no code implementations • 15 Apr 2024 • Shuo Wang, David C. Anastasiu, Zheng Tang, Ming-Ching Chang, Yue Yao, Liang Zheng, Mohammed Shaiqur Rahman, Meenakshi S. Arya, Anuj Sharma, Pranamesh Chakraborty, Sanjita Prajapati, Quan Kong, Norimasa Kobori, Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Fady Alnajjar, Ganzorig Batnasan, Ping-Yang Chen, Jun-Wei Hsieh, Xunlei Wu, Sameer Satish Pusegaonkar, Yizhou Wang, Sujit Biswas, Rama Chellappa
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.
no code implementations • 28 Aug 2023 • Di Yang, Yaohui Wang, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, Francois Bremond
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
no code implementations • 10 May 2023 • Di Yang, Yaohui Wang, Quan Kong, Antitza Dantcheva, Lorenzo Garattoni, Gianpiero Francesca, Francois Bremond
Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time.
no code implementations • 19 Jan 2023 • Snehashis Majhi, Rui Dai, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, Francois Bremond
Video anomaly detection in surveillance systems with only video-level labels (i. e. weakly-supervised) is challenging.
no code implementations • CVPR 2023 • Lijin Yang, Quan Kong, Hsuan-Kung Yang, Wadim Kehl, Yoichi Sato, Norimasa Kobori
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.
no code implementations • ICCV 2023 • Di Yang, Yaohui Wang, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, Francois Bremond
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
no code implementations • 26 Jul 2022 • Yoshiki Ito, Quan Kong, Kenichi Morita, Tomoaki Yoshinaga
Skeleton-based two-person interaction recognition has been gaining increasing attention as advancements are made in pose estimation and graph convolutional networks.
no code implementations • 31 Mar 2022 • Yang Shao, Quan Kong, Tadayuki Matsumura, Taiki Fuji, Kiyoto Ito, Hiroyuki Mizuno
We present Mask Atari, a new benchmark to help solve partially observable Markov decision process (POMDP) problems with Deep Reinforcement Learning (DRL)-based approaches.
1 code implementation • 20 May 2021 • C. -H. Huck Yang, Mohit Chhabra, Y. -C. Liu, Quan Kong, Tomoaki Yoshinaga, Tomokazu Murakami
We resolve this problem by introducing a robust unsupervised multi-object tracking (MOT) model: AttU-Net.
no code implementations • NeurIPS 2020 • Quan Kong, Wenpeng Wei, Ziwei Deng, Tomoaki Yoshinaga, Tomokazu Murakami
We present Cycle-Contrastive Learning (CCL), a novel self-supervised method for learning video representation.
no code implementations • ICCV 2019 • Quan Kong, Ziming Wu, Ziwei Deng, Martin Klinkigt, Bin Tong, Tomokazu Murakami
Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e. g. occlusion and appearance variation.
no code implementations • 17 Jun 2019 • Quan Kong, Bin Tong, Martin Klinkigt, Yuki Watanabe, Naoto Akira, Tomokazu Murakami
Sufficient supervised information is crucial for any machine learning models to boost performance.