GLANCING, FOCUSING, REASONING: A COGNITION-INSPIRED FRAMEWORK FOR FINE-GRAINED FOOD RECOGNITION

Since ancient times, understanding food has been essential to human survival, development, and cultural formation, relying on visual perception and reasoning grounded in prior knowledge. Currently, multimodal large language models (MLLMs) exhibit remarkable capabilities in visual-semantic understanding. However, enabling MLLMs to understand fine-grained food categories poses three food-specific challenges: discovering non-part-aligned discriminative cues under interference from shared superclass patterns, recognizing ingredients under inter-ingredient similarity and intra-ingredient appearance variation, and reasoning over ingredient roles and relations. Inspired by cognitive science, we decompose food understanding into three progressive cognitive actions to address these challenges: glancing establishes coarse food context for discriminative cue discovery; focusing localizes regions to recognize ingredient identity and state; and reasoning integrates high-level semantics such as cooking methods, ingredient roles, and inter-ingredient relations. Accordingly, we propose GFRFood, a cognition-inspired MLLM framework for fine-grained food recognition (FGFR) that instantiates these three cognitive actions through two complementary modules. Cognition-Inspired Progressive Semantic Reasoning (CIPSR) generates food attributes by progressively integrating discriminative ingredient cues and high-level semantics; Taxonomy-Conditioned Semantic State-Space Model (TC-S3M) uses a selective state-space model to refine fine-grained category understanding. The two modules model visual and taxonomy semantics, respectively, and jointly enhance the MLLM's fine-grained understanding. Extensive evaluations demonstrate that GFRFood outperforms general, fine-grained, and food-specific MLLMs, achieving state-of-the-art performance. Ablation studies and visualizations demonstrate the advantages of GFRFood in attribute quality and taxonomic understanding. Dataset and code will be released upon publication.