Search Results for author:
Found 8 papers, 2 papers with code
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos
In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics.
MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition
To realize this goal, we integrate motion prediction, jigsaw puzzle recognition, and contrastive learning to learn skeleton features from different aspects.
Fashion Meets Computer Vision: A Survey
Fashion is the way we present ourselves to the world and has become one of the world's largest industries.
Modality Compensation Network: Cross-Modal Adaptation for Action Recognition
In this work, we propose a Modality Compensation Network (MCN) to explore the relationships of different modalities, and boost the representations for human action recognition.
Unsupervised Person Image Generation with Semantic Parsing Transformation
Firstly, a semantic generative network is proposed to transform between semantic parsing maps, in order to simplify the non-rigid deformation learning.
Temporal Bilinear Networks for Video Action Recognition
Temporal modeling in videos is a fundamental yet challenging problem in computer vision.
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding
Despite the fact that many 3D human activity benchmarks being proposed, most existing action datasets focus on the action recognition tasks for the segmented videos.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data.
Ranked #110 on
Skeleton Based Action Recognition
on NTU RGB+D
