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20152022
most citedKnowledge Distillation by On-the-Fly Native Ensemble

366 citations · 1.6k across the 47 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.CV2017★ 11 cited

Class Rectification Hard Mining for Imbalanced Deep Learning

Qi Dong, Shaogang Gong, Xiatian Zhu

Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and…

cs.CV2017★ 36 cited

Attribute Recognition by Joint Recurrent Learning of Context and Correlation

Jingya Wang, Xiatian Zhu, Shaogang Gong +1

Recognising semantic pedestrian attributes in surveillance images is a challenging task for computer vision, particularly when the imaging quality is poor with complex background c…

cs.CV2017★ 10 cited

Deep Reinforcement Learning Attention Selection for Person Re-Identification

Xu Lan, Hanxiao Wang, Shaogang Gong +1

Existing person re-identification (re-id) methods assume the provision of accurately cropped person bounding boxes with minimum background noise, mostly by manually cropping. This…

cs.CV2017

Discovering Visual Concept Structure with Sparse and Incomplete Tags

Jingya Wang, Xiatian Zhu, Shaogang Gong

Discovering automatically the semantic structure of tagged visual data (e.g. web videos and images) is important for visual data analysis and interpretation, enabling the machine i…

cs.CV2017★ 42 cited

Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Wei Li, Xiatian Zhu, Shaogang Gong

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary…

cs.CV2017★ 346 cited

Person Re-Identification by Camera Correlation Aware Feature Augmentation

Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng +1

The challenge of person re-identification (re-id) is to match individual images of the same person captured by different non-overlapping camera views against significant and unknow…