activity
20162023
most citedLess is More: Micro-expression Recognition from Video using Apex Frame

405 citations · 1.5k across the 24 of their papers we have counts for

collaborators

24 papers

cs.CV2023★ 4 cited

Few-shot Action Recognition via Intra- and Inter-Video Information Maximization

Huabin Liu, Weiyao Lin, Tieyuan Chen +3

Current few-shot action recognition involves two primary sources of information for classification:(1) intra-video information, determined by frame content within a single video cl…

cs.CV2023★ 47 cited

Spatio-Temporal Point Process for Multiple Object Tracking

Tao Wang, Kean Chen, Weiyao Lin +4

Multiple Object Tracking (MOT) focuses on modeling the relationship of detected objects among consecutive frames and merge them into different trajectories. MOT remains a challengi…

cs.CV2022

Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition

Huabin Liu, Weixian Lv, John See +1

A primary challenge faced in few-shot action recognition is inadequate video data for training. To address this issue, current methods in this field mainly focus on devising algori…

cs.CV2022★ 1 cited

Controllable Augmentations for Video Representation Learning

Rui Qian, Weiyao Lin, John See +1

This paper focuses on self-supervised video representation learning. Most existing approaches follow the contrastive learning pipeline to construct positive and negative pairs by s…

cs.CV2021

Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective

Yuxi Li, Ning Xu, Wenjie Yang +2

Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still sho…

cs.CV2021

Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization

Rui Qian, Yuxi Li, Huabin Liu +5

The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics…