GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification

• URL: http://arxiv.org/abs/2512.21476v1
• Date: Thu, 25 Dec 2025 02:40:46 GMT
• Title: GPF-Net: Gated Progressive Fusion Learning for Polyp Re-Identification
• Authors: Suncheng Xiang, Xiaoyang Wang, Junjie Jiang, Hejia Wang, Dahong Qian,
• Abstract summary: Colonoscopic Polyp Re-Identification aims to match the same polyp with images from different views taken using different cameras.<n>The coarse resolution of high-level features of a specific polyp often leads to inferior results for small objects.<n>We propose a novel architecture, named Gated Progressive Fusion network, to selectively fuse features from multiple levels using gates.
• Score: 16.17476305564124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Colonoscopic Polyp Re-Identification aims to match the same polyp from a large gallery with images from different views taken using different cameras, which plays an important role in the prevention and treatment of colorectal cancer in computer-aided diagnosis. However, the coarse resolution of high-level features of a specific polyp often leads to inferior results for small objects where detailed information is important. To address this challenge, we propose a novel architecture, named Gated Progressive Fusion network, to selectively fuse features from multiple levels using gates in a fully connected way for polyp ReID. On the basis of it, a gated progressive fusion strategy is introduced to achieve layer-wise refinement of semantic information through multi-level feature interactions. Experiments on standard benchmarks show the benefits of the multimodal setting over state-of-the-art unimodal ReID models, especially when combined with the specialized multimodal fusion strategy.

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