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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1df554697efbacc2685b9ff7d735f8d5CVF · CVPR 2026

EMR-Diff: Edge-aware Multimodal Residual Diffusion Model for Hyperspectral Image Super-resolution

Zhang, Tao, Yao, Shengtao, Zeng, Rong, Zhu, Zunjie, Zheng, Bolun, Sun, Yaoqi, Fu, Ying, Yan, Chenggang

原文摘要

Hardware constraints make it challenging to simultaneously acquire hyperspectral images (HSIs) with both high spatial and high spectral resolutions. A promising solution is to fuse low-resolution HSI (LR-HSI) with high-resolution multispectral images (HR-MSI) to generate high-resolution HSI (HR-HSI). Recently, diffusion models have introduced possibilities for HSI super-resolution, but suffer from low-efficiency sampling, detail-limited generation, and insufficient denoising. To address these issues, we propose an Edge-aware Multimodal Residual Diffusion Model (EMR-Diff). Specifically, multimodal residual mechanism is introduced to facilitate efficient information transfer among HR-MSI, LR-HSI, and HR-HSI, significantly improving the fusion efficiency. Edge-aware noise strategy is designed by exploiting the edge information of HR-MSI, which guides the model to prioritize high-frequency detail reconstruction by applying stronger noise perturbations to edge regions. In addition, we propose a Bilateral Attention Fusion UNet and design a multi-scale supervision mechanism to enable progressive reconstruction and collaborative optimization of spectral and spatial features. Extensive experiments demonstrate that our method achieves superior performance over existing approaches in both quantitative metrics and visual quality.

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:29dafd39b68ab49f3bf7d7ea4a3531d8CVF · CVPR 2026

Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models

Guo, Boyang, Li, Liang, Peng, Lin, Gao, Yuhan, Sheng, Xichun, Yan, Chenggang

原文摘要

Prompt learning has emerged as an efficient alternative to fine-tuning pre-trained vision-language models (VLMs).Despite its promise, current methods still struggle to maintain tail-class discriminability when adapting to class-imbalanced datasets. In this work, we propose cluster-aware neural collapse prompt tuning (CPT), which enhances the discriminability of tail classes in prompt-tuned VLMs without sacrificing their overall generalization.First, we design a cluster-invariant space by mining semantic assignments from the pre-trained VLM and mapping them to prompt-tuned features.This computes cluster-level boundaries and restricts the constraints to local neighborhoods, which reduces interference with the global semantic structure of the pre-trained VLM.Second, we introduce neural-collapse-driven discriminability optimization with three losses: textual Equiangular Tight Frame (ETF) separation loss, class-wise convergence loss, and rotation stabilization loss.These losses work together to shape intra-cluster geometry for better inter-class separation and intra-class alignment.Extensive experiments on 11 diverse datasets demonstrate that CPT outperforms SOTA methods, with stronger performance on long-tail classes and good generalization to unseen classes.

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:4714d53532a3deb25727f6551584f6b6CVF · CVPR 2026

GS-ASM: 2DGS-Supervised Active Stereo Matching

Wu, Zhengling, Lu, Rongfeng, Chen, Quan, Zeng, Longjian, Lu, Ming, Sun, Yaoqi, Chen, Yahong, Ji, Baofeng 等 9 位作者

原文摘要

Due to the lack of ground truth, existing methods of active stereo matching generally employ fully self-supervised learning to produce precise depth estimates. Although they can achieve promising results, their performance still has a noticeable gap compared with supervised models. To fill this gap, we propose a novel framework that synthesizes proxy labels to enable supervised training of deep active stereo networks without requiring any ground-truth depth. To expand the training data and generate disparity proxy labels, we develop an active 2D Gaussian Splatting (2DGS)-based synthesis method that explicitly models the scene geometry and the projected active pattern. Furthermore, to balance the varying contributions of different supervisions during training, we design a hybrid supervision regularization strategy that dynamically adjusts the loss weights to achieve stable optimization. We also contribute a real-world dataset captured by a handheld RealSense camera, along with our active 2DGS model, which facilitates future research on active stereo matching. Extensive qualitative and quantitative experiments demonstrate that our method achieves state-of-the-art performance on active stereo matching task. The code and dataset will be publicly released.

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:ad581980b987750eb1b73200ac0bdfb2CVF · CVPR 2026

Expert-Teacher-Student Collaborative Learning for Domain Adaptive Object Detection

Cui, Yiming, Li, Liang, Yin, Haibing, Gao, Yuhan, Sheng, Xichun, Yan, Chenggang

原文摘要

Domain adaptive object detection (DAOD) aims to generalize an object detector trained on a source domain to a target domain, where the domain gap degrades the adaptability. Recently, large-scale vision foundation models (VFMs), pretrained on web-scale datasets, exhibit such powerful generalization capabilities that many approaches leverage them to bridge the domain gap. However, their generalized knowledge is not tailored to the specific domain, which makes it difficult to offer precise guidance in the target domain. In this paper, we propose an Expert-Teacher-Student collaborative learning (ETS) framework to synergize the generalized knowledge from VFMs with the domain-specific knowledge from the teacher model. Concretely, we first design an Expert-Teacher Collaborative Teaching (ETCT) module, which leverages the complementary knowledge of expert and teacher models to collaboratively generate high-quality pseudo labels for supervising student model learning. Second, we devise an Expert-Teacher Joint Consolidating (ETJC) module, which introduces class-wise prototype alignment among expert, teacher, and student models, to jointly consolidate generalized and domain-specific knowledge within the student model. ETS leverages VFMs as the expert model in a free lunch manner, thus avoiding significant additional training costs. Extensive experiments exhibit that our method outperforms the existing SOTA methods on three benchmarks.Our code is available in the supplementary materials.

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 ↗