International Journal of Computer Vision

Journal Information
ISSN / EISSN : 0920-5691 / 1573-1405
Published by: Springer Nature (10.1007)
Total articles ≅ 2,542
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Latest articles in this journal

, Zeyong Wei, Xianzhi Li, Yabin Xu, Mingqiang Wei,
International Journal of Computer Vision pp 1-15; https://doi.org/10.1007/s11263-021-01564-7

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, Adrien Bartoli
International Journal of Computer Vision pp 1-27; https://doi.org/10.1007/s11263-021-01571-8

The publisher has not yet granted permission to display this abstract.
International Journal of Computer Vision pp 1-17; https://doi.org/10.1007/s11263-021-01559-4

Abstract:
Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR.
Xiaotian Qiao, Quanlong Zheng, Ying Cao,
International Journal of Computer Vision pp 1-13; https://doi.org/10.1007/s11263-021-01560-x

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Ha-Young Shin,
International Journal of Computer Vision pp 1-26; https://doi.org/10.1007/s11263-021-01561-w

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, Oleksandr Bailo, Jinsun Park, ,
International Journal of Computer Vision pp 1-21; https://doi.org/10.1007/s11263-021-01558-5

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