285 citations · 499 across the 33 of their papers we have counts for
7 papers · 1 filter
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…
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…
Open-world Semantic Segmentation for LIDAR Point Clouds
Jun Cen, Peng Yun, Shiwei Zhang +5
Current methods for LIDAR semantic segmentation are not robust enough for real-world applications, e.g., autonomous driving, since it is closed-set and static. The closed-set assum…
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…
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…
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,…
TCTrack: Temporal Contexts for Aerial Tracking
Ziang Cao, Ziyuan Huang, Liang Pan +3
Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers. In this work, we present TCTrack, a comprehensive framework to fully explo…