no code implementations • 7 Mar 2024 • Yutao Cui, Xiaotong Zhao, Guozhen Zhang, Shengming Cao, Kai Ma, LiMin Wang
Point-based image editing has attracted remarkable attention since the emergence of DragGAN.
no code implementations • 25 Aug 2023 • Jiaming Zhang, Yutao Cui, Gangshan Wu, LiMin Wang
To overcome these issues, we propose a unified VOS framework, coined as JointFormer, for joint modeling the three elements of feature, correspondence, and a compressed memory.
1 code implementation • NeurIPS 2023 • Yutao Cui, Tianhui Song, Gangshan Wu, LiMin Wang
Our key design is to introduce four special prediction tokens and concatenate them with the tokens from target template and search areas.
1 code implementation • ICCV 2023 • Yutao Cui, Chenkai Zeng, Xiaoyu Zhao, Yichun Yang, Gangshan Wu, LiMin Wang
We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association.
Ranked #3 on Multi-Object Tracking on SportsMOT (using extra training data)
1 code implementation • 6 Feb 2023 • Yutao Cui, Cheng Jiang, Gangshan Wu, LiMin Wang
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #1 on Visual Object Tracking on TrackingNet
1 code implementation • CVPR 2022 • Yutao Cui, Cheng Jiang, LiMin Wang, Gangshan Wu
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #6 on Visual Object Tracking on UAV123
Semi-Supervised Video Object Segmentation Visual Object Tracking
1 code implementation • 1 Apr 2021 • Yutao Cui, Cheng Jiang, LiMin Wang, Gangshan Wu
Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos.
Ranked #1 on Visual Object Tracking on VOT2019
2 code implementations • 15 Apr 2020 • Yutao Cui, Cheng Jiang, Li-Min Wang, Gangshan Wu
To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target filter based tracking paradigm.