# Inter- and Intra-image Refinement for Few Shot Segmentation Source: [Inter- and Intra-image Refinement for Few Shot Segmentation]() Collection index: [llms.txt]() Data updated: 2025-12-18T05:00:04.344Z Authors: Ourui Fu, Hangzhou He, Kaiwen Li, Xinliang Zhang, Lei Zhu, Shuang Zeng, Zhaoheng Xie, Yanye Lu Published: 2025-07-08T10:06:53.000Z ## Abstract Deep neural networks for semantic segmentation rely on large-scale annotated datasets, leading to an annotation bottleneck that motivates few shot semantic segmentation (FSS) which aims to generalize to novel classes with minimal labeled exemplars. Most existing FSS methods adopt a prototype-based paradigm, which generates query prior map by extracting masked-area features from support images and then makes predictions guided by the prior map. However, they suffer from two critical limitations induced by inter- and intra-image discrepancies: 1) The intra-class gap between support and query images, caused by single-prototype representation, results in scattered and noisy prior maps; 2) The inter-class interference from visually similar but semantically distinct regions leads to inconsistent support-query feature matching and erroneous predictions. To address these issues, we propose the Inter- and Intra-image Refinement (IIR) model. The model contains an inter-image class activation mapping based method that generates two prototypes for class-consistent region matching, including core discriminative features and local specific features, and yields an accurate and robust prior map. For intra-image refinement, a directional dropout mechanism is introduced to mask inconsistent support-query feature pairs in cross attention, thereby enhancing decoder performance. Extensive experiments demonstrate that IIR achieves state-of-the-art performance on 9 benchmarks, covering standard FSS, part FSS, and cross-domain FSS. Our source code is available at \\href{https://github.com/forypipi/IIR}{https://github.com/forypipi/IIR}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()