Summary of Expand Vsr Benchmark For Vllm to Expertize in Spatial Rules, by Peijin Xie et al.
Expand VSR Benchmark for VLLM to Expertize in Spatial Rules
by Peijin Xie, Lin Sun, Bingquan Liu, Dexin Wang, Xiangzheng Zhang, Chengjie Sun, Jiajia Zhang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a unified test set to evaluate the capabilities of Vision Large Language Models (VLLMs) specifically targeting visual positional reasoning. Current VLLMs are found to exhibit over-sensitivity to language instructions and under-sensitivity to visual positional information, which is mitigated by expanding the original benchmark from two aspects: tuning data and model structure. The authors expand spatially positioned image data controllably using diffusion models for the first time and integrate original visual encoding (CLIP) with other three powerful visual encoders (SigLIP, SAM, and DINO). After conducting combination experiments on scaling data and models, a VLLM VSR Expert (VSRE) is obtained that generalizes better to different instructions and accurately distinguishes differences in visual positional information. VSRE achieves over a 27% increase in accuracy on the VSR test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can learn about spatial relationships, which is important for things like recognizing objects in pictures. Researchers are trying to improve machine learning models that can see and understand images, but they need better ways to test these models’ abilities. This paper proposes a new way to test these models by using a combination of visual information and language instructions. The authors found that current models are not very good at understanding spatial relationships and developed a new model that is much better. This new model can recognize objects in images more accurately and could be used for things like self-driving cars or image recognition apps. |
Keywords
» Artificial intelligence » Diffusion » Machine learning » Sam
