A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models

Chunhao Zhang; Kenny Q. Zhu; Mengyue Wu; Xiujie Song; Yanyi Chen

A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models

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arxiv 2402.18409 v4 pith:SYENZCEB submitted 2024-02-28 cs.AI cs.CLcs.CV

A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models

Xiujie Song , Mengyue Wu , Kenny Q. Zhu , Chunhao Zhang , Yanyi Chen This is my paper

classification cs.AI cs.CLcs.CV

keywords cognitivelvlmsabilitiesbenchmarkevaluationtaskdescriptionimage

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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.

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