Search Results for author: Haodong Duan

Found 18 papers, 14 papers with code

TRB: A Novel Triplet Representation for Understanding 2D Human Body

2 code implementations ICCV 2019 Haodong Duan, Kwan-Yee Lin, Sheng Jin, Wentao Liu, Chen Qian, Wanli Ouyang

In this paper, we propose the Triplet Representation for Body (TRB) -- a compact 2D human body representation, with skeleton keypoints capturing human pose information and contour keypoints containing human shape information.

Conditional Image Generation Open-Ended Question Answering

Omni-sourced Webly-supervised Learning for Video Recognition

3 code implementations ECCV 2020 Haodong Duan, Yue Zhao, Yuanjun Xiong, Wentao Liu, Dahua Lin

Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning.

Ranked #5 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition +1

Revisiting Skeleton-based Action Recognition

4 code implementations CVPR 2022 Haodong Duan, Yue Zhao, Kai Chen, Dahua Lin, Bo Dai

In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.

Group Activity Recognition Pose Estimation +1

OCSampler: Compressing Videos to One Clip with Single-step Sampling

1 code implementation CVPR 2022 Jintao Lin, Haodong Duan, Kai Chen, Dahua Lin, LiMin Wang

Recent works prefer to formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance, while we present a new paradigm of learning instance-specific video condensation policies to select informative frames for representing the entire video only in a single step.

Video Recognition

PYSKL: Towards Good Practices for Skeleton Action Recognition

1 code implementation19 May 2022 Haodong Duan, Jiaqi Wang, Kai Chen, Dahua Lin

The toolbox supports a wide variety of skeleton action recognition algorithms, including approaches based on GCN and CNN.

Action Recognition Skeleton Based Action Recognition

Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks

1 code implementation20 Sep 2022 Haodong Duan, Yue Zhao, Kai Chen, Yuanjun Xiong, Dahua Lin

Deep learning models have achieved excellent recognition results on large-scale video benchmarks.

Action Recognition

SkeleTR: Towards Skeleton-based Action Recognition in the Wild

no code implementations ICCV 2023 Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, Alessandro Bergamo

It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in the wild.

Action Classification Action Recognition +3

Self-supervised Action Representation Learning from Partial Spatio-Temporal Skeleton Sequences

1 code implementation17 Feb 2023 Yujie Zhou, Haodong Duan, Anyi Rao, Bing Su, Jiaqi Wang

Specifically, we construct a negative-sample-free triplet steam structure that is composed of an anchor stream without any masking, a spatial masking stream with Central Spatial Masking (CSM), and a temporal masking stream with Motion Attention Temporal Masking (MATM).

Action Recognition Contrastive Learning +4

SkeleTR: Towrads Skeleton-based Action Recognition in the Wild

no code implementations20 Sep 2023 Haodong Duan, Mingze Xu, Bing Shuai, Davide Modolo, Zhuowen Tu, Joseph Tighe, Alessandro Bergamo

It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in general scenarios.

Action Classification Action Recognition +2

BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues

1 code implementation20 Oct 2023 Haodong Duan, Jueqi Wei, Chonghua Wang, Hongwei Liu, Yixiao Fang, Songyang Zhang, Dahua Lin, Kai Chen

In contrast, other LLMs struggle to generate multi-turn dialogues of satisfactory quality due to poor instruction-following capability, tendency to generate lengthy utterances, or limited general capability.

Instruction Following

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