跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+1087新增论文
+804其中 AI 相关
+107已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-14 14:09 · 全站最近入库：2026-11-13 20:44各信源进度 →

统计口径与数据新鲜度

统计 2026-11-13 14:14 — 2026-11-14 14:14（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-14 14:09最近发现新增版本：2026-11-13 20:44库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,727 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 14 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-14 14:07（北京时间）
首次观测 · 记录 #1CVF · CVPR 2026:cvf:19383edbeb5d672d8e19e5c18e856c18CVF · CVPR 2026

S2C2Seg: Semantic-Spatial Consistency and Category Optimization for Open-Vocabulary Segmentation

Qing, Yuhao, Wang, Yueying, Chen, Chaoyang, Zhang, Weidong, Wen, Jie, Xu, Xin

原文摘要

Open-vocabulary semantic segmentation extends pixel-level recognition to arbitrary text-described categories. Despite strong global semantic understanding, vision-language models such as CLIP exhibit limited spatial precision and semantic ambiguity across large vocabularies, constraining their effectiveness for dense prediction. We present S2C2Seg, a training-free framework that integrates with existing methods through Category Subset Selection (CSS) and Consistent Semantic Guidance (CSG). CSS employs three complementary scoring functions to filter category candidates: CLIP-based global semantic similarity, spatial presence from dense prediction models, and multi-view consistency via alignment and conditional entropy. This joint exploitation of semantic, spatial, and consistency cues reduces category redundancy and semantic ambiguity. CSG adaptively fuses CLIP global features with local spatial predictions through category-specific confidence weighting, applying stronger regularization to high-similarity categories for correcting prediction biases while preserving spatial precision for low-confidence categories. Extensive experiments across eight benchmarks demonstrate broad applicability: when integrated with SCLIP, ProxyCLIP, and CorrCLIP, S2C2Seg achieves consistent improvements of 3.4 to 9.7 percentage points in mIoU, establishing a new state-of-the-art of 51.2% average mIoU.

cs.CV · cs.CL

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:53d3a274e781a6bdafe8506e98dbb0f4CVF · CVPR 2026

META: Meta Evolution of Tool Trajectory Adaptation for Long-Video Understanding

Huang, Jing, Chen, Luyuan, Xu, Zhijie, Li, Yadong, Xu, Xingzhong, Chen, Siye, Liu, Jie, Kong, Ming 等 9 位作者

原文摘要

Long-video understanding remains challenging due to extreme temporal redundancy, sparse yet decisive events, and the instability of long-horizon reasoning in visual-language models (VLMs). Existing agent-based methods invoke external micro-tools but remain static, repeatedly rebuilding long chains of fine-grained operations for each task without acquiring reusable multi-step perceptual skills.We propose META, the first training-free agent capable of self-evolving its tool-augmented reasoning. META operates through dual Solving and Evolving loops: it analyzes its own tool trajectories, abstracts recurring multi-step patterns into reusable macro-tools, and distills failed executions into structured failure priors that refine tool usage. Through symbolic consolidation and pruning, META progressively shortens reasoning paths and acquires more general perceptual and temporal abilities--without any parameter updates. META achieves state-of-the-art performance on long-video benchmarks, demonstrating a scalable, model-agnostic paradigm for long-video understanding that can continually evolve without additional training.

cs.CV · cs.CL

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:66f0de25e496a2fbbc46576356e7605eCVF · CVPR 2026

MASQuant: Modality-Aware Smoothing Quantization for Multimodal Large Language Models

Hu, Lulu, Xiao, Wenhu, Chen, Xin, Xu, Xinhua, Xu, Bowen, Li, Kun, Tao, Yongliang

原文摘要

Post-training quantization (PTQ) with computational equivalence for Large Language Models (LLMs) have demonstrated remarkable advances, however, their application to Multimodal Large Language Models (MLLMs) presents substantial challenges. In this paper, we analyze SmoothQuant as a case study and identify two critical issues: Smoothing Misalignment and Cross-Modal Computational Invariance. To address these issues, we propose Modality-Aware Smoothing Quantization (MASQuant), a novel framework that introduces (1) Modality-Aware Smoothing (MAS), which learns separate, modality-specific smoothing factors to prevent Smoothing Misalignment, and (2) Cross-Modal Compensation (CMC), which addresses Cross-modal Computational Invariance by using SVD whitening to transform multi-modal activation differences into low-rank forms, enabling unified quantization across modalities. MASQuant demonstrates stable quantization performance across both dual-modal and tri-modal MLLMs. Experimental results show that MASQuant is competitive among the state-of-the-art PTQ algorithms.

cs.CV · cs.CL

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:6f4dc14928c1f853df1171b77943777cCVF · CVPR 2026

FedBPrompt: Federated Domain Generalization Person Re-Identification via Body Distribution Aware Visual Prompts

Xu, Xin, Li, Weilong, Liu, Wei, Huang, Wenke, Yu, Zhixi, Yang, Bin, Liao, Xiaoying, Jiang, Kui

原文摘要

Federated Domain Generalization for Person Re-Identification (FedDG-ReID) aims to learn domain-invariant representations from decentralized data. Although Vision Transformers (ViTs) are widely adopted, their global attention often fails to distinguish pedestrians from high similarity backgrounds or diverse viewpoints---a challenge further amplified by cross-client distribution shifts in FedDG-ReID. To address this, we propose Federated Body Distribution Aware Visual Prompt (FedBPrompt), which introduces learnable visual prompts to explicitly guide Transformer attention toward pedestrian-centric regions. FedBPrompt employs a Body Distribution Aware Visual Prompts Mechanism (BAPM) that divides prompts into two groups: Holistic Full Body Prompts suppress cross-client background noise, while Body Part Alignment Prompts capture fine-grained details robust to pose and viewpoint variations. To mitigate the high communication cost of large Transformer models, we further design a Prompt-based Fine-Tuning Strategy (PFTS) that freezes the ViT backbone and updates only lightweight prompts, significantly reducing communication overhead while maintaining adaptability. Extensive experiments demonstrate that BAPM effectively enhances feature discrimination and cross-domain generalization, while PFTS achieves notable performance gains within only a few aggregation rounds. Moreover, both BAPM and PFTS can be easily integrated into existing ViT-based FedDG-ReID frameworks, making FedBPrompt a flexible and effective solution for federated person re-identification. The code will be released.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:8ff40234dc6409f56803c3451e35816cCVF · CVPR 2026

VAST: Video Ability-Stratified Taxonomy for Data-Efficient Video Reasoning

Wang, Zhongan, Wen, Xiaoyu, Du, Lingxiao, Li, Kun, Wu, Zhiliang, Xu, Xingcheng, Zhang, Qiaosheng, Lu, Chaochao 等 9 位作者

原文摘要

Reinforcement learning (RL) has emerged as an effective approach for improving video reasoning in multimodal large language models (MLLMs). However, existing methods remain inefficient for two reasons. First, training data are typically organized by task formats rather than underlying reasoning abilities, creating a mismatch that encourages models to learn task-specific patterns instead of transferable capabilities. As a result, improving ability generalization requires broad coverage over many ability-task combinations, making RL training costly. Second, prior work often compensates for this inefficiency with increasingly complex algorithmic designs, such as specialized temporal architectures or multi-objective reward frameworks, which further complicate training. To address these issues, we present VAST, a cognitive taxonomy and data organization framework that structures video understanding into three layers: Perception, Reasoning, and Cognition. Based on this taxonomy, we construct VAST-15K for training and VAST-Bench for evaluation. We further introduce VideoVAST, a reinforcement learning framework that uses consistency rewards to encourage alignment between reasoning traces and final answers, without architectural modifications. Experiments show that VideoVAST achieves 66.3% accuracy on MVBench and 57.4% on VAST-Bench, compared with 62.7% and 54.3% for Video-R1, while using approximately 72% fewer GPU hours and 96% fewer training samples under the same training settings. Code and resources are available at https://zhongan-wang.github.io/VAST.

cs.CV · cs.CL

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a42aea1301c7406e75a7916939306934CVF · CVPR 2026

RHCNet: Residual-Guided Hierarchical Calibration Network for Robust Underwater Object Detection

Wang, Yueying, Guo, Yiteng, Zhang, Weidong, Wen, Jie, Shen, Liquan, Yan, Huaicheng, Xu, Xin

原文摘要

Underwater images commonly suffer from foreground-background ambiguity, loss of structural details, and severely reduced contrast, which collectively make underwater object detection (UOD) an inherently challenging task. To handle this issue, we present a residual-guided hierarchical calibration network (RHCNet) designed to achieve more efficient and robust UOD, which comprises a residual-guided feature enhancement module (RGFE) and a hierarchical feature calibration pyramid module (HFCP). Concretely, RHCNet extends the standard ResNet-50 backbone by embedding the RGFE, which effectively strengthens the representation of edge and texture features in blurry regions by jointly leveraging convolutional operations and attention mechanisms to achieve more discriminative feature extraction for UOD. Subsequently, the HFCP integrates a bottom-up semantic enhancement path and a top-down fine-grained feature compensation path, while a K-means clustering-guided feature calibration module is jointly employed to ensure multi-level cross-scale semantic consistency and accurate alignment of salient region features. Extensive experiments on the DUO and UTDAC benchmark datasets demonstrated that our RHCNet attains the highest AP scores of 70.53% and 53.35%, respectively. Besides, our RHCNet also maintains excellent detection accuracy and strong generalization capability on the COCO dataset for terrestrial scenarios. The code is available at https://github.com/YitengGuo/RHCNet.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a6ee95c5d784924b8010e2d80539b292CVF · CVPR 2026

TextOVSR: Text-Guided Real-World Opera Video Super-Resolution

Chang, Hua, Xu, Xin, Liu, Wei, Wu, Jiayi, Jiang, Kui, Ma, Fei, Tian, Qi

原文摘要

Many classic opera videos exhibit poor visual quality due to the limitations of early filming equipment and long-term degradation during storage. Although real-world video super-resolution (RWVSR) has achieved significant advances in recent years, directly applying existing methods to degraded opera videos remains challenging. The difficulties are twofold. First, accurately modeling real-world degradations is complex: simplistic combinations of classical degradation kernels fail to capture the authentic noise distribution, while methods that extract real noise patches from external datasets are prone to style mismatches that introduce visual artifacts. Second, current RWVSR methods, which rely solely on degraded image features, struggle to reconstruct realistic and detailed textures due to a lack of high-level semantic guidance. To address these issues, we propose a Text-guided Dual-Branch Opera Video Super-Resolution (TextOVSR) network, which introduces two types of textual prompts to guide the super-resolution process. Specifically, degradation-descriptive text, derived from the degradation process, is incorporated into the negative branch to constrain the solution space. Simultaneously, content-descriptive text is incorporated into a positive branch and our proposed Text-Enhanced Discriminator (TED) to provide semantic guidance for enhanced texture reconstruction. Furthermore, we design a Degradation-Robust Feature Fusion (DRF) module to facilitate cross-modal feature fusion while suppressing degradation interference. Experiments on our OperaLQ benchmark show that TextOVSR outperforms state-of-the-art methods both qualitatively and quantitatively. The code is available at https://github.com/ChangHua0/TextOVSR.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:b2338304df35ef7add0c6b083aa2a799CVF · CVPR 2026

GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models

Wei, Jingxuan, Jia, Caijun, Bai, Xi, Xu, Xinglong, Li, Siyuan, Sun, Linzhuang, Yu, Bihui, He, Conghui 等 10 位作者

原文摘要

Unified Multimodal Models (UMMs) are redefining the landscape of artificial intelligence by coupling perception and generation across language, vision, and structured reasoning. Yet, despite their growing sophistication, a critical gap persists in evaluation: existing benchmarks largely measure discriminative understanding or unconstrained generation in isolation, overlooking the integrated generative reasoning required for genuine multimodal intelligence. To address this, we introduce GGBench, the benchmark explicitly designed to evaluate geometric generative reasoning--the ability of a model to understand, reason about, and construct a solution within a unified framework. Each instance in GGBench contains precisely aligned natural-language instructions, executable GeoGebra code, and rendered diagrams, enabling deterministic and interpretable verification of a model's reasoning and constructive fidelity. The benchmark comprises 1,411 rigorously curated problems covering eight categories and multiple difficulty levels, resulting in over 7,000 aligned visualizations. We propose a comprehensive tri-modal evaluation protocol that jointly assesses textual planning quality, code executability, and geometric accuracy of generated diagrams through both automated and human-in-the-loop judging. Extensive experiments on both state-of-the-art UMMs and general Large Language Models (LLMs) reveal a large performance gap between end-to-end generation and reasoning-grounded construction. GGBench establishes a new standard for testing multimodal systems that must not only understand but also build, marking a crucial step toward grounded, verifiable generative intelligence.

cs.CV · cs.CL · cs.AI

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:cb15c39b38ba642bd65b6c79c095dbd1CVF · CVPR 2026

Language-Grounded Decoupled Action Representation for Robotic Manipulation

Weng, Wuding, Wu, Tongshu, Chen, Liucheng, Xie, Siyu, Wang, Zheng, Xu, Xing, Song, Jingkuan, Shen, Heng Tao

原文摘要

The heterogeneity between high-level vision-language understanding and low-level action control remains a fundamental challenge in robotic manipulation. Although recent methods have advanced task-specific action alignment, they often struggle to generate robust and accurate actions for novel or semantically related tasks. To address this, we propose the Language-Grounded Decoupled Action Representation (LaDA) framework, which leverages natural language as a semantic bridge to connect perception and control. LaDA introduces a fine-grained intermediate layer of three interpretable action primitives--translation, rotation, and gripper control--providing explicit semantic structure for low-level actions. It further employs a semantic-guided soft-label contrastive learning objective to align similar action primitives across tasks, enhancing generalization and motion consistency. An adaptive weighting strategy, inspired by curriculum learning, dynamically balances contrastive and imitation objectives for stable and effective training. Extensive experiments on simulated benchmarks (LIBERO and MimicGen) and real-world demonstrations validate that LaDA achieves strong performance and generalizes effectively to unseen or related tasks.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ce2070f2400669e73b5c87cecb4b4149CVF · CVPR 2026

From Few-way to Many-way: Rethinking Few-shot Fine-grained Image Classification

Zhao, Li-Jun, Chen, Zhen-Duo, Luo, Xin, Xu, Xin-Shun

原文摘要

Few-shot fine-grained image classification (FSFG) aims to recognize novel fine-grained categories from only a few labeled samples. Existing FSFG methods primarily focus on fine-grained feature extraction and modeling query-support interactions within training episodes containing a small number of classes. Relying on the episodic training strategy, these methods typically assume that the capabilities learned on training samples can directly transfer to evaluation episodes with a few novel classes (few-way). However, in more practical and challenging scenarios involving many novel classes (many-way), existing approaches lack a reliable and global characterization of the feature space, making it difficult for episodic adaptation alone to generalize effectively. In this paper, we pioneer a theoretical analysis of novel class behavior in FSFG and derive a class discriminative index bound. Guided by this analysis, we propose a novel SCEG method that incorporates Self and Collaborative feature extraction as well as Episodic and Global feature space optimization. Extensive experiments demonstrate that our method consistently and significantly outperforms existing methods under both conventional few-way and the new many-way settings. Codes are available at: https://github.com/Legenddddd/SCEG.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e005da6837430217faba23b158329187CVF · CVPR 2026

Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

Jiang, Xun, Gu, Yufan, Hu, Disen, Hou, Yuqing, Yao, Yazhou, Shen, Fumin, Shen, Heng Tao, Xu, Xing

原文摘要

Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty towards the reliability of individual modalities and instances during learning. In this paper, we propose a unified framework, termed Conformal Predictive Self-Calibration (CPSC), which leverages conformal prediction to equip the model with the ability to perform self-guided calibration on-the-fly. The core of our proposed CPSC lies in a novel self-calibrating training loop that seamlessly integrates two key modules: (1) Representation Self-Calibration, which decomposes unimodal features into components, selectively fuses the most robust ones identified by a conformal predictor to enhance feature resilience. (2) Gradient Self-Calibration, which recalibrates the gradient flow during backpropagation based on instance-wise reliability scores, steering the optimization towards more trustworthy directions. Furthermore, we also devise a self-update strategy for the conformal predictor to ensure the entire system co-evolves consistently throughout the training process. Extensive experiments on six benchmark datasets under both imbalanced and noisy settings demonstrate that our CPSC framework consistently outperforms existing state-of-the-art methods. Our code is available at https://github.com/XunCHN/CPSC.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e14964bac8d0487c786691a97f222619CVF · CVPR 2026

Gravitation-Driven Semantic Alignment for Text Video Retrieval

Yang, Yi, Wang, Zheng, Xu, Xing, Song, Jingkuan, Shen, Heng Tao

原文摘要

The inherent semantic ambiguity of "many-to-many", where one video matches multiple texts and vice versa, aggravates the difficulty in text-video retrieval. The dominant deterministic embeddings only struggle to capture the mean semantics, while existing probabilistic methods fail to distinguish hard negatives for their imposing rigid uncertainty priors or ignoring the interaction between similarity and uncertainty. To this end, we propose a novel physics-inspired framework (GraviAlign) that decomposes the alignment of cross-modal semantic distributions into two orthogonal factors inspired by the Gravitational Force: (1) Semantic Attraction measuring gravitational alignment between distribution centers via uncertainty-derived "semantic mass" and "semantic distance"; (2) Geometric Overlap quantifying distribution intersection. Each factor has independent veto power to reject those matches with misalignment or poor overlap. Additionally, GraviAlign offers an efficient (O(D)), theoretically grounded alternative to intractable joint integrals. Extensive experiments on DiDeMo, MSR-VTT, and ActivityNet demonstrate our effectiveness and superiority, and solid ablation studies confirm the indispensability of two novel components.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e5e362e4b25f2caf450b08430cd5b008CVF · CVPR 2026

VLM4RSDet: Collaborative Optimization with Vision-Language Model for Enhancing Remote Sensing Object Detection

Shi, Shuohao, Fang, Qiang, Xu, Xin

原文摘要

Closed-set object detection in remote sensing imagery has made significant progress, but achieving high detection accuracy remains challenging. Vision-Language Models (VLMs), which possess rich prior knowledge, offer a promising solution to this challenge. However, most existing VLMs are designed for open-vocabulary tasks and exhibit inherent limitations when directly applied to closed-set scenarios, such as notable accuracy degradation and high deployment costs. To address these issues, we propose VLM4RSDet, a novel collaborative training framework that leverages vision-language model to enhance the performance of conventional closed-set remote sensing object detectors. Notably, during inference, VLM4RSDet only retains the standard object detection architecture, thus avoiding any additional deployment overhead. Furthermore, we introduce a Global-Local Cross-Attention (GLCA) module and a Learnable Hierarchical Prediction Strategy (LHPS) to further improve collaborative training performance. Extensive experiments on five benchmark datasets demonstrate the effectiveness and robustness of our approach. In particular, our method outperforms the state-of-the-art by 7.5% in mAP_ 0.5:0.95 on the VisDrone2019 dataset. Our code is available at \href https://github.com/cszzshi/VLM4RSDet https://github.com/cszzshi/VLM4RSDet .

cs.CV · cs.CL

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

查看论文
CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:eaefe516d2a72ad4f398ca72d4a53c52CVF · CVPR 2026

Contrastive Cross-Bag Augmentation for Multiple Instance Learning-based Whole Slide Image Classification

Zhang, Bo, Xu, Xinan, Yan, Shuo, Bai, Yu, Zhang, Zheng, Wang, Wufan, Gao, Hui, Wang, Wendong

原文摘要

Recent pseudo-bag augmentation methods for Multiple Instance Learning (MIL)-based Whole Slide Image (WSI) classification sample instances from a limited number of bags, resulting in constrained diversity. To address this issue, we propose Contrastive Cross-Bag Augmentation (C2Aug) to sample instances from all bags with the same class to increase the diversity of pseudo-bags. However, introducing new instances into the pseudo-bag increases the number of critical instances (e.g., tumor instances). This increase results in a reduced occurrence of pseudo-bags containing few critical instances, thereby limiting model performance, particularly on test slides with small tumor areas. To address this, we introduce a bag-level and group-level contrastive learning framework to enhance the discrimination of features with distinct semantic meanings, thereby improving model performance. C2Aug samples instances with consistent bag-level labels across the entire dataset, which simultaneously enhances the diversity of pseudo-bags and mitigates label noise. Experimental results demonstrate that C2Aug consistently outperforms state-of-the-art approaches across multiple evaluation metrics. Our code is publicly available at: https://github.com/weiaicunzai/mixup.

cs.CV

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

查看论文
CVF · CVPR 2026 ↗PDF ↗