Existing works often focus on reducing the architecture redundancy for accelerating image classification but ignore the spatial redundancy of the input image. This paper proposes an efficient image classification pipeline to solve this problem. We first pinpoint task-aware regions over the input image by a lightweight patch proposal network called AnchorNet. We then feed these localized semantic patches with much smaller spatial redundancy into a general classification network. Unlike the popular design of deep CNN, we aim to carefully design the Receptive Field of AnchorNet without intermediate convolutional paddings. This ensures the exact mapping from a high-level spatial location to the specific input image patch. The contribution of ea...
International audienceConvolutional neural networks (CNNs) have received increasing attention over t...
Neural network representations proved to be relevant for many computer vision tasks such as image cl...
Semantic segmentation solves the task of labelling every pixel inan image with its class label, and ...
Despite recent progress on the segmentation of high-resolution images, there exist an unsolved probl...
In recent years, convolutional networks have dramatically (re)emerged as the dominant paradigm for s...
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual r...
Semantic segmentation is pixel-wise classification which retains critical spatial information. The “...
deep learning, context priming Abstract: Classifying single image patches is important in many diffe...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
Neural Networks require large amounts of memory and compute to process high resolution images, even ...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
Semantic segmentation aims to parse the scene structure of images by annotating the labels to each p...
International audienceWe address the pixelwise classification of high-resolution aerial imagery. Whi...
Deep convolutional neural networks (CNNs) are the back-bone of state-of-art semantic image segmentat...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
International audienceConvolutional neural networks (CNNs) have received increasing attention over t...
Neural network representations proved to be relevant for many computer vision tasks such as image cl...
Semantic segmentation solves the task of labelling every pixel inan image with its class label, and ...
Despite recent progress on the segmentation of high-resolution images, there exist an unsolved probl...
In recent years, convolutional networks have dramatically (re)emerged as the dominant paradigm for s...
Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual r...
Semantic segmentation is pixel-wise classification which retains critical spatial information. The “...
deep learning, context priming Abstract: Classifying single image patches is important in many diffe...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
Neural Networks require large amounts of memory and compute to process high resolution images, even ...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
Semantic segmentation aims to parse the scene structure of images by annotating the labels to each p...
International audienceWe address the pixelwise classification of high-resolution aerial imagery. Whi...
Deep convolutional neural networks (CNNs) are the back-bone of state-of-art semantic image segmentat...
This paper describes a deep learning approach to semantic segmentation of very high resolution (aeri...
International audienceConvolutional neural networks (CNNs) have received increasing attention over t...
Neural network representations proved to be relevant for many computer vision tasks such as image cl...
Semantic segmentation solves the task of labelling every pixel inan image with its class label, and ...
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