Delving into multimodal prompting for fine-grained visual classification

• Main Authors: JIANG, Xin, TANG, Hao, GAO, Junyao, DU, Xiaoyu, HE, Shengfeng, LI, Zechao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Fine-grained visual classification; Categorization; Multimodal prompts; Optimization strategy; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8741; https://ink.library.smu.edu.sg/context/sis_research/article/9744/viewcontent/28034_Article_Text_32088_1_2_20240324.pdf
• Institution: Singapore Management University

Fine-grained visual classification (FGVC) involves categorizing fine subdivisions within a broader category, which poses challenges due to subtle inter-class discrepancies and large intra-class variations. However, prevailing approaches primarily focus on uni-modal visual concepts. Recent advancemen...