Multi attention module for visual tracking

Chen, Bo‐Yu; Li, Peixia; Sun, Chong; Wang, Dong; Yang, Gang; Lu, Huchuan

doi:10.1016/j.patcog.2018.10.005

Cited by 68 publications

(31 citation statements)

References 5 publications

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“…Deep convolutional neural networks (CNNs) have demonstrated exceptional performance in many vision tasks such as image classification [ 55 , 56 ], semantic segmentation [ 57 , 58 , 59 ], object detection [ 60 , 61 ], and object tracking [ 62 , 63 ]. Deep CNNs have also benefited SOD and delivered a huge performance gain compared to the conventional SOD models.…”

Section: Overview Of Salient Object Detectionmentioning

confidence: 99%

“…The success of training a deep convolutional neural network (CNN) [ 55 ] on large scale object recognition dataset [ 136 ] has had a huge impact on the entire research community. Researchers from diverse fields such as natural language processing [ 3 , 137 ], computer networks [ 138 , 139 , 140 ], stock market analysis [ 141 , 142 ], document analysis and recognition [ 143 , 144 ], and of course computer vision [ 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ] have effectively leveraged deep-learning to devise models that achieve appreciable performance compared to heuristic, and classical machine learning (ML) techniques. Following other related fields in computer vision, existing pre-trained CNNs [ 56 , 145 ] for image classification task on ImageNet dataset have been re-purposed to effectively address various challenges present in the SOD task.…”

Section: Deep Learning-based Salient Object Detectionmentioning

confidence: 99%

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Salient Object Detection Techniques in Computer Vision—A Survey

Gupta

Seal

Prasad

et al. 2020

Entropy

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Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

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Section: Overview Of Salient Object Detectionmentioning

confidence: 99%

Section: Deep Learning-based Salient Object Detectionmentioning

confidence: 99%

Salient Object Detection Techniques in Computer Vision—A Survey

Gupta

Seal

Prasad

et al. 2020

Entropy

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“…-visual attention: attention mechanism introduced in machine translation is recently evolved in closely related vision and video processing tasks, e.g., attention weighted CNN features in video captioning [34], spatial attention, temporal attention and channel-wise attention in visual tracking [35], attentive feedback modules and the attention guidance in image SOD [25,26], which considerably enhance the accuracy by boosting the representative power of CNNs. It is valuable to put efforts into visual attention mechanism for video SOD to achieve more promising accuracy.…”

Section: Promising Future Workmentioning

confidence: 99%

Overview of deep-learning based methods for salient object detection in videos

Wang

Zhang

et al. 2020

Pattern Recognition

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Video salient object detection is a challenging and important problem in computer vision domain. In recent years, deep-learning based methods have contributed to significant improvements in this domain. This paper provides an overview of recent developments in this domain and compares the corresponding methods up to date, including 1) classification of the state-of-the-art methods and their frameworks; 2) summary of the benchmark datasets and commonly used evaluation metrics; 3) experimental comparison of the performances of the state-of-the-art methods; 4) suggestions of some promising future works for unsolved challenges.

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“…There are two groups of deep-learning-based trackers. The first group [36,28,32,4] improves the discriminative ability of deep networks by frequent online update. They utilize the first frame to initialize the model and update it * Corresponding Author: Dr. Dong Wang Figure 1.…”

Section: Introductionmentioning

confidence: 99%

GradNet: Gradient-Guided Network for Visual Object Tracking

Chen

Ouyang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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The fully-convolutional siamese network based on template matching has shown great potentials in visual tracking. During testing, the template is fixed with the initial target feature and the performance totally relies on the general matching ability of the siamese network. However, this manner cannot capture the temporal variations of targets or background clutter. In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations. To be specific, the algorithm can utilize the information from the gradient to update the template in the current frame. In addition, a template generalization training method is proposed to better use gradient information and avoid overfitting. To our knowledge, this work is the first attempt to exploit the information in the gradient for template update in siamese-based trackers. Extensive experiments on recent benchmarks demonstrate that our method achieves better performance than other state-of-the-art trackers. The source codes are available at

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Multi attention module for visual tracking

Cited by 68 publications

References 5 publications

Salient Object Detection Techniques in Computer Vision—A Survey

Salient Object Detection Techniques in Computer Vision—A Survey

Overview of deep-learning based methods for salient object detection in videos

GradNet: Gradient-Guided Network for Visual Object Tracking

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