Signals 4 · free daily AI digest

Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation

Paper recorded by Signals 4 on 2026-10-29 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CV · 计算机视觉 · first seen 2026-10-30

Abstract

Domain generalized semantic segmentation is challenging because it requires accurate dense prediction on unseen domains without any access to target-domain data during training. Existing methods improve transferability through data diversification, invariant learning, or direct use of foundation models, but they often struggle to simultaneously suppress domain-sensitive interference and preserve f

Read on arXiv →

#73 most recent of 710 cs.CV papers we have recorded · ↑ newer: MLLM-GS: Multimodal Large Language Model Prior-Guided Fast Novel View · ↓ older: PhysAg3D: Bridging 3D Generative and Agentic Models for High-Fidelity
Cite this page: Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation: the #73 most recent of 710 cs.CV papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/spectral-invariant-fusion-and-refinement-for-domain-generalized-semantic-segment.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CV papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions