The Experts below are selected from a list of 17787 Experts worldwide ranked by ideXlab platform
Anton Van Den Hengel - One of the best experts on this subject based on the ideXlab platform.
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Cross-Convolutional-Layer Pooling for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) trained on a large image dataset can be used as a universal image descriptor and that doing so leads to impressive performance for a variety of image recognition tasks. Most of these studies adopt activations from a single DCNN Layer, usually a fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is used for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first directly uses Convolutional Layers from a DCNN. The second applies the pre-trained CNN on densely sampled image regions and treats the fully-connected activations of each image region as a Convolutional Layer's feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find that our first scheme tends to perform better on applications which demand strong discrimination on lower-level visual patterns while the latter excels in cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing approaches for extracting image representations from a DCNN. In addition, we apply cross-Layer pooling to the problem of image retrieval and propose schemes to reduce the computational cost. Experimental results suggest that the proposed method achieves promising results for the image retrieval task.
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Cross-Convolutional-Layer Pooling for Image Recognition
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN Layer, usually the fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first one directly uses Convolutional Layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as Convolutional feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.
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Cross-Convolutional-Layer Pooling for Generic Visual Recognition.
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN Layer, usually the fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first one directly uses Convolutional Layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as Convolutional feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.
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CVPR - The treasure beneath Convolutional Layers: Cross-Convolutional-Layer pooling for image classification
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive performance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected Layer of a DCNN as the image or region representation and it is believed that Convolutional Layer activations are less discriminative. This paper, however, advocates that if used appropriately, Convolutional Layer activations constitute a powerful image representation. This is achieved by adopting a new technique proposed in this paper called cross-Convolutional-Layer pooling. More specifically, it extracts subarrays of feature maps of one Convolutional Layer as local features, and pools the extracted features with the guidance of the feature maps of the successive Convolutional Layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected Layer activations to describe local regions. Also, the proposed method is easier to implement since it is codebook free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected Layer based image representations.
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The Treasure beneath Convolutional Layers: Cross-Convolutional-Layer Pooling for Image Classification
arXiv: Computer Vision and Pattern Recognition, 2014Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image description which leads to astounding performance in many visual classification tasks. Most of these studies, if not all, adopt activations of the fully-connected Layer of a DCNN as the image or region representation and it is believed that Convolutional Layer activations are less discriminative. This paper, however, advocates that if used appropriately Convolutional Layer activations can be turned into a powerful image representation which enjoys many advantages over fully-connected Layer activations. This is achieved by adopting a new technique proposed in this paper called cross-Convolutional-Layer pooling. More specifically, it extracts subarrays of feature maps of one Convolutional Layer as local features and pools the extracted features with the guidance of feature maps of the successive Convolutional Layer. Compared with exising methods that apply DCNNs in the local feature setting, the proposed method is significantly faster since it requires much fewer times of DCNN forward computation. Moreover, it avoids the domain mismatch issue which is usually encountered when applying fully connected Layer activations to describe local regions. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected Layer based image representations while incurring much lower computational cost.
Sa-kwang Song - One of the best experts on this subject based on the ideXlab platform.
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Kick: Shift-N-Overlap Cascades of Transposed Convolutional Layer for Better Autoencoding Reconstruction on Remote Sensing Imagery
IEEE Access, 2020Co-Authors: Seungkyun Hong, Sa-kwang SongAbstract:A Convolutional autoencoder is an essential deep neural model architecture for understanding and predicting large-scale and widespread multi-dimensional information such as remote sensing imagery. To training a Convolutional autoencoder, an automatic image reconstruction from input data and evaluation is repeatedly performed to achieve optimal reconstruction performance. Checkerboard artifacts, which are frequently produced on output images and lead to degraded image quality, are a significant issue during image reconstruction using a Convolutional autoencoder. To remedy this coarse visual saliency issue during model training, we propose the ‘Kick’ deConvolutional Layer - a cascaded transposed Convolutional Layer with pixel shifting and overlapping for checkerboard pattern smoothing. By using pixel-shifted identity Convolutional Layers, we improved image reconstruction performance using fewer trainable decoder parameters than previously suggested models without losing reconstruction capability. Moreover, our proposed Layer can be used with any type of Convolutional autoencoder, including typical Convolutional autoencoders and adversarial autoencoders. To evaluate an image reconstruction performance of our suggested deConvolutional Layer, we used a dataset containing 12 years of geostationary satellite observation data of East Asia.
Chunhua Shen - One of the best experts on this subject based on the ideXlab platform.
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Cross-Convolutional-Layer Pooling for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) trained on a large image dataset can be used as a universal image descriptor and that doing so leads to impressive performance for a variety of image recognition tasks. Most of these studies adopt activations from a single DCNN Layer, usually a fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is used for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first directly uses Convolutional Layers from a DCNN. The second applies the pre-trained CNN on densely sampled image regions and treats the fully-connected activations of each image region as a Convolutional Layer's feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find that our first scheme tends to perform better on applications which demand strong discrimination on lower-level visual patterns while the latter excels in cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing approaches for extracting image representations from a DCNN. In addition, we apply cross-Layer pooling to the problem of image retrieval and propose schemes to reduce the computational cost. Experimental results suggest that the proposed method achieves promising results for the image retrieval task.
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Cross-Convolutional-Layer Pooling for Image Recognition
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN Layer, usually the fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first one directly uses Convolutional Layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as Convolutional feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.
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Cross-Convolutional-Layer Pooling for Generic Visual Recognition.
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN Layer, usually the fully-connected Layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive Convolutional Layers: one Layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of Convolutional Layers, we further develop two schemes to realize this idea. The first one directly uses Convolutional Layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as Convolutional feature activations. We then train another Convolutional Layer on top of that as the pooling-guidance Convolutional Layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.
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the treasure beneath Convolutional Layers cross Convolutional Layer pooling for image classification
Computer Vision and Pattern Recognition, 2015Co-Authors: Lingqiao Liu, Chunhua Shen, Anton Van Den HengelAbstract:A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive performance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected Layer of a DCNN as the image or region representation and it is believed that Convolutional Layer activations are less discriminative. This paper, however, advocates that if used appropriately, Convolutional Layer activations constitute a powerful image representation. This is achieved by adopting a new technique proposed in this paper called cross-Convolutional-Layer pooling. More specifically, it extracts subarrays of feature maps of one Convolutional Layer as local features, and pools the extracted features with the guidance of the feature maps of the successive Convolutional Layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected Layer activations to describe local regions. Also, the proposed method is easier to implement since it is codebook free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected Layer based image representations.
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CVPR - The treasure beneath Convolutional Layers: Cross-Convolutional-Layer pooling for image classification
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015Co-Authors: Chunhua Shen, Anton Van Den HengelAbstract:A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image descriptor, and that doing so leads to impressive performance at a range of image classification tasks. Most of these studies, if not all, adopt activations of the fully-connected Layer of a DCNN as the image or region representation and it is believed that Convolutional Layer activations are less discriminative. This paper, however, advocates that if used appropriately, Convolutional Layer activations constitute a powerful image representation. This is achieved by adopting a new technique proposed in this paper called cross-Convolutional-Layer pooling. More specifically, it extracts subarrays of feature maps of one Convolutional Layer as local features, and pools the extracted features with the guidance of the feature maps of the successive Convolutional Layer. Compared with existing methods that apply DCNNs in the similar local feature setting, the proposed method avoids the input image style mismatching issue which is usually encountered when applying fully connected Layer activations to describe local regions. Also, the proposed method is easier to implement since it is codebook free and does not have any tuning parameters. By applying our method to four popular visual classification tasks, it is demonstrated that the proposed method can achieve comparable or in some cases significantly better performance than existing fully-connected Layer based image representations.
Shuiwang Ji - One of the best experts on this subject based on the ideXlab platform.
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Pixel Transposed Convolutional Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Hao Yuan, Zhengyang Wang, Shuiwang JiAbstract:Transposed Convolutional Layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of transposed Convolutional operations is that they result in the so-called checkerboard problem. This is caused by the fact that no direct relationship exists among adjacent pixels on the output feature map. To address this problem, we propose the pixel transposed Convolutional Layer (PixelTCL) to establish direct relationships among adjacent pixels on the up-sampled feature map. Our method is based on a fresh interpretation of the regular transposed Convolutional operation. The resulting PixelTCL can be used to replace any transposed Convolutional Layer in a plug-and-play manner without compromising the fully trainable capabilities of original models. The proposed PixelTCL may result in slight decrease in efficiency, but this can be overcome by an implementation trick. Experimental results on semantic segmentation demonstrate that PixelTCL can consider spatial features such as edges and shapes and yields more accurate segmentation outputs than transposed Convolutional Layers. When used in image generation tasks, our PixelTCL can largely overcome the checkerboard problem suffered by regular transposed Convolutional operations.
Xiaoyu Wang - One of the best experts on this subject based on the ideXlab platform.
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Progressive Learning of Low-Precision Networks for Image Classification
IEEE Transactions on Multimedia, 2020Co-Authors: Zhengguang Zhou, Wengang Zhou, Huang Xuan, Xiaoyu WangAbstract:Recent years have witnessed a great advance of deep learning in a variety of vision tasks. Many state-of-the-art deep neural networks suffer from large size and high complexity, which makes them difficult to deploy in resource-limited platforms such as mobile devices. To this end, low-precision neural networks are widely studied that quantize weights or activations into the low-bit format. Although efficient, low-precision networks are usually difficult to train and encounter severe accuracy degradation. In this paper, we propose a new training strategy based on progressive learning for image classification. First, we equip each low-precision Convolutional Layer with an ancillary full-precision Convolutional Layer based on a low-precision network structure. Second, a decay method is introduced to reduce the output of the added full-precision convolution gradually, which keeps the resulting topology structure the same as the original low-precision convolution. Extensive experiments on SVHN, CIFAR and ILSVRC-2012 datasets reveal that the proposed method can bring faster convergence and higher accuracy for low-precision neural networks.
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Progressive Learning of Low-Precision Networks.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Zhengguang Zhou, Wengang Zhou, Huang Xuan, Xiaoyu WangAbstract:Recent years have witnessed the great advance of deep learning in a variety of vision tasks. Many state-of-the-art deep neural networks suffer from large size and high complexity, which makes it difficult to deploy in resource-limited platforms such as mobile devices. To this end, low-precision neural networks are widely studied which quantize weights or activations into the low-bit format. Though being efficient, low-precision networks are usually hard to train and encounter severe accuracy degradation. In this paper, we propose a new training strategy through expanding low-precision networks during training and removing the expanded parts for network inference. First, we equip each low-precision Convolutional Layer with an ancillary full-precision Convolutional Layer based on a low-precision network structure, which could guide the network to good local minima. Second, a decay method is introduced to reduce the output of the added full-precision convolution gradually, which keeps the resulted topology structure the same to the original low-precision one. Experiments on SVHN, CIFAR and ILSVRC-2012 datasets prove that the proposed method can bring faster convergence and higher accuracy for low-precision neural networks.