arXiv:2509.25339v3 Announce Type: replace-cross Abstract: Is basic visual understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 question-answer pairs, with privately held ground-truth responses. Unlike prior VQA datasets that typically focus on near global image understanding, VisualOverload challenges models to perform simple, knowledge-free vision tasks in densely populated (or, overloaded) scenes. Our dataset consists of high-resolution scans of public-domain paintings that are populat
The proliferation of advanced Vision-Language Models (VLMs) necessitates more rigorous evaluation benchmarks to accurately assess their capabilities beyond basic understanding.
This new benchmark highlights a critical limitation in current state-of-the-art VLMs regarding complex visual scene understanding, indicating a gap in their 'basic visual understanding'.
The focus for VLM development will likely shift towards improving object recognition and contextual understanding in 'overloaded' visual environments, rather than just global image comprehension.
- · Researchers specializing in VLM robustness
- · Developers of dense scene annotation tools
- · Companies investing in advanced visual processing for complex environments
- · VLMs optimized primarily for global image understanding
- · Benchmarking modalities focused solely on simple visual tasks
VLMs exposed to VisualOverload will demonstrate lower performance, revealing current limitations.
This will drive research and development into new architectural designs and training methodologies for VLMs to handle dense visual information more effectively.
Improved VLM performance in complex visual environments could unlock new applications in fields requiring detailed scene analysis, such as autonomous systems or medical imaging.
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Read at arXiv cs.LG