RAR：用于视觉识别的检索与排序增强多模态大语言模型
RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition
AI总结：
RAR结合CLIP的检索与MLLM的排序，提升细粒度识别精度，增强少样本和零样本任务表现。
摘要：
CLIP通过对比学习从噪声图像-文本对中脱颖而出，但在区分细粒度项目时精度不足。多模态大语言模型（MLLMs）在细粒度分类中表现优异，但随着类别数增加，性能下降。本文提出RAR方法，通过CLIP建立多模态检索器以存储额外记忆，利用MLLM进行排序和预测，提升视觉语言识别任务的准确性，并在多个基准测试中取得显著改进。
英文摘要：
CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of candidates, yet its focus on broad associations hinders the precision in distinguishing subtle differences among fine-grained items. Conversely, Multimodal Large Language Models (MLLMs) excel at classifying fine-grained categories, thanks to their substantial knowledge from pre-training on web-level corpora. However, the performance of MLLMs declines with an increase in category numbers, primarily due to growing complexity and constraints of limited context window size. To synergize the strengths of both approaches and enhance the few-shot/zero-shot recognition abilities for datasets characterized by extensive and fine-grained vocabularies, this paper introduces RAR, a Retrieving And Ranking augmented method for MLLMs. We initially establish a multi-modal retriever based on CLIP to create and store explicit memory for different categories beyond the immediate context window. During inference, RAR retrieves the top-k similar results from the memory and uses MLLMs to rank and make the final predictions. Our proposed approach not only addresses the inherent limitations in fine-grained recognition but also preserves the model's comprehensive knowledge base, significantly boosting accuracy across a range of vision-language recognition tasks. Notably, our approach demonstrates a significant improvement in performance on 5 fine-grained visual recognition benchmarks, 11 few-shot image recognition datasets, and the 2 object detection datasets under the zero-shot recognition setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
