Summary of Give: Guiding Visual Encoder to Perceive Overlooked Information, by Junjie Li et al.
GiVE: Guiding Visual Encoder to Perceive Overlooked Information
by Junjie Li, Jianghong Ma, Xiaofeng Zhang, Yuhang Li, Jianyang Shi
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper introduces a new approach for enhancing multimodal large language models in applications such as text-to-video generation and visual question answering. The proposed Guiding Visual Encoder to Perceive Overlooked Information (GiVE) method improves object consideration, retrieval accuracy, and comprehensiveness by incorporating three novel loss terms: Object-focused Image-Text Contrast (OITC) loss, Object-focused Image-Image Contrast (OIIC) loss, and Object-focused Image Discrimination (OID) loss. The approach also includes dynamic visual focus adjustment and a new Multi-Object Instruction (MOInst) dataset. Experimental results show that GiVE achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper makes AI better by helping computers understand pictures better. Right now, these computer vision models are good at recognizing things in pictures but often miss important details. The new approach, called GiVE, is designed to fix this problem. It uses special techniques to help the model focus on specific objects and improve its ability to recognize them. This could be useful for tasks like generating videos from text or answering questions about what’s happening in a picture. |
Keywords
» Artificial intelligence » Encoder » Question answering
