Hierarchical Prompt Injector for Domain Generalization Segmentation

• URL: http://arxiv.org/abs/2609.05864v1
• Date: Sat, 05 Sep 2026 03:56:00 GMT
• Title: Hierarchical Prompt Injector for Domain Generalization Segmentation
• Abstract summary: Domain Generalized Semantic (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains.<n>Existing methods use prompt learning to transfer structural priors into DGSS models, but typically encode each class as a single holistic prompt.<n>We address this with textbfSpatial Hierarchical Prompts (SHP) that enrich each class with region-level geometric anchors capturing structural appearance from distinct viewing angles.
• Score: 21.255611918751104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Domain Generalized Semantic Segmentation (DGSS) is a challenging task, as vision models often rely on low-level appearance cues that change across domains. In contrast, structural attributes exhibit cross-domain stability, motivating the use of structural priors for DGSS. Existing methods use prompt learning to transfer such priors into DGSS models, but typically encode each class as a single holistic prompt. Moreover, these methods apply prompts uniformly to all pixels, offering no mechanism to adapt when only a subset of object regions is visible due to viewpoint changes, occlusion, and environmental variation. We address this with \textbf{Spatial Hierarchical Prompts (SHP)} that enrich each class with region-level geometric anchors capturing structural appearance from distinct viewing angles, ensuring complementary coverage under arbitrary viewpoints. Additionally, we propose the \textbf{Hierarchical Prompt Injector (HPI)}, which enables spatially adaptive prompt injection in foundation models. HPI spatially grounds prompts by modeling their semantic relevance and spatial influence with visual features. Considering the difficulty of learning spatially and semantically aware prompt injection, we further introduce auxiliary supervision to align hierarchical prompts with their corresponding object regions. We achieve 70.62\% and 72.74\% mIoU on synthetic-to-real and real-to-real benchmarks, respectively. Code and checkpoints are released at https://github.com/MosukFate/HPI

Related papers

• LASA: A Weak Supervision Method for Open-Vocabulary Scene Sketch Semantic Segmentation [9.681252371247643]
  Open-vocabulary scene sketch semantic segmentation aims to assign dense semantic labels to sparse line drawings based on flexible category vocabularies specified at inference time.<n>Unlike natural images, sketches lack texture and color cues, making semantic understanding heavily dependent on stroke layout and spatial configuration.<n>We propose a structure-aware framework built upon textbfLayer-wise textbfAccumulated textbfStructural textbfAttention (textbfLASA), which aggregates multi-layer attention to guide hierarchical semantic alignment under weak supervision
  arXiv  Detail & Related papers  (2026-06-10T09:17:36Z)
• HAAF: Hierarchical Adaptation and Alignment of Foundation Models for Few-Shot Pathology Anomaly Detection [10.649984141835189]
  We propose the Hierarchical Adaptation and Alignment Framework (HAAF)<n>At its core is a novel Cross-Level Scaled Alignment mechanism that enforces a sequential calibration order.<n>A dual-branch inference strategy integrates semantic scores with geometric prototypes to ensure stability in few-shot settings.
  arXiv  Detail & Related papers  (2026-01-24T10:31:21Z)
• Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models [57.357091028792325]
  Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment.<n>We propose a novel constrained prompt enhancement (CPE) method to improve visual-textual alignment.<n>Our approach consists of two key components: Topology-Guided Synonymous Semantic Generation (TGSSG) and Category-Agnostic Discriminative Region Selection (CADRS)
  arXiv  Detail & Related papers  (2025-08-24T15:45:22Z)
• Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation [8.068623902839368]
  Open-Vocabulary semantic segmentation (OVSS) and domain generalization in semantic segmentation (DGSS) highlight a subtle complementarity.<n>OV-DGSS aims to generate pixel-level masks for unseen categories while maintaining robustness across unseen domains.<n>We introduce Vireo, a novel single-stage framework for OV-DGSS that unifies the strengths of OVSS and DGSS for the first time.
  arXiv  Detail & Related papers  (2025-06-11T15:54:47Z)
• In the Era of Prompt Learning with Vision-Language Models [1.060608983034705]
  We introduce textscStyLIP, a novel domain-agnostic prompt learning strategy for Domain Generalization (DG) StyLIP disentangles visual style and content in CLIPs vision encoder by using style projectors to learn domain-specific prompt tokens. We also propose AD-CLIP for unsupervised domain adaptation (DA), leveraging CLIPs frozen vision backbone.
  arXiv  Detail & Related papers  (2024-11-07T17:31:21Z)
• PointDGMamba: Domain Generalization of Point Cloud Classification via Generalized State Space Model [77.00221501105788]
  Domain Generalization (DG) has been recently explored to improve the generalizability of point cloud classification.<n>We present the first work that studies the generalizability of state space models (SSMs) in DG PCC.<n>We propose a novel framework, PointDGMamba, that excels in strong generalizability toward unseen domains.
  arXiv  Detail & Related papers  (2024-08-24T12:53:48Z)
• Beyond Mask: Rethinking Guidance Types in Few-shot Segmentation [67.35274834837064]
  We develop a universal vision-language framework (UniFSS) to integrate prompts from text, mask, box, and image. UniFSS significantly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-16T08:41:01Z)
• CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing [66.6712018832575]
  Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. We make use of large-scale VLMs like CLIP and leverage the textual feature to dynamically adjust the classifier's weights for exploring generalizable visual features.
  arXiv  Detail & Related papers  (2024-03-21T11:58:50Z)
• Deep Hierarchical Semantic Segmentation [76.40565872257709]
  hierarchical semantic segmentation (HSS) aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. HSSN casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. With hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space, so as to generate well-structured pixel representations.
  arXiv  Detail & Related papers  (2022-03-27T15:47:44Z)
• HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning [74.76431541169342]
  Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. We propose a novel hierarchical semantic-visual adaptation (HSVA) framework to align semantic and visual domains. Experiments on four benchmark datasets demonstrate HSVA achieves superior performance on both conventional and generalized ZSL.
  arXiv  Detail & Related papers  (2021-09-30T14:27:50Z)

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.