A multi-agent vision-language framework that extracts event, time, and location from public event images, evaluated with a new soft metric on VLM-augmented datasets.
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision-Language Models
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abstract
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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cs.CV 1years
2025 1verdicts
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GETReason: Enhancing Image Context Extraction through Hierarchical Multi-Agent Reasoning
A multi-agent vision-language framework that extracts event, time, and location from public event images, evaluated with a new soft metric on VLM-augmented datasets.