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首次观测 · 记录 #1CVF · CVPR 2026:cvf:31ea04cc46719170fc19d13a120b98aaCVF · CVPR 2026

Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning

Hou, Shihao, Shang, Chikai, Yang, Zhiheng, Yang, Jiacheng, Shang, Xinyi, Gao, Junlong, Zhang, Yiqun, Lu, Yang

原文摘要

Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world scenarios often face the co-occurrence of non-IID data and long-tailed class distributions, presenting unique challenges that remain underexplored in PFL. In this paper, we investigate this long-tailed personalized federated learning and observe that current methods suffer from two limitations: (i) Fine-tuning degrades performance below zero-shot baselines due to the erosion of inherent class balance in foundation models; (ii) Conventional personalization techniques further transfer this bias to local models through parameter or feature-level fusion. To address these challenges, we propose Federated Learning via Gradient Purification and Residual Learning (FedPuReL), which preserves balanced knowledge in the global model while enabling unbiased personalization. Specifically, we purify local gradients using zero-shot predictions to maintain a class-balanced global model, and model personalization as residual corrections atop the frozen global model. Extensive experiments demonstrate that FedPuReL consistently outperforms state-of-the-art methods, achieving superior performance on both global and personalized models across diverse long-tailed scenarios. The code is available at https://github.com/shihaohou/FedPuReL.

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:4bb4867d3062574cc717b43dccf7fec5CVF · CVPR 2026

Direct Segmentation without Logits Optimization for Training-Free Open-Vocabulary Semantic Segmentation

Li, Jiahao, Lu, Yang, Zhang, Yachao, Wang, Fangyong, Xie, Yuan, Qu, Yanyun

原文摘要

Open-vocabulary semantic segmentation (OVSS) aims to segment arbitrary category regions in images using open-vocabulary prompts, necessitating that existing methods possess pixel-level vision-language alignment capability. Typically, this capability involves computing the cosine similarity, ie, logits, between visual and linguistic features, and minimizing the distribution discrepancy between the logits and the ground truth (GT) to generate optimal logits that are subsequently used to construct segmentation maps, yet it depends on time-consuming iterative training or model-specific attention modulation. In this work, we propose a more direct approach that eschews the logits-optimization process by directly deriving an analytic solution for the segmentation map. We posit a key hypothesis: the distribution discrepancy encodes semantic information; specifically, this discrepancy exhibits consistency across patches belonging to the same category but inconsistency across different categories. Based on this hypothesis, we directly utilize the analytic solution of this distribution discrepancy as the semantic maps. In other words, we reformulate the optimization of the distribution discrepancy as deriving its analytic solution, thereby eliminating time-consuming iterative training, freeing us from model-specific attention modulation, and achieving state-of-the-art performance on eight benchmark datasets.

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:7c3d9ab7272908386aa977333264cdceCVF · CVPR 2026

SECOS: Semantic Capture for Rigorous Classification in Open-World Semi-Supervised Learning

Liu, Hezhao, Yang, Jiacheng, Gao, Junlong, Li, Mengke, Zhang, Yiqun, Gowda, Shreyank N, Lu, Yang

原文摘要

In open-world semi-supervised learning (OWSSL), a model learns from labeled data and unlabeled data containing both known and novel classes. In practical OWSSL applications, models are expected to perform rigorous classification by directly selecting the most semantically relevant label from a candidate set for each sample. Existing OWSSL methods fail to achieve this because novel samples are trained without explicit supervision, and these methods lack mechanisms to extract latent semantic information, resulting in predicted labels that have no semantic correspondence to candidate textual labels. To address this, we introduce SEmantic Capture for Open-world Semi-supervised learning (SECOS), which directly predicts textual labels from the candidate set without post-processing, meeting the requirements of practical OWSSL applications. SECOS leverages external knowledge to extract and align semantic representations across modalities for both known and novel classes, providing explicit supervisory signals for training novel classes. Extensive experiments demonstrate that even when existing OWSSL methods are evaluated under the more lenient post-hoc matching setting, SECOS still surpasses them by up to 5.4% without such assistance, highlighting its superior effectiveness. Code is available at https://github.com/ganchi-huanggua/OSSL-Classification.

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:8500b82e59c28bb49cd0db7752851c1aCVF · CVPR 2026

Polarization State Tracing for Reflection Removal and Color-Consistent Reconstruction

Wang, Dongyue, Lu, Yang, Tian, Jiandong

原文摘要

Colored glass is widely used in everyday settings, yet its reflective and absorptive properties often introduce ghost shadows and color bias in captured images. However, existing methods typically neglect the absorption issue, making it difficult to address color bias caused by colored glass. To address this, we are the first to apply polarization imaging theory to model the light transmission process within glass. Specifically, we propose a novel imaging model, the Polarization State Tracing Model (PSTM), which traces polarized light along multiple propagation paths and accounts for wavelength-selective absorption, enabling joint reflection removal and color-consistent reconstruction. Guided by PSTM, we design a Channel Ring Attention (CRA) mechanism to efficiently capture inter-angle polarization dependencies and enhance feature interaction across polarization channels, ensuring physically consistent recovery. Besides, the recovered polarization information can be directly applied to advanced downstream tasks, such as Shape-from-Polarization (SfP). We construct a real-world dataset, GlassPol, containing a wide range of glass materials, enabling testing under diverse optical conditions. Extensive experiments show that our method outperforms existing state-of-the-art methods, achieving up to a 3dB improvement in PSNR, establishing a new benchmark for polarized reflection removal.

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:da3539c4c4479ff41c1e98f09ce224a5CVF · CVPR 2026

Decision Boundary-aware Generation for Long-tailed Learning

Yang, Jiacheng, Zhang, Ruichi, Shang, Chikai, Li, Mengke, Shang, Xinyi, Gao, Junlong, Zhang, Yonggang, Lu, Yang

原文摘要

Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this problem by generating additional data, while head-to-tail transfer further mitigate the generator bias inherit from long-tailed dataset. However, we show that while head-to-tail transfer helps balance the decision space of the classifier, it also induces latent non-local feature mixing that entangles inter-class features, causing decision boundary overlap and tail class distribution shift. To address this, we first identify the problem of boundary ambiguity and then propose Decision Boundary-aware Generation (DBG) framework, which promotes near-boundary representation learning by generating informative near-boundary samples. Overall, DBG rebalances the long-tailed dataset while yielding more separable decision space for long-tailed learning. Across standard long-tailed benchmarks, DBG consistently improves tail class and overall accuracy with less inter-class overlap. The code of DBG is available at https://github.com/keepdigitalabc-svg/DBG.

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:e9edb87e8eb3adc047230c81ed19e21eCVF · CVPR 2026

CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning

Zhang, Ruichi, Shang, Chikai, Yang, Jiacheng, Li, Mengke, Zhou, Yang, Gao, Junlong, Lu, Yang

原文摘要

Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few. Recently, fine-tuning foundation models for long-tailed learning has gained attention due to their excellent performance. However, most existing methods focus solely on mitigating long-tailed distribution bias while overlooking concept confusion caused by the long-tailed distribution. In this paper, we study this problem and attribute it to the mutual exclusivity of single-label supervision under long-tailed distributions, which suppresses feature sharing among related classes and amplifies the dominance of head classes, leading to disrupted inter-class discriminality. To address this, we propose CUE, Concept-aware Multi-label Expansion, which introduces multi-label concept signals to preserve disrupted inter-class relationships. Specifically, CUE constructs concept sets by (i) extracting instance-level visual cues from zero-shot CLIP and (ii) generating class-level semantic cues with LLM; the two cues are incorporated via separately weighted Binary Logit-Adjustmen (BLA) auxiliary losses and jointly optimized with the baseline Logit-Adjustmen (LA) loss. Experiments on several long-tailed benchmarks, CUE achieves balanced and strong performance, surpassing recent state-of-the-art methods. Code is available at: https://github.com/zhangruichi/CUE.

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 ↗