Preference-Guided Adaptation for Open-Vocabulary Semantic Segmentation via Prompt Disagreement

arXiv:2609.34528 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/blue-531/pref-ovss

Terminology

• Adaptation — 58× in this paper · explained in 1 episode
• Benchmark — 21× in this paper · explained in 2 episodes
• Consistency — 15× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 12× in this paper
• Ablation — 10× in this paper · explained in 1 episode
• Regularization — 9× in this paper · explained in 3 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Aggregation — 6× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Direct Preference Optimization — 5× in this paper
• Region-Localized Preference Optimization — 5× in this paper
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Diffusion Models — 3× in this paper · explained in 6 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Direct Preference Optimization (DPO) — 2× in this paper · explained in 9 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Instance Segmentation — 2× in this paper · explained in 3 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aleatoric Uncertainty — 1× in this paper · explained in 4 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• KL Regularization — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Optimal Transport — 1× in this paper · explained in 5 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Test-time augmentation (TTA) — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Rethinking Atrous Convolution for Semantic Image Segmentation
• Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation
• Language-driven Semantic Segmentation
• Open-Vocabulary Universal Image Segmentation with MaskCLIP
• OpenDAS: Open-Vocabulary Domain Adaptation for 2D and 3D Segmentation
• Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
• Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
• OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation
• Personalized OVSS: Understanding Personal Concept in Open-Vocabulary Semantic Segmentation
• Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
• Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning
• A Dense Reward View on Aligning Text-to-Image Diffusion with Preference
• DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution
• DP$^2$O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution
• SAMPO-Path: Segmentation Intent-Aligned Preference Optimization for Pathology Foundation Model Segmentation
• Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
• Multiple-Human Parsing in the Wild
• DreamBoothDPO: Improving Personalized Generation using Direct Preference Optimization
• Human Alignment of Large Language Models through Online Preference Optimisation
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models