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新论文 · v1arXiv:2611.13496

Reasoning-Aware Relational Representation Learning for Open-Vocabulary Scene Graph Generation

Jiawei Xiao, Boya Wang, Longtian Qiu, Shan Ning, Xuming He

原文摘要

Open-Vocabulary Scene Graph Generation (OV-SGG) requires models to recognize visual relationships beyond the training vocabulary, yet existing methods often rely on dataset-specific object--predicate co-occurrence patterns. Under long-tailed distributions and noisy object localization, such reliance leads to biased, entangled relation representations, limiting both rare-relation recognition and generalization to unseen predicates. To address these challenges, we propose RaRe, a reasoning-aware relational representation learning framework that transforms MLLM-generated chain-of-thought (CoT) rationales into structured relation embeddings. Rather than using CoT only as an intermediate reasoning trace for final prediction, RaRe directly optimizes the rationale-derived representation space to improve inter-relation discriminability. Specifically, RaRe first aligns rationale embeddings with textualized relation descriptions through self-supervised contrastive learning, and then refines rationale generation with a specificity-oriented reinforcement learning objective that encourages semantically object-pair-specific relational evidence. Experiments on VG150 and PSG demonstrate improved category-balanced recognition over existing baselines, while the additional gains from learning vary across datasets and proposal settings. Code available at: https://artanic30.github.io/project_pages/RaRe.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22457

Erase the Mark, Preserve the Motion: Temporally Consistent Removal of Video Watermarks

Yifan Liao, Zhen Sun, Zhicong Huang, Jiaming He, Zongmin Zhang, Xi Ai, Cheng Hong, Xinhu Zheng 等 9 位作者

原文摘要

Recent advances in video generation have increased the demand for reliable watermarking for copyright protection, yet the robustness of video watermarks against removal attacks remains underexplored. Most watermark-removal methods originate from the image domain: signal optimization injects imperceptible pixel-domain perturbations, whereas generative resynthesis produces a visually similar instance through diffusion synthesis. When extended to videos, the former introduces temporally varying noise that causes visible flickering, while the latter regenerates fine-grained structures inconsistently across frames, leading to structural motion inconsistency. To evaluate video watermark robustness, we propose PoseRegen, a pose-consistent reconstruction framework that removes watermarks while preserving the original content and motion. PoseRegen first applies a temporally shared viewpoint transformation to disrupt watermark-bearing spatial patterns and then jointly reconstructs the transformed sequence with a pose-conditioned video model. A frozen video DiT processes the complete sequence with its reference-video latent, and full-sequence Flow Matching supervision keeps identity and background consistent across frames. To further preserve motion structure, we introduce a lightweight Pose-Conditioned Motion Consistency Adapter that adapts the frozen backbone to temporally ordered pose guidance and constrains body-part trajectories throughout reconstruction. Experiments on eleven video watermarking schemes show that PoseRegen reduces the average bit accuracy to 0.505, near random guessing, while outperforming the strongest baseline by 9.34% on motion-related VBench metrics. These results reveal a practical vulnerability of current video watermarks and highlight the need to evaluate robustness jointly with temporal and motion quality.

cs.CR · cs.CV

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新论文 · v1arXiv:2610.38346

Knowledge-structured and evidence-aware semantic self-distillation for glomerular pathology foundation model

Qiming He, Jing Li, Zihan Wang, Tian Guan, Yifei Ma, Zimo Zhao, Yanxia Wang, Hongjing Chen 等 27 位作者

原文摘要

Visual foundation models learn rich pathological morphology but lack explicit concept-level supervision. Vision--language pretraining can provide this supervision but requires costly image--text pairs. Here, we introduce GloSAGE, a semantic self-distillation framework that uses lesion profiles predicted by a pretrained visual teacher to construct structured pathology descriptions. Reliability-guided selection identifies image--text anchors, while Knowledge-Structured Pathology Text Synthesis and Evidence-Guided Negative-pair Construction account for partially observed lesions. Image--text alignment trains projection layers while retaining the frozen visual encoder; an intermediate Semantic Transfer Layer supplies features for downstream tasks. Developed on GloMillion and evaluated across seven glomerulus- and case-level cohorts, GloSAGE supported zero-shot lesion recognition, image--text retrieval, hard-negative discrimination and concept-intervention analyses. It outperformed its source visual model in most lesion-recognition tasks and improved lesion grading, while supporting competitive case-level disease classification, injury grading and baseline renal function estimation. Ablations supported the combined contributions of knowledge-structured text and evidence-guided negatives, and the intermediate transfer layer improved mean lesion-recognition ROC-AUC relative to the complete projection output. A frozen language model conditioned on GloSAGE features generated glomerular descriptions. These findings suggest that teacher-derived lesion profiles can provide annotation-efficient semantic supervision for extending a glomerular visual foundation model.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.03740

Multimodal Knowledge Aggregation for Microbial Keratitis: Geographic Confounding and Shortcut Risk in Pooled Multicountry Data

Yiqing Wang, Maria A. Woodward, Ziyun Yang, N. Venkatesh Prajna, Chunming He, Leslie M. Niziol, Mercy Pawar, Ming-Chen Lu 等 14 位作者

原文摘要

To develop an open-source multimodal model for bacterial-versus-fungal microbial keratitis classification and assess shortcuts from geographic pooling. Participants: 1,645 US and Indian patients contributed 17,158 slit-lamp images under blue-light, sclerotic-scatter, and white-light illumination. Methods: The model combined cross-modality contrastive learning, modality-specific fine-tuning, and fusion with 7 clinical variables. Diagnoses used smear, culture, and polymerase chain reaction results, with expert review of culture-negative cases. The model and 5 comparators used 5 patient-disjoint outer folds stratified by country and organism, with inner validation. Secondary analyses examined within-country performance, a country-only reference, ablations, and imbalance mitigation. Code: https://github.com/yqwang01/TPMKA. Main Outcome Measures: Patient-level macro-F1, class-specific F1, accuracy, and area under the receiver operating characteristic curve (AUROC), overall and by country. Results: The model had the highest overall macro-F1 (83.06%; 95% CI, 81.58-84.56) and accuracy (84.50%; 95% CI, 83.16-85.84) among matched comparators, with 89.9% AUROC (95% CI, 88.7-91.1). Paired differences were not consistently significant. AUROC was 83.9% in India and 67.0% in the US; bacterial F1 in India was 47.78%, and fungal F1 in the US was 40.97%. A country-only reference achieved 81.88% accuracy and 78.2% AUROC overall despite 50.0% AUROC within each country. Resampling and group distributionally robust optimization improved some underrepresented groups without removing the geographic pattern. Conclusions: The model compared favorably with matched methods, but geographic prevalence differences made pooled performance more reassuring than within-country discrimination. Independent geographic validation is needed.

q-bio.QM · cs.CV · eess.IV

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arXiv ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:070137c5e9cb9ba9c6f7e77f87aefe6cCVF · CVPR 2026

Stabilizing Streaming Video Geometry via Dynamic Feature Normalization

Lyu, Xiaoyang, Liu, Muxin, Wu, Xiaoshan, Wang, Ruicheng, Huang, Yi-Hua, Sun, Yang-Tian, Shi, Shaoshuai, Qi, Xiaojuan

原文摘要

Consistent 3D geometry estimation from streaming RGB input is crucial for real-world applications such as autonomous driving, embodied AI, and large-scale reconstruction. While modern monocular geometry foundation models achieve strong single-image accuracy, they exhibit severe temporal inconsistency on continuous input, notably dominated by scale-shift drifting. Through targeted empirical analysis, we trace this instability to its root cause: fluctuations in latent feature statistics, whose mean and variance directly determine the predicted depth's scale and shift. Building on this insight, we introduce Dynamic Feature Normalization (DyFN), a lightweight, causal recurrent module that dynamically and robustly modulates feature statistics to maintain stable geometry over time. We adapt powerful pretrained monocular geometry models for streaming by finetuning only DyFN, a mere 2% additional parameters, while keeping the backbone frozen, thereby achieving temporal consistency without compromising single-image accuracy. Extensive experiments across four benchmarks show that DyFN effectively eliminates temporal artifacts such as disjointed layering and positional jitter, and achieves state-of-the-art temporal stability, improving over prior streaming methods by up to 14% and even outperforming heavier non-causal video baselines. Project page: https://shawlyu.github.io/DyFN

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:7fba5b5592b6dc20b9779e056cea413bCVF · CVPR 2026

Feed-forward Gaussian Registration for Head Avatar Creation and Editing

Prinzler, Malte, Gotardo, Paulo, Tang, Siyu, Bolkart, Timo

原文摘要

We present MATCH (Multi-view Avatars from Topologically Corresponding Heads), a multi-view Gaussian registration method for high-quality head avatar creation and editing. State-of-the-art multi-view head avatars require time-consuming head tracking, which is followed by an expensive avatar optimization, often resulting in a total creation time that exceeds one day. MATCH instead directly predicts Gaussian splat textures in correspondence from calibrated multi-view images in 0.5 seconds per frame. While the learned intra-subject correspondence across frames allows us to quickly build personalized head avatars, correspondence across subjects enables various applications such as expression transfer, optimization-free tracking, semantic editing, and identity interpolation. We learn to establish such correspondences end-to-end, with a transformer-based model that predicts textures of Gaussian splats in the fixed UV layout of a template mesh. To this end, we introduce a novel registration-guided attention block, in which each UV map token attends exclusively to image tokens depicting its corresponding mesh region. MATCH outperforms existing methods for novel-view synthesis, geometry registration, and head avatar generation, the latter being 10xfaster than the qualitatively closest baseline. Code and model weights are available under https://malteprinzler.github.io/projects/match

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 ↗