1 code implementation • ECCV 2020 • Ke Cheng, Yifan Zhang, Congqi Cao, Lei Shi, Jian Cheng, Hanqing Lu
Nevertheless, how to efficiently model the spatial-temporal skeleton graph without introducing extra computation burden is a challenging problem for industrial deployment.
no code implementations • 8 Jul 2023 • Congqi Cao, Ze Sun, Qinyi Lv, Lingtong Min, Yanning Zhang
Egocentric action anticipation is a challenging task that aims to make advanced predictions of future actions from current and historical observations in the first-person view.
no code implementations • CVPR 2023 • Congqi Cao, Yue Lu, Peng Wang, Yanning Zhang
At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly.
no code implementations • 15 Mar 2023 • Congqi Cao, Yizhe WANG, Yue Lu, Xin Zhang, Yanning Zhang
Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling.
no code implementations • 16 Dec 2022 • Congqi Cao, Xin Zhang, Shizhou Zhang, Peng Wang, Yanning Zhang
To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data.
1 code implementation • 7 Sep 2022 • Congqi Cao, Yue Lu, Yanning Zhang
For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame.
Ranked #1 on Anomaly Detection on Corridor
no code implementations • 14 Feb 2022 • Congqi Cao, Xin Zhang, Shizhou Zhang, Peng Wang, Yanning Zhang
For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information.
no code implementations • 15 Nov 2021 • Yue Lu, Congqi Cao, Yanning Zhang
In this paper, we propose a novel distance-based VAD method to take advantage of all the available normal data efficiently and flexibly.
no code implementations • 29 Jun 2021 • Qinyi Lv, Lingtong Min, Congqi Cao, Shigang Zhou, Deyun Zhou, Chengkai Zhu, Yun Li, Zhongbo Zhu, Xiaojun Li, Lixin Ran
In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests.
no code implementations • 20 Mar 2021 • Congqi Cao, Yue Lu, Yifan Zhang, Dongmei Jiang, Yanning Zhang
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
no code implementations • 13 Oct 2020 • Congqi Cao, Yajuan Li, Qinyi Lv, Peng Wang, Yanning Zhang
Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application.
no code implementations • 29 Feb 2020 • Congqi Cao, Yanning Zhang
First, we introduce a semantic alignment loss to align the relation statistics of the features from samples that belong to the same category.
no code implementations • ICCV 2017 • Congqi Cao, Yifan Zhang, Yi Wu, Hanqing Lu, Jian Cheng
Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses.
no code implementations • 24 Apr 2017 • Congqi Cao, Yifan Zhang, Chunjie Zhang, Hanqing Lu
To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously.