Summary of Gsr-bench: a Benchmark For Grounded Spatial Reasoning Evaluation Via Multimodal Llms, by Navid Rajabi et al.
GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs
by Navid Rajabi, Jana Kosecka
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 In a breakthrough in visual reasoning, researchers have proposed a novel evaluation framework for understanding spatial relationships between objects in images. Building on an existing dataset, What’sUp, they evaluated 27 models, including early vision and language models (VLMs) and multimodal large language models (MLLMs). The study highlights the strengths and weaknesses of these models, which vary in parameter sizes, training methods, and visual resolution. The findings provide insights into the scaling laws governing this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to understand spatial relationships between objects in images. This skill is important for tasks like recognizing objects and their positions. Early computer vision models struggled with this task. Researchers have now extended a dataset called What’sUp, which tests how well language models can do this job. They tested 27 different models, including some that are good at language and others that are good at images. The results show which models are best at understanding spatial relationships. |
Keywords
» Artificial intelligence » Scaling laws
