TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition

• URL: http://arxiv.org/abs/2602.07262v2
• Date: Tue, 10 Feb 2026 23:43:51 GMT
• Title: TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition
• Authors: Junbo Jacob Lian, Feng Xiong, Yujun Sun, Kaichen Ouyang, Zong Ke, Mingyang Yu, Shengwei Fu, Zhong Rui, Zhang Yujun, Huiling Chen,
• Abstract summary: We introduce TwistNet-2D, a lightweight module that computes emphlocal pairwise channel products under directional spatial displacement.<n>The core component, Spiral-Twisted Channel Interaction (STCI), shifts one feature map along a prescribed direction before element-wise channel multiplication.
• Score: 8.911086692137593
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Second-order feature statistics are central to texture recognition, yet current methods face a fundamental tension: bilinear pooling and Gram matrices capture global channel correlations but collapse spatial structure, while self-attention models spatial context through weighted aggregation rather than explicit pairwise feature interactions. We introduce TwistNet-2D, a lightweight module that computes \emph{local} pairwise channel products under directional spatial displacement, jointly encoding where features co-occur and how they interact. The core component, Spiral-Twisted Channel Interaction (STCI), shifts one feature map along a prescribed direction before element-wise channel multiplication, thereby capturing the cross-position co-occurrence patterns characteristic of structured and periodic textures. Aggregating four directional heads with learned channel reweighting and injecting the result through a sigmoid-gated residual path, \TwistNet incurs only 3.5% additional parameters and 2% additional FLOPs over ResNet-18, yet consistently surpasses both parameter-matched and substantially larger baselines -- including ConvNeXt, Swin Transformer, and hybrid CNN--Transformer architectures -- across four texture and fine-grained recognition benchmarks.

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