Weijie Kong- MS
- Peking University
Weijie Kong
- MS
- Peking University
About
8
Publications
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Introduction
Weijie Kong currently works at the School of Electronic and Computer Engineering, Peking University. Weijie does research in Computer Vision, Pedestrian Detection and Video Analysis. His current project is 'Video Analysis'.
Current institution
Publications
Publications (8)
Video anomaly detection under weak labels is formulated as a typical multiple-instance learning problem in previous works. In this paper, we provide a new perspective, i.e., a supervised learning task under noisy labels. In such a viewpoint, as long as cleaning away label noise, we can directly apply fully supervised action classifiers to weakly su...
Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enou...
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data. Under the supervision of category labels, weakly supervised detectors are usually built upon classifiers. However, there is an inherent contradiction betw...
Weakly supervised temporal action detection is a Herculean task in understanding untrimmed videos, since no supervisory signal except the video-level category label is available on training data. Under the supervision of category labels, weakly supervised detectors are usually built upon classifiers. However, there is an inherent contradiction betw...
Recently, Faster R-CNN achieves great performance in deep learning based object detection. However, a major bottleneck of Faster RCNN lies on the sharp performance deterioration when detecting objects that are small in size or have a similar appearance with their backgrounds. To address this problem, we present a new pedestrian detection approach b...
