Summary of A Cognitive Evaluation Benchmark Of Image Reasoning and Description For Large Vision-language Models, by Xiujie Song et al.
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models
by Xiujie Song, Mengyue Wu, Kenny Q. Zhu, Chunhao Zhang, Yanyi Chen
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 paper proposes a novel benchmark to evaluate the high-level cognitive abilities of Large Vision-Language Models (LVLMs) using images with rich semantics. The benchmark, inspired by human cognitive tests like the Cookie Theft task, consists of 251 images with comprehensive annotations and two tasks: image description and visual question answering. It defines eight reasoning capabilities to assess LVLMs’ abilities. The evaluation shows that there is still a significant gap in cognitive abilities between LVLMs and humans. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper wants to see how well computers can understand images and answer questions about what they see. They created a special test with 251 pictures that have lots of details, along with instructions on what the computer should say or do about each picture. This helps scientists figure out how smart these computer models are compared to humans. |
Keywords
» Artificial intelligence » Question answering » Semantics
