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首次观测 · 记录 #1CVF · CVPR 2026:cvf:acb91c0aeb24b0724a7e88f8042d17a3CVF · CVPR 2026

SANER: Switchable Adapter with Non-parametric Enhanced Routing for Person De-Reidentification

Liu, Yimin, Pu, Nan, Yang, Fengxiang, Li, Wenjing, Li, Zhihui, Zhong, Zhun

原文摘要

Person De-Reidentification (De-ReID) is an emerging and safety-critical task that aims to selectively forget specific individuals in surveillance systems while preserving the recognition capability for others. Existing methods typically learn both forgetting and retaining objectives within a unified feature space, which leads to conflicting optimization goals and may cause unexpected performance degradation on novel or retained identities. We provide a new perspective to handle De-ReID through feature space decoupling. Although it is a promising solution, discriminating which feature space should be used for the given novel query remain unsolved. To alleviate these challenges, we propose SANER, advancing De-ReID with a Switchable Adapter (SA) and a test-time Non-parametric Enhanced Routing (NER) algorithm. SA decouples the pre-trained feature space into two task-specific subspaces with a forgetting adapter and a retaining adapter. The former suppresses identity-specific semantics for de-identification, while the latter preserves discriminative cues for accurate re-ID. In addition, SA is further enhanced with NER to adaptively analyze optimal feature space routing for the given query at test-time by comparing the query with pre-computed prototypes in the original feature space. Extensive experiments on multiple De-ReID benchmarks demonstrate the effectiveness of SANER, achieving new state-of-the-art De-ReID performance.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:bae1cafda6a4a1f65bac7394c8e64a09CVF · CVPR 2026

Open-Vocabulary Domain Generalization in Urban-Scene Segmentation

Zhao, Dong, Zang, Qi, Pu, Nan, Li, Wenjing, Sebe, Nicu, Zhong, Zhun

原文摘要

Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restricted to a fixed set of known categories, limiting their applicability in open-world scenarios. Recent progress in Vision-Language Models (VLMs) has advanced Open-Vocabulary Semantic Segmentation (OV-SS) by enabling models to recognize a broader range of concepts. Yet, these models remain sensitive to domain shifts and struggle to maintain robustness when deployed in unseen environments, a challenge that is particularly severe in urban-driving scenarios. To bridge this gap, we introduce Open-Vocabulary Domain Generalization in Semantic Segmentation (OVDG-SS), a new setting that jointly addresses unseen domains and unseen categories. We introduce the first benchmark for OVDG-SS in autonomous driving, addressing a previously unexplored problem and covering both synthetic-to-real and real-to-real generalization across diverse unseen domains and unseen categories. In OVDG-SS, we observe that domain shifts often distort text-image correlations in pre-trained VLMs, which hinders the performance of OV-SS models. To tackle this challenge, we propose S^2-Corr, a state-space-driven text-image correlation refinement mechanism that can mitigate domain-induced distortions and produce a more consistent text-image correlation under distribution changes. Extensive experiments on our constructed benchmark demonstrate that the proposed method achieves superior cross-domain performance and efficiency compared to existing OV-SS approaches.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:bf6ef93444e3a71f953e7854f1149111CVF · CVPR 2026

The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery

Zheng, Haiyang, Pu, Nan, Cai, Yaqi, Long, Teng, Li, Wenjing, Sebe, Nicu, Zhong, Zhun

原文摘要

Generalized Category Discovery (GCD) leverages labeled data to categorize unlabeled samples from known or unknown classes. Most previous methods jointly optimize supervised and unsupervised objectives and achieve promising results. However, inherent optimization interference still limits their ability to improve further. Through quantitative analysis, we identify a key issue, i.e., gradient entanglement, which 1) distorts supervised gradients and weakens discrimination among known classes, and 2) induces representation-subspace overlap between known and novel classes, reducing the separability of novel categories. To address this issue, we propose the Energy-Aware Gradient Coordinator (EAGC), a plug-and-play gradient-level module that explicitly regulates the optimization process. EAGC comprises two components: Anchor-based Gradient Alignment (AGA) and Energy-aware Elastic Projection (EEP). AGA introduces a reference model to anchor the gradient directions of labeled samples, preserving the discriminative structure of known classes against the interference of unlabeled gradients. EEP softly projects unlabeled gradients onto the complement of the known-class subspace and derives an energy-based coefficient to adaptively scale the projection for each unlabeled sample according to its degree of alignment with the known subspace, thereby reducing subspace overlap without suppressing unlabeled samples that likely belong to known classes. Experiments show that EAGC consistently boosts existing methods and establishes new state-of-the-art results. Code is available at https://haiyangzheng.github.io/EAGC.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:dd6968e7ca26fab406896d7278cb4e5eCVF · CVPR 2026

GeCo: Geometry-Consistent Regularization for Domain Generalized Semantic Segmentation

Zang, Qi, Zhao, Dong, Pu, Nan, Li, Wenjing, Zhong, Zhun, Wang, Meng

原文摘要

Vision Foundation Models (VFMs) provide rich and transferable representations through large-scale pretraining, yet their high-capacity representations remain underutilized when adapted to downstream tasks. In Domain Generalization Semantic Segmentation (DGSS), parameter-efficient fine-tuning (PEFT) often overfits adapters to source domain statistics and seen class boundaries, leading to representation degradation manifested as domain bias and semantic rigidity. Existing regularization strategies alleviate this through random perturbations, but such operations disrupt the pretrained geometric structure, causing semantic drift and unstable generalization. We propose Geometry-Consistent Regularization (GeCo), which extrapolates the pretrained representation space toward the target task under structure-respected constraints, thereby preserving the inherent generalization of VFMs while enhancing their task-specific adaptation. GeCo introduces curvature-guided perturbation to modulate feature variation according to local manifold complexity of the pretrained embedding space, enabling structure-aligned representation expansion. Complementarily, a geodesic-based regularization constrains prediction shifts along smooth, manifold-aligned trajectories, ensuring semantic continuity and stable decision behavior. Extensive experiments demonstrate that GeCo achieves superior generalization across both closed-set and open-set DGSS benchmarks. The code is available at https://github.com/DZhaoXd/GeCo.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗