Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation

• URL: http://arxiv.org/abs/2603.27206v1
• Date: Sat, 28 Mar 2026 09:22:50 GMT
• Title: Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
• Abstract summary: Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations.<n>We propose Syn4Seg, a generation-enhanced GFSS framework designed to expand novel-class coverage while improving pseudo-label quality.
• Score: 49.73871999518061
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized few-shot semantic segmentation (GFSS) is fundamentally limited by the coverage of novel-class appearances under scarce annotations. While diffusion models can synthesize novel-class images at scale, practical gains are often hindered by insufficient coverage and noisy supervision when masks are unavailable or unreliable. We propose Syn4Seg, a generation-enhanced GFSS framework designed to expand novel-class coverage while improving pseudo-label quality. Syn4Seg first maximizes prompt-space coverage by constructing an embedding-deduplicated prompt bank for each novel class, yielding diverse yet class-consistent synthetic images. It then performs support-guided pseudo-label estimation via a two-stage refinement that i) filters low-consistency regions to obtain high-precision seeds and ii) relabels uncertain pixels with image-adaptive prototypes that combine global (support) and local (image) statistics. Finally, we refine only boundary-band and unlabeled pixels using a constrained SAM-based update to improve contour fidelity without overwriting high-confidence interiors. Extensive experiments on PASCAL-$5^i$ and COCO-$20^i$ demonstrate consistent improvements in both 1-shot and 5-shot settings, highlighting synthetic data as a scalable path for GFSS with reliable masks and precise boundaries.

Related papers

• ProBAG: Prototype-Guided Boundary-Aware Graph Diffusion for Weakly Supervised Histopathology Segmentation [1.8629748140948348]
  Weakly supervised semantic segmentation enables histopathology tissue segmentation from image-level annotations.<n>We propose ProBAG, a stage-1 pseudo-mask generator that combines dataset-specific visual prototypes with pathology-aligned CONCH text prototypes.<n> Experiments on BCSS-WSSS and LUAD-HistoSeg show consistent gains over recent WSSS approaches.
  arXiv  Detail & Related papers  (2026-08-12T08:05:07Z)
• PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution [51.96078493242164]
  PRISM is a single-step diffusion-based Text-SR framework.<n> PRISM achieves state-of-the-art performance with millisecond-level inference.
  arXiv  Detail & Related papers  (2026-05-13T05:31:06Z)
• Semi-Supervised Hyperspectral Image Classification with Edge-Aware Superpixel Label Propagation and Adaptive Pseudo-Labeling [5.022329161015679]
  We propose a novel semi-supervised hyperspectral classification framework integrating spatial prior information with a dynamic learning mechanism.<n>We introduce a Dynamic History-Fused Prediction (DHP) method to smoothens pseudo-label fluctuations and improves temporal consistency and noise resistance.<n>The Dynamic Reliability-Enhanced Pseudo-Label Framework (DREPL) strengthens pseudo-label stability across temporal and sample domains.
  arXiv  Detail & Related papers  (2026-01-26T00:31:08Z)
• SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning [59.013863248600046]
  We propose a spatially re-focused super-resolution framework that refines text conditioning at inference time.<n>Second, we introduce a Spatially Targeted-Free Guidance mechanism that selectively bypasses text influences on ungrounded pixels to prevent hallucinations.
  arXiv  Detail & Related papers  (2025-10-26T05:03:55Z)
• Beyond Frequency: Scoring-Driven Debiasing for Object Detection via Blueprint-Prompted Image Synthesis [97.37770785712475]
  We present a generation-based debiasing framework for object detection.<n>Our method significantly narrows the performance gap for underrepresented object groups.
  arXiv  Detail & Related papers  (2025-10-21T02:19:12Z)
• Enhancing Generalization in Data-free Quantization via Mixup-class Prompting [8.107092196905157]
  Post-training quantization (PTQ) improves efficiency but struggles with limited calibration data, especially under privacy constraints.<n>Data-free quantization (DFQ) mitigates this by generating synthetic images using generative models such as generative adversarial networks (GANs) and text-conditioned latent diffusion models (LDMs)<n>We propose textbfmixup-class prompt, a mixup-based text prompting strategy that fuses multiple class labels at the text prompt level to generate diverse, robust synthetic data.
  arXiv  Detail & Related papers  (2025-07-29T16:00:20Z)
• Semantic Alignment and Reinforcement for Data-Free Quantization of Vision Transformers [58.80845404416028]
  Data-free quantization (DFQ) enables model quantization without accessing real data, addressing concerns regarding data security and privacy.<n>With the growing adoption of Vision Transformers (ViTs), DFQ for ViTs has garnered significant attention.<n>We propose SARDFQ, a novel Semantics Alignment and Reinforcement Data-Free Quantization method for ViTs.
  arXiv  Detail & Related papers  (2024-12-21T09:30:45Z)
• Diversified in-domain synthesis with efficient fine-tuning for few-shot classification [64.86872227580866]
  Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class. We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data. We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
  arXiv  Detail & Related papers  (2023-12-05T17:18:09Z)
• Label Geometry Aware Discriminator for Conditional Generative Networks [40.89719383597279]
  Conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes. These synthetic images have not always been helpful to improve downstream supervised tasks such as image classification.
  arXiv  Detail & Related papers  (2021-05-12T08:17:25Z)

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.