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Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing
Zhuo Chen, Weisi Lin, Shiqi Wang +2
The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intel…
Multi-task Mid-level Feature Alignment Network for Unsupervised Cross-Dataset Person Re-Identification
Shan Lin, Haoliang Li, Chang-Tsun Li +1
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. Such…
Attention to Head Locations for Crowd Counting
Youmei Zhang, Chunluan Zhou, Faliang Chang +1
Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications. In this paper, we propose a n…
CRRN: Multi-Scale Guided Concurrent Reflection Removal Network
Renjie Wan, Boxin Shi, Ling-Yu Duan +2
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcr…
Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
Jianlou Si, Honggang Zhang, Chun-Guang Li +4
Typical person re-identification (ReID) methods usually describe each pedestrian with a single feature vector and match them in a task-specific metric space. However, the methods b…
Decoupled Spatial Neural Attention for Weakly Supervised Semantic Segmentation
Tianyi Zhang, Guosheng Lin, Jianfei Cai +3
Weakly supervised semantic segmentation receives much research attention since it alleviates the need to obtain a large amount of dense pixel-wise ground-truth annotations for the…