AI RESEARCH
Make It Up: Fake Images, Real Gains in Generalized Few-shot Semantic Segmentation
arXiv CS.CV
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ArXi:2603.27206v1 Announce Type: new 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.