonstrating the compatibility on recent attention modules and the expandability of proposed network. For semantic understanding on learned features, we also visualize intermediate layers and plot the curve of layer scaling coefficients(i.e., layer attention). Recurrent Layer Attention network achieves significant performance enhancement requiring a slight increase on parameters in an image classification task with CIFAR and ImageNet-1K 2012 dataset and an object detection task with Microsoft COCO 2014 dataset.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper (Recurrent Layer Attention Network) describes a method for aggregating information over layers, using a combination of RNNs *controlling* the scaling of feature volumes from CNN activations. The experiments and ablation studies in this paper are detailed, and show the workings of the proposed mechanism. Given comments on the ICLR submission page, codifying the relationship between DIANet and this work inside the paper itself would be beneficial. While the baselines chosen are sensible, it would be of great interest to see this method applied to a nearer-to-SOTA architecture (likely a ResNet variant) as well, given the plethora of open source implementations to choose from for the benchmarks, and the direct applicability of the proposed method. The same holds for the instance segmentation experiments, although I do understand the cost of running experiments in that domain - the proposed method should be easily applied to *any* segmentation architecture utilizing a ResNet trunk. *Recurrent Layer Attention network achieves significant performance enhancement* seems a bit of an over-reach given the existing experiments, the more modest claims in the introduction *RLA network achieves similar or superior results* make more sense. The primary issue with this paper is flawed grammar and generally difficult-to-read *flow* of the writing itself. Rewriting with a careful eye to these problems has potential to raise my score - as it stands I cannot recommend the paper for acceptance, even though the core work, experiments, and architecture all seem promising to me. More work on the writing portion of this paper would greatly improve things, along with experiments pushing the edge of performance based on recent open source codebases.
assistant
This paper (Recurrent Layer Attention Network) describes a method for aggregating information over layers, using a combination of RNNs *controlling* the scaling of feature volumes from CNN activations. The experiments and ablation studies in this paper are detailed, and show the workings of the proposed mechanism. Given comments on the ICLR submission page, codifying the relationship between DIANet and this work inside the paper itself would be beneficial. While the baselines chosen are sensible, it would be of great interest to see this method applied to a nearer-to-SOTA architecture (likely a ResNet variant) as well, given the plethora of open source implementations to choose from for the benchmarks, and the direct applicability of the proposed method. The same holds for the instance segmentation experiments, although I do understand the cost of running experiments in that domain - the proposed method should be easily applied to *any* segmentation architecture utilizing a ResNet trunk. *Recurrent Layer Attention network achieves significant performance enhancement* seems a bit of an over-reach given the existing experiments, the more modest claims in the introduction *RLA network achieves similar or superior results* make more sense. The primary issue with this paper is flawed grammar and generally difficult-to-read *flow* of the writing itself. Rewriting with a careful eye to these problems has potential to raise my score - as it stands I cannot recommend the paper for acceptance, even though the core work, experiments, and architecture all seem promising to me. More work on the writing portion of this paper would greatly improve things, along with experiments pushing the edge of performance based on recent open source codebases.