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SHC: Deeply Activating Human-Like Cognitive Ability for Visual Question Answering
Summary
A new Systematic Human-like Cognitive (SHC) method enhances visual question answering by modeling human cognition. SHC improves multi-modal understanding and fine-grained visual perception.
Area of Science:
- Artificial Intelligence
- Cognitive Science
- Computer Vision
Background:
- Human cognition relies on complex information processing, but current AI models often study processes in isolation.
- Large language models (LLMs) have advanced language understanding, yet visual information processing remains a challenge.
Purpose of the Study:
- To propose a Systematic Human-like Cognitive (SHC) method for visual question answering (VQA).
- To enhance multi-modal understanding by systematically modeling human cognitive processes.
Main Methods:
- SHC models human cognition through three modules: Hierarchical Perception (HPM), Semantic Refinement (SRM), and Dynamic Reasoning (DRM).
- HPM extracts hierarchical features, SRM refines semantics using cascaded attention, and DRM fuses features for unified representation.
- The fused representation is used with text input in an LLM for reasoning.
Main Results:
- SHC achieves competitive performance on VQA benchmarks (VQA-v2, Text-VQA, GQA, ScienceQA).
- It shows strong results on multimodal benchmarks (POPE, MMB, MME, MM-Vet).
- SHC demonstrates superior fine-grained visual perception and outperforms larger models on some tasks.
Conclusions:
- The SHC method effectively simulates human cognitive mechanisms for improved VQA.
- SHC offers a promising approach for advancing multi-modal understanding and visual perception in AI.
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