Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation

• URL: http://arxiv.org/abs/2506.02677v1
• Date: Tue, 03 Jun 2025 09:23:20 GMT
• Title: Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation
• Authors: Jintao Tong, Yixiong Zou, Guangyao Chen, Yuhua Li, Ruixuan Li,
• Abstract summary: Cross-Domain Few-Shot (CD-FSS) aims to transfer knowledge from a source-domain dataset to unseen target-domain datasets with limited annotations.<n>Current methods typically compare the distance between training and testing samples for mask prediction.<n>We find an entanglement problem exists in this widely adopted method, which tends to bind sourcedomain patterns together.
• Score: 14.585178464610182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-Domain Few-Shot Segmentation (CD-FSS) aims to transfer knowledge from a source-domain dataset to unseen target-domain datasets with limited annotations. Current methods typically compare the distance between training and testing samples for mask prediction. However, we find an entanglement problem exists in this widely adopted method, which tends to bind sourcedomain patterns together and make each of them hard to transfer. In this paper, we aim to address this problem for the CD-FSS task. We first find a natural decomposition of the ViT structure, based on which we delve into the entanglement problem for an interpretation. We find the decomposed ViT components are crossly compared between images in distance calculation, where the rational comparisons are entangled with those meaningless ones by their equal importance, leading to the entanglement problem. Based on this interpretation, we further propose to address the entanglement problem by learning to weigh for all comparisons of ViT components, which learn disentangled features and re-compose them for the CD-FSS task, benefiting both the generalization and finetuning. Experiments show that our model outperforms the state-of-the-art CD-FSS method by 1.92% and 1.88% in average accuracy under 1-shot and 5-shot settings, respectively.

Related papers

• DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation [68.43628183890007]
  We argue that domain gaps can also be caused by different foreground (nucleus)-background ratios. First, we introduce a re-coloring method that relieves dramatic image color variations between different domains. Second, we propose a new instance normalization method that is robust to the variation in the foreground-background ratios.
  arXiv  Detail & Related papers  (2023-09-01T01:01:13Z)
• Dense Affinity Matching for Few-Shot Segmentation [83.65203917246745]
  Few-Shot (FSS) aims to segment the novel class images with a few samples. We propose a dense affinity matching framework to exploit the support-query interaction. We show that our framework performs very competitively under different settings with only 0.68M parameters.
  arXiv  Detail & Related papers  (2023-07-17T12:27:15Z)
• Cross-domain Few-shot Segmentation with Transductive Fine-tuning [29.81009103722184]
  We propose to transductively fine-tune the base model on a set of query images under the few-shot setting. Our method could consistently and significantly improve the performance of prototypical FSS models in all cross-domain tasks.
  arXiv  Detail & Related papers  (2022-11-27T06:44:41Z)
• Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment [53.401889855278704]
  Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. We propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local to local (L2L) similarity metric. Experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2022-10-04T07:54:40Z)
• Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency [0.06117371161379209]
  We show a decrease in the COVID-19 segmentation quality of the model trained on the smooth and tested on the sharp reconstruction kernels. We propose the unsupervised adaptation method, called F-Consistency, that outperforms the previous approaches.
  arXiv  Detail & Related papers  (2022-03-28T10:00:03Z)
• Maximum Spatial Perturbation Consistency for Unpaired Image-to-Image Translation [56.44946660061753]
  This paper proposes a universal regularization technique called maximum spatial perturbation consistency (MSPC) MSPC enforces a spatial perturbation function (T ) and the translation operator (G) to be commutative (i.e., TG = GT ) Our method outperforms the state-of-the-art methods on most I2I benchmarks.
  arXiv  Detail & Related papers  (2022-03-23T19:59:04Z)
• Causality-inspired Single-source Domain Generalization for Medical Image Segmentation [12.697945585457441]
  We propose a simple data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. We remove spurious correlations among objects in an image that might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains.
  arXiv  Detail & Related papers  (2021-11-24T14:45:17Z)
• DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation [97.74059510314554]
  Unsupervised domain adaptation (UDA) for semantic segmentation aims to adapt a segmentation model trained on the labeled source domain to the unlabeled target domain. Existing methods try to learn domain invariant features while suffering from large domain gaps. We propose a novel Dual Soft-Paste (DSP) method in this paper.
  arXiv  Detail & Related papers  (2021-07-20T16:22:40Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.