Summary of Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision, by Shengcao Cao et al.
Emerging Pixel Grounding in Large Multimodal Models Without Grounding Supervision
by Shengcao Cao, Liang-Yan Gui, Yu-Xiong Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The abstract presents a novel approach to grounding in large multimodal models (LMMs), which enables the model to relate language components to visual entities without explicit supervision. The authors introduce an “attend-and-segment” method that leverages attention maps from standard LMMs for pixel-level segmentation, and propose DIFFLMM, a diffusion-based visual encoder-trained LMM. Without relying on biased or limited-scale grounding-specific supervision data, the approach achieves competitive performance on both grounding-specific and general visual question answering benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large multimodal models are great at understanding language, but they struggle to connect words to pictures. A new way of training these models can actually help them learn this skill without extra instructions. The researchers developed a special method that helps the model focus on specific parts of an image and even improve its ability to relate words to visual entities. This breakthrough allows the model to do tasks like generating text about an image, and it outperforms other models in some cases. |
Keywords
» Artificial intelligence » Attention » Diffusion » Encoder » Grounding » Question answering
