A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models
This work addresses the need for comprehensive cognitive evaluation in LVLMs, which is incremental as it introduces a new benchmark for an existing domain.
The authors tackled the problem of evaluating high-level cognitive abilities in Large Vision-Language Models (LVLMs) by proposing a novel benchmark based on the Cookie Theft task, using 251 images with annotations, and found a significant gap between LVLMs and humans in cognitive performance.
Large Vision-Language Models (LVLMs), despite their recent success, are hardly comprehensively tested for their cognitive abilities. Inspired by the prevalent use of the Cookie Theft task in human cognitive tests, we propose a novel evaluation benchmark to evaluate high-level cognitive abilities of LVLMs using images with rich semantics. The benchmark consists of 251 images along with comprehensive annotations. It defines eight reasoning capabilities and comprises an image description task and a visual question answering task. Our evaluation of well-known LVLMs shows that there is still a significant gap in cognitive abilities between LVLMs and humans.