The Experts below are selected from a list of 135 Experts worldwide ranked by ideXlab platform

Bailing Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Multi-scale Attention Networks for action recognition
    Signal Processing-image Communication, 2018
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Abstract Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Amongst them, the Hierarchical Multi-scale RNN (HM-RNN), a recently proposed multi-scale hierarchical RNN, can automatically learn the hierarchical temporal structure from data. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, the attention mechanism can be applied to extract the relevant information from the inputs thus facilitating the modeling of the input–output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by incorporating the attention mechanism into the HM-RNN and applying it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To reduce the negative effect of the temperature sensitivity of the Gumbel-softmax, an adaptive temperature training method is applied to improve the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what has been learnt by the network, it can be observed that both the attention regions of the images and the hierarchical temporal structure can be captured by a HM-AN.

  • Hierarchical Multi-scale Attention Networks for Action Recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.

Jeremy S. Smith - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Multi-scale Attention Networks for action recognition
    Signal Processing-image Communication, 2018
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Abstract Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Amongst them, the Hierarchical Multi-scale RNN (HM-RNN), a recently proposed multi-scale hierarchical RNN, can automatically learn the hierarchical temporal structure from data. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, the attention mechanism can be applied to extract the relevant information from the inputs thus facilitating the modeling of the input–output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by incorporating the attention mechanism into the HM-RNN and applying it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To reduce the negative effect of the temperature sensitivity of the Gumbel-softmax, an adaptive temperature training method is applied to improve the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what has been learnt by the network, it can be observed that both the attention regions of the images and the hierarchical temporal structure can be captured by a HM-AN.

  • Hierarchical Multi-scale Attention Networks for Action Recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.

Christian Igel - One of the best experts on this subject based on the ideXlab platform.

  • detection of traffic signs in real world images the german traffic sign detection benchmark
    International Joint Conference on Neural Network, 2013
    Co-Authors: Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, Christian Igel
    Abstract:

    Real-time detection of traffic signs, the Task of pinpointing a traffic sign's location in natural images, is a challenging Computer Vision Task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.

  • IJCNN - Detection of traffic signs in real-world images: The German traffic sign detection benchmark
    The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
    Co-Authors: Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, Christian Igel
    Abstract:

    Real-time detection of traffic signs, the Task of pinpointing a traffic sign's location in natural images, is a challenging Computer Vision Task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.

Wenjin Lu - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical Multi-scale Attention Networks for action recognition
    Signal Processing-image Communication, 2018
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Abstract Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Amongst them, the Hierarchical Multi-scale RNN (HM-RNN), a recently proposed multi-scale hierarchical RNN, can automatically learn the hierarchical temporal structure from data. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, the attention mechanism can be applied to extract the relevant information from the inputs thus facilitating the modeling of the input–output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by incorporating the attention mechanism into the HM-RNN and applying it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To reduce the negative effect of the temperature sensitivity of the Gumbel-softmax, an adaptive temperature training method is applied to improve the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what has been learnt by the network, it can be observed that both the attention regions of the images and the hierarchical temporal structure can be captured by a HM-AN.

  • Hierarchical Multi-scale Attention Networks for Action Recognition
    arXiv: Computer Vision and Pattern Recognition, 2017
    Co-Authors: Jeremy S. Smith, Wenjin Lu, Bailing Zhang
    Abstract:

    Recurrent Neural Networks (RNNs) have been widely used in natural language processing and Computer Vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the Computer Vision Task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the Vision Task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.

Sebastian Houben - One of the best experts on this subject based on the ideXlab platform.

  • detection of traffic signs in real world images the german traffic sign detection benchmark
    International Joint Conference on Neural Network, 2013
    Co-Authors: Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, Christian Igel
    Abstract:

    Real-time detection of traffic signs, the Task of pinpointing a traffic sign's location in natural images, is a challenging Computer Vision Task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.

  • IJCNN - Detection of traffic signs in real-world images: The German traffic sign detection benchmark
    The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
    Co-Authors: Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, Christian Igel
    Abstract:

    Real-time detection of traffic signs, the Task of pinpointing a traffic sign's location in natural images, is a challenging Computer Vision Task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.