Search Results for author: Laiyun Qing

Found 6 papers, 3 papers with code

Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection

1 code implementation4 Dec 2023 Chen Zhang, Guorong Li, Yuankai Qi, Hanhua Ye, Laiyun Qing, Ming-Hsuan Yang, Qingming Huang

To address these limitations, we propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection, which learns multi-scale temporal features.

Anomaly Detection Video Anomaly Detection

Multi-Attention Network for Compressed Video Referring Object Segmentation

1 code implementation26 Jul 2022 Weidong Chen, Dexiang Hong, Yuankai Qi, Zhenjun Han, Shuhui Wang, Laiyun Qing, Qingming Huang, Guorong Li

To address this problem, we propose a multi-attention network which consists of dual-path dual-attention module and a query-based cross-modal Transformer module.

Object Referring Expression Segmentation +4

Activity Auto-Completion: Predicting Human Activities From Partial Videos

no code implementations ICCV 2015 Zhen Xu, Laiyun Qing, Jun Miao

Finally, the missing observation of an activity is predicted as the activity candidates provided by the auto-completion model.

Activity Prediction Information Retrieval +2

Deep Trans-layer Unsupervised Networks for Representation Learning

no code implementations27 Sep 2015 Wentao Zhu, Jun Miao, Laiyun Qing, Xilin Chen

Compared to traditional deep learning methods, the implemented feature learning method has much less parameters and is validated in several typical experiments, such as digit recognition on MNIST and MNIST variations, object recognition on Caltech 101 dataset and face verification on LFW dataset.

Face Verification Object Recognition +1

Constrained Extreme Learning Machines: A Study on Classification Cases

1 code implementation25 Jan 2015 Wentao Zhu, Jun Miao, Laiyun Qing

Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers.

Classification General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.