IEEE TPAMI

Toward a Holistic Evaluation of Robustness in CLIP Models

Weijie Tu, Weijian Deng, Tom Gedeon

Comments Accepted to IEEE TPAMI, extension of NeurIPS'23 work: A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)

Unified Domain Adaptive Semantic Segmentation

Zhe Zhang, Gaochang Wu, Jing Zhang, Xiatian Zhu, Dacheng Tao, Tianyou Chai

机构 * State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China（合成自动化过程工业国家重点实验室，东北大学，沈阳，中国） ； School of Computer Science, Wuhan University（武汉大学计算机学院） ； Surrey Institute for People-Centred Artificial Intelligence, and Centre for Vision, Speech and Signal Processing, University of Surrey（以人为中心的人工智能 Surrey 院，以及视觉、语音和信号处理中心， Surrey 大学） ； College of Computing & Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学） 纠错

Comments 34 pages (main paper and supplementary material), 25 figures, 19 tables. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025

Journal ref IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 47, no. 8, pp. 6731-6748, Aug. 2025