4 papers indexed
Chenglizhao Chen, Tianxiang Nan, Wen Li, Xinyu Liu, Guisheng Zhang, Mengke Song, et al.
Weakly supervised video anomaly detection relies solely on video-level labels for training, making it difficult to accurately localize anomalous events in complex scenes. In real-world videos, anomalous behaviors exhibit large variations in appearance and temporal duration, while…
Zhimin Yuan, Ming Cheng, Shangshu Yu, Wen Li, Dunqiang Liu, Xin Huang, et al.
3D unsupervised domain adaptive (UDA) segmentation mitigates the high cost of manual annotations of the new domain data. Self-training has emerged as the dominant approach in this area, where its success heavily depends on a well-initialized warm-up model to generate reliable pse…
Yanan Wang, Wen Li, Yibin Ying, Zhenghao Fei
Reasoning segmentation requires localizing targets based on complex, implicit queries. Current end-to-end models typically entangle perception and deduction into an opaque black box, severely limiting interpretability and scalability. To address this, we propose GEAR-Seg (Grounde…
Shangxuan Xie, Haifeng Wu, Yuhang Wang, Huarong Jia, Wen Li
Homography estimation, as one of the fundamental problems in computer vision, remains challenged by scale variation scenarios where image pairs potentially exhibit significant scale discrepancies. Existing deep learning frameworks frequently suffer from a significant performance…