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.
Abstract (translated)
结肠镜息肉再识别旨在从大量图像库中匹配同一息肉，这些图像是使用不同摄像头从不同角度拍摄的。这一技术在计算机辅助诊断中对预防和治疗大肠癌起着重要作用。然而，特定息肉的高级特征通常分辨率较低，这导致对于细节信息至关重要的小对象识别效果不佳。为了解决这一挑战，我们提出了一种新颖的架构，名为门控渐进融合网络（Gated Progressive Fusion network），该架构通过使用全连接方式中的门来选择性地融合来自多个级别的特征，以实现息肉再识别。在此基础上，引入了门控渐进融合策略，通过多层次特征交互实现语义信息逐层细化。 在标准基准上的实验表明，多模态设置相较于最先进的单模态再识别模型具有优势，尤其是在结合专门设计的多模态融合策略时效果更佳。
URL
https://arxiv.org/abs/2512.21476
