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首次观测 · 记录 #1CVF · CVPR 2026:cvf:0609ebbcfbb5bcf90d18daf5caaf41eeCVF · CVPR 2026

UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

Zhang, Yanran, Zheng, Wenzhao, Li, Yifei, Yu, Bingyao, Zheng, Yu, Chen, Lei, Lu, Jiwen, Zhou, Jie

原文摘要

In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discriminative frameworks. A recent trend in both domains is the use of adversarial information to enhance performance, revealing potential for synergy. However, the significant architectural divergence between them presents considerable challenges. Departing from previous approaches, we propose **UniGenDet**: a **Uni**fied generative-discriminative framework for co-evolutionary image **Gen**eration and generated image **Det**ection. To bridge the task gap, we design a symbiotic multimodal self-attention mechanism and a unified fine-tuning algorithm. This synergy allows the generation task to improve the interpretability of authenticity identification, while authenticity criteria guide the creation of higher-fidelity images. Furthermore, we introduce a detector-informed generative alignment mechanism to facilitate seamless information exchange. Extensive experiments on multiple datasets demonstrate that our method achieves state-of-the-art performance. Code: https://github.com/Zhangyr2022/UniGenDet.

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:18b9d43777264b769e905fd284bb3da0CVF · CVPR 2026

KAMP: Knowledge-Anchored Multimodal Pretraining Framework for Medical Image Representation

Huang, Feiyu, Li, Jia, Chen, Zhao, Wu, Yang, Cao, Caleb Chen, Chen, Lei

原文摘要

Cross-modal biomedical signals such as pathology and genomics can provide richer and more robust semantic guidance for medical image representation learning. However, the availability of such guidance remains limited, as privacy constraints and acquisition costs severely restrict access to medical images paired with other biomedical data. A further challenge lies in modality discrepancy, which introduces intra-modal statistical bias and cross-modal noise, thereby degrading the quality of medical image representations. To address these challenges, we propose KAMP, a large language model (LLM)-driven multimodal pretraining framework for medical image representation learning. KAMP leverages textual priors as the semantic anchor to enhance medical image representations and align them with multimodal biomedical representations, enabling the learning of rich and robust features even when paired data are scarce. KAMP operates in three stages. First, the LLM generates personalized diagnostic knowledge from patient clinical text and imaging metadata. This knowledge is injected as a prior to enrich medical image representations and serves as a semantic anchor to reduce the representation gap between medical images and other biomedical modalities. Second, the LLM is optimized using Group Relative Policy Optimization (GRPO), with the cross-modal aligner pretrained in the first stage serving as the reward model. Third, the refined knowledge is used to retrain the cross-modal aligner, yielding more robust medical image representations while mitigating bias and noise introduced by other modalities. Comprehensive evaluations on brain, bladder, and liver cancer datasets demonstrate that KAMP outperforms existing methods in most downstream few-shot classification tasks.

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:23d1536ec3259ce6550225b5e4701fe3CVF · CVPR 2026

Reading or Reasoning? Format Decoupled Reinforcement Learning for Document OCR

Zhong, Yufeng, Chen, Lei, Zeng, Zhixiong, Zhao, Xuanle, Jiang, Deyang, Zheng, Liming, Huang, Jing, Qiu, Haibo 等 11 位作者

原文摘要

Reading text from images or scanned documents via OCR models has been a longstanding focus of researchers. Intuitively, text reading is perceived as a straightforward perceptual task, and existing work primarily focuses on constructing enriched data engineering to enhance SFT capabilities. In this work, we observe that even advanced OCR models exhibit significantly higher entropy in formatted text (e.g., formula, table) compared to plain text, often by an order of magnitude. These statistical patterns reveal that advanced OCR models struggle with high output uncertainty when dealing with format sensitive document, suggesting that reasoning over diverse reading pathways may improve OCR performance. To address this, we propose Format Decoupled Reinforcement Learning (FD-RL) , which leverages high-entropy patterns for targeted optimization. Our approach employs entropy-based data filtration strategy to identify format-intensive instances, and adopt format decoupled rewards tailored to different format types, enabling format-level validation rather than token-level memorization. FD-RL achieves an average score of 90.41 on OmniDocBench, demonstrating highly competitive performance among end-to-end models on this highly popular benchmark. More importantly, we conduct comprehensive ablation studies over data, training, filtering, and rewarding strategies, thoroughly validating their effectiveness.

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:354b1c279e17725187cb712943558924CVF · CVPR 2026

URICA: A Uniformity Region Affine Identifier Capture Algorithm for Arbitrary Region Retrieval in Pathology Images

Su, Ri, Chen, Zhao, Cao, Caleb Chen, Chen, Lei

原文摘要

Whole slide image (WSI) region retrieval remains an open challenge in computational pathology, as existing methods struggle to represent and preserve information of all possible regions. Current approaches that rely on fixed-size patches or slide-level retrieval are misaligned with real clinical workflows, where pathologists often examine WSI regions of arbitrary orientations and sizes rather than predefined patches or slides. In this work, we redefine WSI retrieval as a semantically optimal matching problem between arbitrary regions under spatial transformations, which necessitates a region-level representation that maintains semantic consistency. To fulfill this requirement, we introduce semantic tessellation, which organizes patch units into flexible, geometry-aware region descriptors. Building on this representation, we develop the affine identifier, a semantic signature that enables rotation- and scale-consistent region matching. We further derive theoretical bounds between the tessellation-derived descriptors and the ideal pixel-level semantic mask objective, showing that they reliably approximate mask-based region similarity. Together, these components form URICA, a theoretically grounded algorithm for robust WSI region retrieval. Experiments on large public datasets demonstrate that URICA achieves strong and consistent performance across diverse WSI retrieval tasks.

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:834505b1dcef9b7af5ff2528e3671df0CVF · CVPR 2026

Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning

Li, Yifei, Zheng, Wenzhao, Zhang, Yanran, Sun, Runze, Zheng, Yu, Chen, Lei, Zhou, Jie, Lu, Jiwen

原文摘要

The misuse of AI-driven video generation technologies has raised serious social concerns, highlighting the urgent need for reliable AI-generated video detectors. However, most existing methods are limited to binary classification and lack the necessary explanations for human interpretation. In this paper, we present Skyra, a specialized multimodal large language model (MLLM) that identifies human-perceivable visual artifacts in AI-generated videos and leverages them as grounded evidence for both detection and explanation. To support this objective, we construct ViF-CoT-4K for Supervised Fine-Tuning (SFT), which represents the first large-scale AI-generated video artifact dataset with fine-grained human annotations.We then develop a two-stage training strategy that systematically enhances our model's spatio-temporal artifact perception, explanation capability, and detection accuracy. To comprehensively evaluate Skyra, we introduce ViF-Bench, a benchmark comprising 3K high-quality samples generated by over ten state-of-the-art video generators. Extensive experiments demonstrate that Skyra surpasses existing methods across multiple benchmarks, while our evaluation yields valuable insights for advancing explainable AI-generated video detection. Our code, models, and datasets will be made publicly available.

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:f374f00a07334b06c2b66e1674a872acCVF · CVPR 2026

IncreFA: Breaking the Static Wall of Generative Model Attribution

Qin, Haotian, Chang, Dongliang, Gao, Yueying, Tan, Yuexuan, Chen, Lei, Ma, Zhanyu

原文摘要

As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive generators appear almost monthly, making existing watermark, classifier and inversion methods obsolete upon release. The core problem lies not in model recognition, but in the inability to adapt attribution itself.We introduce IncreFA, a framework that redefines attribution as a structured incremental learning problem, allowing the system to learn continuously as new generative models emerge. IncreFA departs from conventional incremental learning by exploiting the hierarchical relationships among generative architectures and coupling them with continual adaptation. It integrates two mutually reinforcing mechanisms: (1) Hierarchical Constraints, which encode architectural hierarchies through learnable orthogonal priors to disentangle family-level invariants from model-specific idiosyncrasies; and (2) a Latent Memory Bank, which replays compact latent exemplars and mixes them to generate pseudo-unseen samples, stabilising representation drift and enhancing open-set awareness.On the newly constructed Incremental Attribution Benchmark (IABench) covering 28 generative models released between 2022 and 2025, IncreFA achieves state-of-the-art attribution accuracy and 98.9% unseen detection under a temporally ordered open-set protocol.

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 ↗