Convolutional neural networks have recently been successfully applied to the problems of restoring clean images from their degraded versions. Most studies have designed and trained a dedicated network for each of many image restoration tasks, such as motion blur removal, rain-streak removal, haze removal, etc. In this paper, we show that a single network having a single input and multiple output branches can solve multiple image restoration tasks. This is made possible by improving the attention mechanism and an internal structure of the basic blocks used in the dual residual networks, which was recently proposed and shown to work well for a number of image restoration tasks by Experimental results show that the proposed approach achieves a new state-of-the-art performance on haze removal (both in PSNR SSIM) and JPEG artifact removal (in SSIM). To the authors' knowledge, this is the first report of successful multi-task learning on diverse image restoration tasks.
Attention mechanisms have been developed and employed to solve various computer vision problems @cite_82 @cite_63 @cite_13 @cite_66 @cite_11 . Hu al proposed a squeeze-and-excitation (SE) block, which produces and applies channel-wise weights on an input tensor @cite_66 . This block has been successfully applied to various tasks such as classification @cite_66 , super resolution @cite_43 and single-view depth estimation @cite_52 . A number of later studies @cite_39 @cite_77 @cite_64 @cite_56 @cite_61 aim at improving the SE-block. Woo al propose to use channel-wise and spatial attention weights @cite_64 . Hu al study how to efficiently combine a SE-block and a ResNet module @cite_77 . Gao al propose to use correlation of activations of each pair of channels to generate attention Sec_order_pool . Hu al improve SE-block by replacing global average pooling with a pooling operation with trainable parameters @cite_56 .
@cite_61 @cite_64 @cite_82 @cite_52 @cite_39 @cite_56 @cite_43 @cite_77 @cite_63 @cite_13 @cite_66 @cite_11
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We aim to model the top-down attention of a convolutional neural network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. We show a theoretic connection between the proposed contrastive attention formulation and the Class Activation Map computation. Efficient implementation of Excitation Backprop for common neural network layers is also presented. In experiments, we visualize the evidence of a model’s classification decision by computing the proposed top-down attention maps. For quantitative evaluation, we report the accuracy of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images. Finally, we demonstrate applications of our method in model interpretation and data annotation assistance for facial expression analysis and medical imaging tasks. We aim to model the top-down attention of a convolutional neural network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. We show a theoretic connection between the proposed contrastive attention formulation and the Class Activation Map computation. Efficient implementation of Excitation Backprop for common neural network layers is also presented. In experiments, we visualize the evidence of a model’s classification decision by computing the proposed top-down attention maps. For quantitative evaluation, we report the accuracy of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images. Finally, we demonstrate applications of our method in model interpretation and data annotation assistance for facial expression analysis and medical imaging tasks. The field of object detection has made great progress in recent years. Most of these improvements are derived from using a more sophisticated convolutional neural network. However, in the case of humans, the attention mechanism, global structure information, and local details of objects all play an important role for detecting an object. In this paper, we propose a novel fully convolutional network, named as Attention CoupleNet, to incorporate the attention-related information and global and local information of objects to improve the detection performance. Specifically, we first design a cascade attention structure to perceive the global scene of the image and generate class-agnostic attention maps. Then the attention maps are encoded into the network to acquire object-aware features. Next, we propose a unique fully convolutional coupling structure to couple global structure and local parts of the object to further formulate a discriminative feature representation. To fully explore the global and local properties, we also design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local information. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging data sets, i.e., a mAP of 85.7 on VOC07, 84.3 on VOC12, and 35.4 on COCO. Codes are publicly available at https: github.com tshizys CoupleNet . Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local receptive fields. In order to boost the representational power of a network, several recent approaches have shown the benefit of enhancing spatial encoding. In this work, we focus on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We demonstrate that by stacking these blocks together, we can construct SENet architectures that generalise extremely well across challenging datasets. Crucially, we find that SE blocks produce significant performance improvements for existing state-of-the-art deep architectures at minimal additional computational cost. SENets formed the foundation of our ILSVRC 2017 classification submission which won first place and significantly reduced the top-5 error to 2.251 , achieving a 25 relative improvement over the winning entry of 2016. Code and models are available at https: github.com hujie-frank SENet.