arXiv:2508.10956v3 Announce Type: replace-cross Abstract: Inspired by human categorization, visual reasoning about object properties, such as physical attributes and functions, involves identifying and recognizing low-level details and higher-level abstractions. While current visual question answering (VQA) studies consider multiple object properties, such as size, they typically blend perception and reasoning and lack representativeness with respect to reasoning levels and image categories, making it unclear whether and how vision-language models (VLMs) recognize and reason about depicted obj
The paper addresses a current limitation in AI by focusing on the distinction between perception and higher-level reasoning in visual understanding, crucial for advancing AI capabilities beyond pattern recognition.
This research provides a foundational step towards building more robust and human-like AI, essential for applications requiring deep contextual understanding and complex decision-making.
The focus on separating perception from reasoning offers a path to developing Visual Language Models (VLMs) that truly understand object properties, rather than just identifying them.
- · AI researchers
- · VLM developers
- · Robotics
- · Autonomous systems
- · AI systems lacking advanced commonsense reasoning
- · Current VLM architectures that blend perception and reasoning
Improved performance of Visual Language Models in diverse, real-world scenarios requiring commonsense understanding.
Accelerated development of more capable AI agents that can interact with the physical world with greater nuance and insight.
Enhanced human-AI collaboration due to AI systems possessing a more intuitive grasp of object functionalities and their implications.
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Read at arXiv cs.AI