5 citations · 9 across the 13 of their papers we have counts for
13 papers
Drift-Resilient Temporal Priors for Visual Tracking
Yuqing Huang, Liting Lin, Weijun Zhuang +2
Temporal information is crucial for visual tracking, but existing multi-frame trackers are vulnerable to model drift caused by naively aggregating noisy historical predictions. In…
Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation
Yuqing Huang, Guotian Zeng, Zhenqiao Yuan +4
Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To…
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Henghui Ding, Lingyi Hong, Chang Liu +30
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th…
Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS
Deshui Miao, Yameng Gu, Xin Li +3
Video object segmentation (VOS) is a crucial task in computer vision, but current VOS methods struggle with complex scenes and prolonged object motions. To address these challenges…
Learning Spatial-Semantic Features for Robust Video Object Segmentation
Xin Li, Deshui Miao, Zhenyu He +3
Tracking and segmenting multiple similar objects with distinct or complex parts in long-term videos is particularly challenging due to the ambiguity in identifying target component…
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Henghui Ding, Chang Liu, Yunchao Wei +34
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…
1st Place Solution for MOSE Track in CVPR 2024 PVUW Workshop: Complex Video Object Segmentation
Deshui Miao, Xin Li, Zhenyu He +2
Tracking and segmenting multiple objects in complex scenes has always been a challenge in the field of video object segmentation, especially in scenarios where objects are occluded…
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation
Zikun Zhou, Wentao Xiong, Li Zhou +3
The crux of Referring Video Object Segmentation (RVOS) lies in modeling dense text-video relations to associate abstract linguistic concepts with dynamic visual contents at pixel-l…
Spatial-Temporal Multi-level Association for Video Object Segmentation
Deshui Miao, Xin Li, Zhenyu He +2
Existing semi-supervised video object segmentation methods either focus on temporal feature matching or spatial-temporal feature modeling. However, they do not address the issues o…
RTracker: Recoverable Tracking via PN Tree Structured Memory
Yuqing Huang, Xin Li, Zikun Zhou +3
Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performa…
CiteTracker: Correlating Image and Text for Visual Tracking
Xin Li, Yuqing Huang, Zhenyu He +3
Existing visual tracking methods typically take an image patch as the reference of the target to perform tracking. However, a single image patch cannot provide a complete and preci…
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
Honghu Pan, Yongyong Chen, Yunqi He +2
Compared to visible-to-visible (V2V) person re-identification (ReID), the visible-to-infrared (V2I) person ReID task is more challenging due to the lack of sufficient training samp…
Self-Supervised Tracking via Target-Aware Data Synthesis
Xin Li, Wenjie Pei, Yaowei Wang +3
While deep-learning based tracking methods have achieved substantial progress, they entail large-scale and high-quality annotated data for sufficient training. To eliminate expensi…