no code implementations • 18 Mar 2024 • Thien-Minh Nguyen, Shenghai Yuan, Thien Hoang Nguyen, Pengyu Yin, Haozhi Cao, Lihua Xie, Maciej Wozniak, Patric Jensfelt, Marko Thiel, Justin Ziegenbein, Noel Blunder
Perception plays a crucial role in various robot applications.
no code implementations • 11 Mar 2024 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Xingyu Ji, Shenghai Yuan, Lihua Xie
Multi-modal test-time adaptation (MM-TTA) is proposed to adapt models to an unlabeled target domain by leveraging the complementary multi-modal inputs in an online manner.
1 code implementation • 21 Sep 2023 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Shenghai Yuan, Lihua Xie
In this work, we propose Multi-modal Prior Aided (MoPA) domain adaptation to improve the performance of rare objects.
no code implementations • ICCV 2023 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Pengyu Yin, Shenghai Yuan, Lihua Xie
In this paper, we explore Multi-Modal Continual Test-Time Adaptation (MM-CTTA) as a new extension of CTTA for 3D semantic segmentation.
no code implementations • 17 Nov 2022 • Yuecong Xu, Haozhi Cao, Zhenghua Chen, XiaoLi Li, Lihua Xie, Jianfei Yang
To tackle performance degradation and address concerns in high video annotation cost uniformly, the video unsupervised domain adaptation (VUDA) is introduced to adapt video models from the labeled source domain to the unlabeled target domain by alleviating video domain shift, improving the generalizability and portability of video models.
no code implementations • 10 Aug 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen
To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.
1 code implementation • 9 Mar 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Wu Min, Zhenghua Chen
Video-based Unsupervised Domain Adaptation (VUDA) methods improve the robustness of video models, enabling them to be applied to action recognition tasks across different environments.
no code implementations • 19 Feb 2022 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Jianxiong Yin, Zhenghua Chen, XiaoLi Li, Zhengguo Li, Qianwen Xu
While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored.
no code implementations • 26 Sep 2021 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Kezhi Mao, Lihua Xie, Jianxiong Yin, Simon See
This paper introduces a novel self-supervised method that leverages incoherence detection for video representation learning.
no code implementations • 21 Sep 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Min Wu, Rui Zhao, Zhenghua Chen
Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.
no code implementations • 11 Jul 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See
Yet correlation features of the same action would differ across domains due to domain shift.
no code implementations • ICCV 2021 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Qi Li, Kezhi Mao, Zhenghua Chen
For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem.
no code implementations • 26 Aug 2020 • Haozhi Cao, Yuecong Xu, Jianfei Yang, Kezhi Mao, Jianxiong Yin, Simon See
Temporal feature extraction is an essential technique in video-based action recognition.
no code implementations • 9 Jun 2020 • Yuecong Xu, Haozhi Cao, Jianfei Yang, Kezhi Mao, Jianxiong Yin, Simon See
Empirical results prove the effectiveness and efficiency of our PNL module, which achieves state-of-the-art performance of 83. 09% on the Mini-Kinetics dataset, with decreased computation cost compared to the non-local block.
1 code implementation • 6 Jun 2020 • Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See
We bridge the gap of the lack of data for this task by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset.