This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo labeling process, DetCLIPv2 directly learns the fine-grained word-region alignment from massive image-text pairs in an end-to-end manner. To accomplish this, we employ a maximum word-region similarity between region proposals and textual words to guide the contrastive objective. To enable the model to gain localization capability while learning broad concepts, DetCLIPv2 is trained with a hybrid supervision from detection, grounding and image-text pair data under a unified data formulation. By jointly training with an alternating scheme and adopting low-resolution input for image-text pairs, DetCLIPv2 exploits image-text pair data efficiently and effectively: DetCLIPv2 utilizes 13X more image-text pairs than DetCLIP with a similar training time and improves performance. With 13M image-text pairs for pre-training, DetCLIPv2 demonstrates superior open-vocabulary detection performance, e.g., DetCLIPv2 with Swin-T backbone achieves 40.4% zero-shot AP on the LVIS benchmark, which outperforms previous works GLIP/GLIPv2/DetCLIP by 14.4/11.4/4.5% AP, respectively, and even beats its fully-supervised counterpart by a large margin.
Rethinking Evaluation Metrics of Open-Vocabulary Segmentaion
Harbin Engineering University（哈尔滨工程大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；The Chinese University of Hong Kong（香港中文大学）；Nanyang Technological University（南洋理工大学）；The University of California, Merced（加州大学默塞德分校）
Open Vocabulary Word Recognition From Transcribed Bangla Texts
Department of Computer Science；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh；Engineering Khulna University of Engineering \& Technology Khulna-9203, Bangladesh Email: 1 3
CE-SDWV: Effective and Efficient Concept Erasure for Text-to-Image Diffusion Models via a Semantic-Driven Word Vocabulary
College of Computer Science and Technology, Zhejiang University, China（浙江大学计算机科学与技术学院）；Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi（阿布扎赫德莫罕默德·本·扎耶德人工智能大学）；Department of Information Engineering and Computer Science, University of Trento, Italy（意大利特伦托大学信息工程与计算机科学系）
Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading
Technopark of Sfax（斯法克斯科技园）；Digital Research Center of Sfax（斯法克斯数字研究中心）；ISSAT, Gafsa university（加夫萨大学应用科学与技术学院）；Sidi Ahmed Zarrouk University Campus（西迪艾哈迈德扎鲁克大学校区）
