Fraesormer: Learning Adaptive Sparse Transformer for Efficient Food Recognition

• URL: http://arxiv.org/abs/2503.11995v1
• Date: Sat, 15 Mar 2025 05:13:26 GMT
• Title: Fraesormer: Learning Adaptive Sparse Transformer for Efficient Food Recognition
• Authors: Shun Zou, Yi Zou, Mingya Zhang, Shipeng Luo, Zhihao Chen, Guangwei Gao,
• Abstract summary: We propose an adaptive and efficient sparse Transformer architecture (Fraesormer) with two core designs.<n>ATK-SPA uses a learnable Gated Dynamic Top-K Operator (GDTKO) to retain critical attention scores.<n>HSSFGN employs gating mechanism to achieve multi-scale feature representation.
• Score: 9.83509397800422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, Transformer has witnessed significant progress in food recognition. However, most existing approaches still face two critical challenges in lightweight food recognition: (1) the quadratic complexity and redundant feature representation from interactions with irrelevant tokens; (2) static feature recognition and single-scale representation, which overlook the unstructured, non-fixed nature of food images and the need for multi-scale features. To address these, we propose an adaptive and efficient sparse Transformer architecture (Fraesormer) with two core designs: Adaptive Top-k Sparse Partial Attention (ATK-SPA) and Hierarchical Scale-Sensitive Feature Gating Network (HSSFGN). ATK-SPA uses a learnable Gated Dynamic Top-K Operator (GDTKO) to retain critical attention scores, filtering low query-key matches that hinder feature aggregation. It also introduces a partial channel mechanism to reduce redundancy and promote expert information flow, enabling local-global collaborative modeling. HSSFGN employs gating mechanism to achieve multi-scale feature representation, enhancing contextual semantic information. Extensive experiments show that Fraesormer outperforms state-of-the-art methods. code is available at https://zs1314.github.io/Fraesormer.

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