COCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Xinghong Liu, Yi Zhou, Tao Zhou, Chun-Mei Feng, Ling Shao
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced source-free UniDA (SF-UniDA). SF-UniDA methods eliminate the need for direct access to source samples when performing adaptation to the target domain. However, existing SF-UniDA methods still require an extensive quantity of labeled source samples to train a source model, resulting in significant labeling costs. To tackle this issue, we present a novel plug-and-play classifier-oriented calibration (COCA) method. COCA, which exploits textual prototypes, is designed for the source models based on few-shot learning with vision-language models (VLMs). It endows the VLM-powered few-shot learners, which are built for closed-set classification, with the unknown-aware ability to distinguish common and unknown classes in the SF-UniDA scenario. Crucially, COCA is a new paradigm to tackle SF-UniDA challenges based on VLMs, which focuses on classifier instead of image encoder optimization. Experiments show that COCA outperforms state-of-the-art UniDA and SF-UniDA models.
EDITS: Enhancing Dataset Distillation with Implicit Textual Semantics
University of Electronic Science and Technology of China（电子科技大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research（前沿人工智能研究中心，科技研究局）
Continual Learning on CLIP via Incremental Prompt Tuning with Intrinsic Textual Anchors
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Data61, CSIRO（CSIRO数据61研究中心）；School of Computer Science, University of Auckland（奥克兰大学计算机科学学院）；Australian Institute for Machine Learning (AIML), The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
Attribute-Centric Vision-Language Alignment for Fine-Grained Gait Understanding
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；The Hong Kong University of Science and Technology（香港科技大学）；Zhejiang University（浙江大学）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Southern University of Science and Technology（南方科技大学）；Watrix Technology Limited Co. Ltd.（华瑞芯科技有限公司）
Dynamic Multimodal Prototype Learning in Vision-Language Models
University of Science and Technology of China（中国科学技术大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Sichuan University（四川大学）；National University of Singapore（新加坡国立大学）；Shenzhen University（深圳大学）
University of Electronic Science and Technology of China（电子科技大学）；Peng Cheng Laboratory（鹏城实验室）
Studies of image captioning are shifting towards a trend of a fully end-to-end paradigm by leveraging powerful visual pre-trained models and transformer-based generation architecture for more flexible model training and faster inference speed. State-of-the-art approaches simply extract isolated concepts or attributes to assist description generation. However, such approaches do not consider the hierarchical semantic structure in the textual domain, which leads to an unpredictable mapping between visual representations and concept words. To this end, we propose a novel Progressive Tree-Structur…
Class-Aware Prototype Learning with Negative Contrast for Test-Time Adaptation of Vision-Language Models
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China（人工智能研究所（TeleAI），中国电信，中华人民共和国）；College of Computer Science, Wuhan University（计算机科学学院，武汉大学）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院，光学与电子学（iOPEN），西北工业大学）
Grounding Emotion Recognition with Visual Prototypes: VEGA -- Revisiting CLIP in MERC
Xi'an Jiaotong University（西安交通大学）；Queen Mary University of London（伦敦女王玛丽大学）；Center for Multimodal AI（多模态人工智能中心）；Digital Environment Research Institute（数字环境研究院）
Learning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection
University of Chinese Academy of Sciences（中国科学院大学）；The Hong Kong Polytechnic University（香港理工大学）；Zhejiang University（浙江大学）；The University of Southern Queensland（昆士兰大学）
Bidirectional Prototype-Reward co-Evolution for Test-Time Adaptation of Vision-Language Models
School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；College of Future Information Technology, Fudan University（未来信息技术学院，复旦大学）；College of Computer Science, Wuhan University（计算机科学学院，武汉大学）
Perturbation-Regularized Open-Vocabulary Remote Sensing Segmentation with Unified Multi-Domain Evaluation
Department of Electronic Engineering and Information Science, University of Science and Technology of China（电子工程与信息科学系，中国科学技术大学）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能学院，光学与电子学系（iOPEN），西北工业大学）；Institute of Artificial Intelligence (TeleAI), China Telecom, P. R. China（人工智能研究院（TeleAI），中国电信，中华人民共和国）
