40 citations · 57 across the 5 of their papers we have counts for
11 papers · 1 filter
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions
A S M Iftekhar, Hao Chen, Kaustav Kundu +3
We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize huma…
Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal
Hao Chen, Benoit Lagadec, Francois Bremond
Existing unsupervised person re-identification (ReID) methods focus on adapting a model trained on a source domain to a fixed target domain. However, an adapted ReID model usually…
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation
Zhe Wang, Hao Chen, Xinyu Li +4
Temporal action segmentation is a task to classify each frame in the video with an action label. However, it is quite expensive to annotate every frame in a large corpus of videos…
VidTr: Video Transformer Without Convolutions
Yanyi Zhang, Xinyu Li, Chunhui Liu +6
We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal infor…
Selective Feature Compression for Efficient Activity Recognition Inference
Chunhui Liu, Xinyu Li, Hao Chen +2
Most action recognition solutions rely on dense sampling to precisely cover the informative temporal clip. Extensively searching temporal region is expensive for a real-world appli…
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
Hao Chen, Benoit Lagadec, Francois Bremond
Unsupervised person re-identification (ReID) aims at learning discriminative identity features without annotations. Recently, self-supervised contrastive learning has gained increa…