activity
20182026
most citedFastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

219 citations · 509 across the 40 of their papers we have counts for

collaborators
Showing 2022Show all

6 papers · 1 filter

cs.CV2022★ 12 cited

Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning

Yixuan Pei, Zhiwu Qing, Jun Cen +6

Recent incremental learning for action recognition usually stores representative videos to mitigate catastrophic forgetting. However, only a few bulky videos can be stored due to t…

cs.CV2022★ 16 cited

MAR: Masked Autoencoders for Efficient Action Recognition

Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +5

Standard approaches for video recognition usually operate on the full input videos, which is inefficient due to the widely present spatio-temporal redundancy in videos. Recent prog…

cs.CV2022★ 3 cited

Context-aware Proposal Network for Temporal Action Detection

Xiang Wang, Huaxin Zhang, Shiwei Zhang +3

This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge. The task aims to localize temporal boundarie…

cs.CV2022★ 7 cited

Benchmarking Unsupervised Anomaly Detection and Localization

Ye Zheng, Xiang Wang, Yu Qi +2

Unsupervised anomaly detection and localization, as of one the most practical and challenging problems in computer vision, has received great attention in recent years. From the ti…

cs.CV2022★ 7 cited

Hybrid Relation Guided Set Matching for Few-shot Action Recognition

Xiang Wang, Shiwei Zhang, Zhiwu Qing +5

Current few-shot action recognition methods reach impressive performance by learning discriminative features for each video via episodic training and designing various temporal ali…

cs.CV2022

Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency

Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +6

Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos,…