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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e7ef0ebdb85c6c16cfb46f523d826b1eCVF · CVPR 2026

Globally Optimal Pose from Orthographic Silhouettes

Sengupta, Agniva, Kus, Dilara, Li, Jianning, Zachow, Stefan

原文摘要

We solve the problem of determining the pose of known shapes in \mathbb R ^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches.

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:e8062fd20f01c09ae754dfd83e4714fcCVF · CVPR 2026

Interpretable Debiasing of Vision-Language Models for Social Fairness

An, Na Min, Jang, Yoonna, Hirota, Yusuke, Hachiuma, Ryo, Augenstein, Isabelle, Shim, Hyunjung

原文摘要

The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended forms of social bias. Current debiasing approaches focus on mitigating surface-level bias signals through post-hoc learning or test-time algorithms, while leaving the internal dynamics of the model largely unexplored. In this work, we introduce an interpretable, model-agnostic bias mitigation framework, DeBiasLens, that localizes social attribute neurons in VLMs through sparse autoencoders (SAEs) applied to multimodal encoders. Building upon the disentanglement ability of SAEs, we train them on facial image or caption datasets without corresponding social attribute labels to uncover neurons highly responsive to specific demographics, including those that are underrepresented. By selectively deactivating the social neurons most strongly tied to bias for each group, we effectively mitigate socially biased behaviors of VLMs without degrading their semantic knowledge. Our research lays the groundwork for future auditing tools, prioritizing social fairness in emerging real-world AI systems.

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:e80ff98e8bdea1ec1b34d6ca97b62c07CVF · CVPR 2026

Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure

Yun, Jooyeol, Choo, Jaegul

原文摘要

Scalable Vector Graphics (SVG) are central to modern web design, and the demand to animate them continues to grow as web environments become increasingly dynamic.Yet automating the animation of vector graphics remains challenging for vision-language models (VLMs) despite recent progress in code generation and motion planning.VLMs routinely mis-handle SVGs, since visually coherent parts are often fragmented into low-level shapes that offer little guidance of which elements should move together. In this paper, we introduce a framework that recovers the semantic structure required for reliable SVG animation and reveals the missing layer that current VLM systems overlook. This is achieved through a statistical aggregation of multiple weak part predictions, allowing the system to stably infer semantics from noisy predictions.By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence. Our experiments demonstrate substantial gains over existing approaches, suggesting that semantic recovery is the key step that unlocks robust SVG animation and supports more interpretable interactions between VLMs and vector graphics.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e821d76d9e8b60ba15c702dab17fe840CVF · CVPR 2026

Boosting Vision-Language Models Towards Cross-Domain Incremental Object Detection

Wang, Xu, Lin, Zihan, Zhang, Yixin, Wang, Zilei

原文摘要

Incremental Object Detection (IOD) aims to equip detectors with the ability to handle dynamic environments and emerging object categories, and the rise of vision-language models has substantially advanced this goal. However, existing studies often oversimplify real-world scenarios by assuming the incremental tasks come from a single general domain. To better investigate vision-language models under IOD, it is necessary to explore more generalized scenarios that encompass both novel categories and domains. To this end, we propose Cross-Domain Incremental Object Detection (CDIOD), a new benchmark that assesses the ability to continuously adapt to diverse object detection tasks across domains. CDIOD reveals that existing methods struggle to balance between adaptivity and stability under substantial domain shifts. To tackle this challenge, we propose Dynamic Group Subspace (DGS), a novel framework that dynamically groups tasks by distribution to promote knowledge sharing and prevent task collisions; progressively consolidates adapters to build shared subspaces and control parameter growth; and implements a dynamic training pipeline to maintain a proper stability-adaptivity balance. DGS enables vision-language models to effectively handle task streams of various distribution shifts. Extensive experiments across three benchmarks demonstrate that DGS achieves SOTA performance, highlighting its robustness in diverse incremental learning scenarios. Code is available at https://github.com/Never-wx/dgs.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e826424b8a022b239f6bcdc8a5256fc5CVF · CVPR 2026

Mesh4D: 4D Mesh Reconstruction and Tracking from Monocular Video

Jiang, Zeren, Zheng, Chuanxia, Laina, Iro, Larlus, Diane, Vedaldi, Andrea

原文摘要

We propose Mesh4D, a feed-forward model for monocular 4D mesh reconstruction. Given a monocular video of a dynamic object, our model reconstructs the object's complete 3D shape and motion, represented as a deformation field. Our key contribution is a compact latent space that encodes the entire animation sequence. This latent space is learned by an autoencoder that, during training, is guided by the skeletal structure of the training objects, providing strong priors on plausible deformations. Crucially, skeletal information is not required at inference time. The encoder employs spatio-temporal attention, yielding a more stable representation of the object's overall deformation. Building on this representation, we train a latent diffusion model that, conditioned on the input video and the mesh reconstructed from the first frame, predicts the full animation in one shot. We evaluate Mesh4D on reconstruction and novel view synthesis benchmarks, outperforming prior methods in recovering accurate 3D shape and deformation.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e82f9ff48f2d30c640c1e51295dcb239CVF · CVPR 2026

VKG-QA: Visual Knowledge Graph-based Question Answer for Large Multimodal Models

Du, Yuntao, Wang, Yiming, Yuan, Renshuo, Yue, Jincheng, Chen, Yijing, Fan, Yue, Zhang, Bo, Li, Qian 等 9 位作者

原文摘要

Understanding and reasoning over structured knowledge is a fundamental capability for intelligent systems. While Large Language Models (LLMs) have leveraged textual knowledge graphs for relational reasoning, linearizing graph structures into text often leads to token inefficiency and loss of higher-order relational cues. Inspired by the advances of Large Multimodal Model to capture higher-order relational structures explicitly novel paradigm of visualized knowledge representation, where knowledge graphs are transformed into graphical visualizations that LMMs can directly perceive and reason over. To systematically evaluate this capability, we introduce VKG-QA, a benchmark for Visual Knowledge Graph-based Question Answering, covering three major categories and fourteen subtasks. VKG-QA is constructed via a semi-automatic pipeline ensuring high-quality, semantically aligned, and visually clear data. We evaluate 19 representative LMMs on VKG-QA and perform extensive quantitative and qualitative analyses. Results reveal that current models struggle with visualized relational understanding, graph-specific comprehension remains challenging, and closed-source models significantly outperform open-source counterparts. VKG-QA thus highlights critical limitations in current LMMs and provides a scalable platform for advancing graph-aware visual reasoning.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e85331dbe904bd4ae5b6cd0b4d82cec5CVF · CVPR 2026

TimeLens: Rethinking Video Temporal Grounding with Multimodal LLMs

Zhang, Jun, Wang, Teng, Ge, Yuying, Ge, Yixiao, Li, Xinhao, Wang, Limin

原文摘要

This paper does not introduce a novel method but instead establishes a straightforward, incremental, yet essential baseline for video temporal grounding (VTG), a core capability in video understanding. While multimodal large language models (MLLMs) excel at various video understanding tasks, the recipes for optimizing them for VTG remain under-explored. In this paper, we present TimeLens, a systematic investigation into building MLLMs with strong VTG ability, along two primary dimensions: data quality and algorithmic design. We first expose critical quality issues in existing VTG benchmarks and introduce TimeLens-Bench, comprising meticulously re-annotated versions of three popular benchmarks with strict quality criteria. Our analysis reveals dramatic model re-rankings compared to legacy benchmarks, confirming the unreliability of prior evaluation standards. We also address noisy training data through an automated re-annotation pipeline, yielding TimeLens-100K, a large-scale, high-quality training dataset. Building on our data foundation, we conduct in-depth explorations of algorithmic design principles, yielding a series of meaningful insights and effective yet efficient practices. These include interleaved textual encoding for time representation, a thinking-free reinforcement learning with verifiable rewards (RLVR) approach as the training paradigm, and carefully designed recipes for RLVR training. These efforts culminate in TimeLens models, a family of MLLMs with state-of-the-art VTG performance among open-source models and even surpass proprietary models such as GPT-5 and Gemini-2.5-Flash. All codes, data, and models will be released to facilitate future research.

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:e86ac5e68cf3ef1d8f9086b77219c2beCVF · CVPR 2026

Generalizing Visual Geometry Priors to Sparse Gaussian Occupancy Prediction

Zhou, Changqing, Luo, Yueru, Chen, Changhao

原文摘要

Accurate 3D scene understanding is essential for embodied intelligence, with occupancy prediction emerging as a key task for reasoning about both objects and free space. Existing approaches largely rely on depth priors (e.g., DepthAnything) but make only limited use of 3D cues, restricting performance and generalization. Recently, visual geometry models such as VGGT have shown strong capability in providing rich 3D priors, but similar to monocular depth foundation models, they still operate at the level of visible surfaces rather than volumetric interiors, motivating us to explore how to more effectively leverage these increasingly powerful geometry priors for 3D occupancy prediction. We present GPOcc, a framework that leverages generalizable visual geometry priors (GPs) for monocular occupancy prediction. Our method extends surface points inward along camera rays to generate volumetric samples, which are represented as Gaussian primitives for probabilistic occupancy inference. To handle streaming input, we further design a training-free incremental update strategy that fuses per-frame Gaussians into a unified global representation. Experiments on Occ-ScanNet and EmbodiedOcc-ScanNet demonstrate significant gains: GPOcc improves mIoU by +9.99 in the monocular setting and +11.79 in the streaming setting over prior state of the art. Under the same depth prior, it achieves +6.73 mIoU while running 2.65 times faster. These results highlight that GPOcc leverages geometry priors more effectively and efficiently. Code will be released at https://github.com/JuIvyy/GPOcc.

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:e86cbaafe0543b95e728f972c9b15330CVF · CVPR 2026

InterPhys: Physics-aware Human Motion Synthesis in a Dynamic Scene

Xing, Chaoyue, Mao, Wei, Liu, Miaomiao

原文摘要

This paper tackles the problem of physics-aware human motion synthesis in a dynamic scene. Unlike existing works which mainly tend to generate physically unrealistic motions due to limited contact modeling, typically restricted to hands, in this paper, we introduce a physics-aware human motion generation framework that explicitly models the full spectrum of human-related forces, including human-object, human-scene, and internal body dynamics. Our method imposes soft physical constraints to maintain force and torque balance, ensuring physically grounded motion synthesis. We further propose a novel continuous distance-based force model that generalizes contact modeling to arbitrary surfaces, capturing interactions not only with static environments but also with dynamic, moving objects. Extensive experiments show that our approach significantly improves physical plausibility and generalizes well to complex scenes, setting a new benchmark for physically consistent human motion generation.

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:e876886f2d389fbc34489fd4a20432d6CVF · CVPR 2026

Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning

Choi, Seung hee, Jeon, MinJu, Oh, Hyunwoo, Lee, Jihwan, Kim, Dong-Jin

原文摘要

Existing retrieval-augmented approaches for Dense Video Captioning (DVC) often fail to achieve accurate temporal segmentation aligned with true event boundaries, as they rely on heuristic strategies that overlook ground truth event boundaries.The proposed framework, STaRC, overcomes this limitation by supervising frame-level saliency through a highlight detection module. Note that the highlight detection module is trained on binary labels derived directly from DVC ground truth annotations without the need for additional annotation.We also propose to utilize the saliency scoresas a unified temporal signal that drives retrieval via saliency-guided segmentation and informs caption generation through explicit Saliency Prompts injected into the decoder.By enforcing saliency-constrained segmentation, our method produces temporally coherent segments that align closely with actual event transitions, leading to more accurate retrieval and contextually grounded caption generation. We conduct comprehensive evaluations on the YouCook2 and ViTT benchmarks, where STaRC achieves state-of-the-art performance across most of the metrics.

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:e876bff0950b82a0c9b7c3fd038e58d6CVF · CVPR 2026

AdapAction: Adaptive Target Action Backdoor Attack against GUI Agents

Chen, Baicheng, Zhang, Mingda, Zhang, Min, Li, Haizhou, Wu, Baoyuan

原文摘要

Autonomous Graphical User Interface (GUI) agents powered by Multimodal Large Language Models (MLLMs) are increasingly vital for complex task automation. However, their capacity for self-driven decision-making introduces significant, yet underexplored, security risks, among which backdoor attacks pose a particularly stealthy and high-impact threat. Prior work has shown GUI agents vulnerable to such attacks, but existing methods rely on static trigger-action mappings that execute fixed, context-agnostic behaviors, making them highly detectable. To address this limitation, we introduce **AdapAction**, a novel backdoor attack that subverts the agent's decision-making by embedding an **adaptive, context-aware policy**. Unlike traditional approaches, AdapAction enables the agent to autonomously select environmentally coherent malicious actions based on the current GUI state and user instruction, thereby evading detection while preserving functional utility. Extensive experiments on the Android-In-The-Zoo (AitZ) and AndroidControl benchmarks show that AdapAction achieves up to 100% Attack Success Rate (ASR) while preserving benign task utility. More critically, AdapAction consistently evades a multi-principle-based LLM defense evaluating instruction alignment, visual coherence, and safety, whereas traditional fixed-action attacks are easily detected. This resilience stems from AdapAction's contextually grounded malicious actions, which are semantically and visually indistinguishable from legitimate operations. As a result, AdapAction exhibits exceptional stealth and poses a significantly greater real-world threat to LLM-powered GUI agents.

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:e87ef338d71addd8ce74224c460664a6CVF · CVPR 2026

Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator

Moon, Gyeongsik

原文摘要

Accurately recovering hand poses within the body context remains a major challenge in 3D whole-body pose estimation. This difficulty arises from a fundamental supervision gap: whole-body pose estimators are trained on full-body datasets with limited hand diversity, while hand-only estimators, trained on hand-centric datasets, excel at detailed finger articulation but lack global body awareness. To address this, we propose Hand4Whole++, a modular framework that leverages the strengths of both pre-trained whole-body and hand pose estimators. We introduce CHAM (Conditional Hands Modulator), a lightweight module that modulates the whole-body feature stream using hand-specific features extracted from a pre-trained hand pose estimator. This modulation enables the whole-body model to predict wrist orientations that are both accurate and coherent with the upper-body kinematic structure, without retraining the full-body model. In parallel, we directly incorporate finger articulations and hand shapes predicted by the hand pose estimator, aligning them to the full-body mesh via differentiable rigid alignment. This design allows Hand4Whole++ to combine globally consistent body reasoning with fine-grained hand detail. Extensive experiments demonstrate that Hand4Whole++ substantially improves hand accuracy and enhances overall full-body pose quality.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e880c47761afa65ae1f155c84867374dCVF · CVPR 2026

Fine-Grained Post-Training Quantization for Large Vision Language Models with Quantization-Aware Integrated Gradients

Xiang, Ziwei, Zeng, Fanhu, Fang, Hongjian, Wang, Rui-Qi, Chen, Renxing, Zhu, Yanan, Chen, Yi, Yang, Peipei 等 9 位作者

原文摘要

Large Vision Language Models (LVLMs) have achieved remarkable success in a wide range of downstream tasks that require multimodal interaction, but their powerful capabilities come with substantial computational and memory overhead, which hinders practical deployment. Among numerous acceleration techniques, post-training quantization is a popular and effective strategy for reducing memory cost and accelerating inference. However, existing LVLM quantization methods typically measure token sensitivity at the modality level, which fails to capture the rich and complex cross-token interactions and falls short in quantitatively measuring the quantization error at the token level. As tokens interact within the model, the distinction between modalities gradually diminishes, suggesting the need for fine-grained calibration. Inspired by axiomatic attribution in mechanistic interpretability, we introduce a fine-grained quantization strategy based on Quantization-aware Integrated Gradients (QIG), which leverages integrated gradients to quantitatively evaluate token sensitivity and push the granularity from modality level to token level, reflecting both inter-modality and intra-modality dynamics. Extensive experiments on multiple LVLMs under both W4A8 and W3A16 settings show that our method consistently improves accuracy across models and benchmarks with negligible latency overhead. For example, under 3-bit weight-only quantization, our method improves the average accuracy of LLaVA-onevision-7B by 1.60%, reducing the gap to its full-precision counterpart to only 1.33%. The code is available at https://github.com/ucas-xiang/QIG.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e89748964cca09daef94d0598d46d483CVF · CVPR 2026

NEC-Diff: Noise-Robust Event-RAW Complementary Diffusion for Seeing Motion in Extreme Darkness

Liu, Haoyue, Xu, Jinghan, Feng, Luxin, Zhou, Hanyu, Zhao, Haozhi, Chang, Yi, Yan, Luxin

原文摘要

High-quality imaging of dynamic scenes in extremely low-light conditions is highly challenging. Photon scarcity induces severe noise and texture loss, causing significant image degradation. Event cameras, featuring a high dynamic range (120 dB) and high sensitivity to motion, serve as powerful complements to conventional cameras by offering crucial cues for preserving subtle textures. However, most existing approaches emphasize texture recovery from events, while paying little attention to image noise or the intrinsic noise of events themselves, which ultimately hinders accurate pixel reconstruction under photon-starved conditions. In this work, we propose NEC-Diff, a novel diffusion-based event-RAW hybrid imaging framework that extracts reliable information from heavily noisy signals to reconstruct fine scene structures. The framework is driven by two key insights: (1) combining the linear light-response property of RAW images with the brightness-change nature of events to establish a physics-driven constraint for robust dual-modal denoising; and (2) dynamically estimating the SNR of both modalities based on denoising results to guide adaptive feature fusion, thereby injecting reliable cues into the diffusion process for high-fidelity visual reconstruction. Furthermore, we construct the REAL (Raw and Event Acquired in Low-light) dataset which provides 47,800 pixel-aligned low-light RAW images, events, and high-quality references under 0.001-0.8 lux illumination. Extensive experiments demonstrate the superiority of NEC-Diff under extreme darkness.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e8a69f7a084072dc383a27b76813a444CVF · CVPR 2026

Meta-CoT: Enhancing Granularity and Generalization in Image Editing

Zhang, Shiyi, Cheng, Yiji, Hang, Tiankai, Yin, Zijin, He, Runze, Xu, Yu, Dai, Wenxun, Lin, Yunlong 等 11 位作者

原文摘要

Unified multi-modal understanding/generative models have shown improved image editing performance by incorporating fine-grained understanding into their Chain-of-Thought (CoT) process. However, a critical question remains underexplored: what forms of CoT and training strategy can jointly enhance both the understanding granularity and generalization? To address this, we propose Meta-CoT, a paradigm that performs a two-level decomposition of any single-image editing operation with two key properties: (1) Decomposability. We observe that any editing intention can be represented as a triplet -- (task, target, required understanding ability). Inspired by this, Meta-CoT decomposes both the editing task and the target, generating task-specific CoT and traversing editing operations on all targets. This decomposition enhances the model's understanding granularity of editing operations and guides it to learn each element of the triplet during training, substantially improving the editing capability.(2) Generalizability. In the second decomposition level, we further break down editing tasks into five fundamental meta-tasks. We find that training on these five meta-tasks, together with the other two elements of the triplet, is sufficient to achieve strong generalization across diverse, unseen editing tasks.To further align the model's editing behavior with its CoT reasoning, we introduce the CoT-Editing Consistency Reward, which encourages more accurate and effective utilization of CoT information during editing. Experiments demonstrate that our method achieves an overall 15.8% improvement across 21 editing tasks, and generalizes effectively to unseen editing tasks when trained on only a small set of meta-tasks. Our code, benchmark, and model will be released publicly.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e8b51f72a91fb7522f478886d343fba6CVF · CVPR 2026

No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation

Xiong, Lizhi, Li, Jun, Li, Ziqiang, Jiang, Weiwei, Fu, Zhangjie

原文摘要

Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avatars and virtual try-on systems. However, their reliance on sensitive facial reference images raises growing privacy concerns. Existing defense mechanisms are primarily designed for training-based personalization and struggle to generalize to emerging training-free approaches, due to fundamental differences in their identity integration paradigms. To bridge this gap, we propose IDGuardian--the first generalizable and model-agnostic identity protection framework capable of defending against both training-based and training-free personalization methods. IDGuardian abstracts the personalization process into two critical stages: identity extraction and identity injection. It then introduces crafted adversarial perturbations to simultaneously disrupt both stages. Specifically, it degrades the identity features extracted by external encoders and establishes an adversarial conceptual bridge that misdirects the generative trajectory away from the target identity. Extensive experiments show that IDGuardian effectively protects identity across various personalization pipelines and model architectures, while remaining robust to post-processing, adaptive attacks, and cross-dataset generalization.

cs.CV

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

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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e8b80dd9f22381e3c7e1a611b1377ef1CVF · CVPR 2026

iSHIFT: Lightweight Slow-Fast GUI Agent with Adaptive Perception

Mehrotra, Sarthak, Rebbapragada, Sairam VC, Bonthu, Mani, Balasubramanian, Vineeth N.

原文摘要

Multimodal Large Language Models (MLLMs) show strong potential for interpreting and interacting with complex, pixel-rich Graphical User Interface (GUI) environments. However, building agents that are both efficient for high-level tasks and precise for fine-grained interactions remains challenging. GUI agents must perform routine actions efficiently while also handling tasks that demand exact visual grounding, yet existing approaches struggle when accuracy depends on identifying specific interface elements. These MLLMs also remain large and cannot adapt their reasoning depth to the task at hand. In this work, we introduce iSHIFT: Implicit Slow-fast Hybrid Inference with Flexible Tokens, a lightweight agent that integrates latent thinking (implicit chain-of-thought) with a perception control module. iSHIFT enables an MLLM to switch between a slow mode, which leverages detailed visual grounding for high precision and a fast mode that uses global cues for efficiency. Special perception tokens guide attention to relevant screen regions, allowing the model to decide both how to reason and where to focus. Despite its compact 2.5B size, iSHIFT matches state-of-the-art performance on multiple benchmark datasets.

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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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e8c42f13eefe236629e1e2714c9d63f9CVF · CVPR 2026

Query2Uncertainty: Robust Uncertainty Quantification and Calibration for 3D Object Detection under Distribution Shift

Beemelmanns, Till, Nekrasov, Alexey, Vilceanu, Stefan, Steinhaus, Jonas, Woopen, Timo, Leibe, Bastian, Eckstein, Lutz

原文摘要

Reliable uncertainty estimation for 3D object detection is critical for deploying safe autonomous systems, yet modern detectors remain poorly calibrated, especially under distribution shifts. Although post-hoc calibration methods address this issue and provide improved calibration for in-distribution tests, they fail to adapt in distribution-shifted scenarios. In this work, we address this issue and introduce a density-aware calibration method that couples post-hoc calibrators with the feature density of latent object queries from DETR-style 3D object detectors. These queries form a compact, location and class-aware feature, ideal for density estimation, allowing our approach to adjust model confidences in distribution-shift scenarios. By fitting a density estimator on these query features, our approach jointly recalibrates both classification and bounding box regression uncertainties. On both a multi-view camera and LiDAR-based detector, our approach consistently outperforms standard post-hoc methods in both in-distribution and distribution-shifted scenarios. Code: https://tillbeemelmanns.github.io/query2uncertainty

cs.CV

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

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Generalized and Personalized Federated Learning with Black-Box Foundation Models via Orthogonal Transformations

Kong, Eun Gyung, Yeom, Jewon, Jeon, Yonghoon, Kim, Taesup

原文摘要

Federated Learning (FL) facilitates decentralized model training while preserving data privacy. However, achieving both robust generalization and effective personalization simultaneously in heterogeneous (non-IID) environments remains a formidable challenge. Furthermore, the widespread adoption of proprietary Foundation Models (FMs) introduces a critical requirement for dual privacy: (a) protecting sensitive client data and (b) securing the server's valuable intellectual property. This mandates strictly black-box access to the FM. To address these multifaceted challenges, we introduce FedOT, a novel FL framework optimized for black-box FMs. FedOT employs a shared global task-dependent classifier while facilitating local adaptation through client-specific orthogonal transformations applied externally to the FM embeddings. This architecture inherently guarantees that the FM's internal parameters remain inaccessible and unmodified. By enforcing orthogonality, FedOT effectively mitigates gradient conflicts across diverse clients, which is theoretically bounded, preserves the semantic integrity of the FM representations, and achieves robust performance under significant data heterogeneity. The synergy of global and local parameters optimally balances generalization and personalization, markedly outperforming baseline FL methods across diverse benchmarks. Extensive empirical analysis, including rigorous multi-seed validation and scalability assessments, substantiates the robustness, efficiency, and superior performance of FedOT.

cs.CV

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

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VLM-PTQ: Efficient Post-Training Quantization for Large Vision-Language Models

Deng, Juncan, Huang, Kejie

原文摘要

Post-training quantization (PTQ) serves as a vital technique for efficiently compressing large-scale models, with weight-compensation methods such as GPTQ (symmetric calibration) and GPTAQ (asymmetric calibration) showing remarkable success. However, directly applying these methods to Vision-Language Models (VLMs) exposes two notable shortcomings: 1) the standard rounding-to-nearest (RTN) method is suboptimal for the asymmetric objective, failing to account for residual-induced shifts in the optimal quantization target; and 2) all input channels are processed uniformly across modalities, overlooking the distinct information densities. In this paper, we introduce VLM-PTQ, an asymmetric post-training quantization framework for VLMs. First, we derive a closed-form correction term that shifts the quantization target, which explicitly accounts for the output residual and the corresponding inverse Hessian column, yielding a better local optimum than RTN. Second, we propose a modality-aware quantization that differentiates channel importance between vision and language tokens, allowing the quantizer to pre-compute better quantization parameters through a lightweight search. Our method extends weight-compensation methods with minimal overhead while achieving significant performance improvements in low-bit scenarios. Extensive experiments demonstrate that VLM-PTQ achieves competitive results compared to existing methods, effectively compressing models from 1B to 72B parameters on a single GPU.

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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Rank-Guided Pseudo-Bias Learning for Robust Black-Box Adaptation

Dwivedi, Rajeev Ranjan, Dangwal, Anshuman, Kurmi, Vinod K

原文摘要

Pretrained vision encoders are widely used as frozen, black-box feature extractors, yet they often inherit spurious correlations that disproportionately harm underrepresented groups. We introduce PLD-Debias, a fully black-box debiasing framework that requires neither access to backbone parameters nor demographic annotations. Our method integrates three components: (1) Rank-Regularized Amplification, a lightweight adapter that exaggerates latent spurious directions; (2) Unsupervised Pseudo-Bias Induction, which clusters amplified features to infer high-fidelity proxy bias labels; and (3) Bias-Guided Refinement, combining supervised contrastive alignment with cluster-aware adaptive margins to purify representations and equalize decision boundaries. We theoretically show that these components jointly tighten a worst-group risk bound under spurious correlations. Empirically, PLD-Debias achieves state-of-the-art worst-group accuracy across CelebA, Waterbirds, and CMNIST, improving performance by 3-5 points over prior black-box methods while maintaining average accuracy. Remarkably, our pseudo-bias labels align with ground-truth bias annotations at over 90% fidelity, enabling oracle-level robustness without demographic supervision. Our results demonstrate that fairness and utility can be achieved through a plug-and-play classifier adapter for any frozen foundation model.

cs.CV

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

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M4V: Multimodal Mamba for Efficient Text-to-Video Generation

Huang, Jiancheng, Zhang, Gengwei, Jie, Zequn, Jiao, Siyu, Qian, Yinlong, Chen, Ling, Wei, Yunchao, Ma, Lin

原文摘要

Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators. However, modeling the vast spatiotemporal space remains computationally demanding, particularly when employing Transformers, which incur quadratic complexity in sequence processing and thus limit practical applications. Recent advancements in linear-time sequence modeling, particularly the Mamba architecture, offer a more efficient alternative. Nevertheless, its plain design limits its direct applicability to multimodal and spatiotemporal video generation tasks. To address these challenges, we introduce M4V, a multimodal Mamba framework for efficient text-to-video generation. Specifically, a MultiModal diffusion Mamba (MM-DiM) block is designed within the framework to enable seamless integration of multimodal information and spatiotemporal modeling. In detail, we introduce a novel multimodal token re-composition design, which employs a bidirectional scheme for multimodal information integration through simple token arrangement, along with visual registers to enhance spatial-temporal consistency. As a result, the MM-DiM blocks in M4V reduce FLOPs by 45% compared with the attention-based alternative when generating videos at 768x1280 resolution. Additionally, several training strategies are explored in this work to provide a better understanding of training text-to-video models using only publicly available datasets. Extensive experiments on text-to-video benchmarks demonstrate M4V's ability to produce high-quality videos while significantly lowering computational costs. Code will be made publicly available.

cs.CV

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

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MODIX: A Training-Free Multimodal Information-Driven Positional Index Scaling for Vision-Language Models

Huang, Ruoxiang, Yuan, Zhen

原文摘要

Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet their positional encoding mechanisms remain suboptimal. Existing approaches uniformly assign positional indices to all tokens, overlooking variations in information density within and across modalities, which leads to inefficient attention allocation where redundant visual regions dominate while informative content is underrepresented. We identify positional granularity as an implicit resource and propose MODIX (Multimodal Information-Driven Positional IndeX Scaling), a training-free framework that dynamically adapts positional strides based on modality-specific contributions. MODIX jointly models intra-modal density via covariance-based entropy and inter-modal interaction via cross-modal alignment to derive unified scores, which rescale positional indices to allocate finer granularity to informative modalities while compressing redundant ones, without requiring any modification to model parameters or architecture. Experiments across diverse architectures and benchmarks demonstrate that MODIX consistently improves multimodal reasoning and adaptively reallocates attention according to task-dependent information distributions, suggesting that positional encoding should be treated as an adaptive resource in Transformers for multimodal sequence modeling.

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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ParaUni: Enhance Generation in Unified Multimodal Model with Reinforcement-driven Hierarchical Parallel Information Interaction

Tan, Jiangtong, Liu, Lin, Huang, Jie, Zhang, Xiaopeng, Tian, Qi, Zhao, Feng

原文摘要

Unified multimodal models significantly improve visual generation by combining vision-language models (VLMs) with diffusion models. However, existing methods struggle to fully balance sufficient interaction and flexible implementation due to vast representation difference. Considering abundant and hierarchical information in VLM's layers from low-level details to high-level semantics, we propose ParaUni. It extracts features from variants VLM's layers in a Parallel way for comprehensive information interaction and retains a flexible separation architecture to enhance generation in Unified multimodal model. Concretely, visual features from all VLM's layers are fed in parallel into a Layer Integration Module (LIM), which efficiently integrates fine-grained details and semantic abstractions and provides the fused representation as a condition to the diffusion model. To further enhance performance, we reveal that these hierarchical layers respond unequally to different rewards in Reinforcement Learning (RL). Crucially, we design a Layer-wise Dynamic Adjustment Mechanism (LDAM) to facilitate multiple reward improvements that aligns the hierarchical properties of these layers using RL. Extensive experiments show ParaUni leverages complementary multi-layer features to substantially improve generation quality and shows strong potential for multiple reward advances during RL stages.

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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Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints

Li, Chenxi, Liu, Xianggan, Shen, Dake, Du, Yaosong, Yao, Zhibo, Jiang, Hao, Jiang, Linyi, Cao, Chengwei 等 12 位作者

原文摘要

Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, where LVLMs complete missing slot values with unsafe content even when the slot types are deliberately crafted to appear benign. Building on this finding, we propose StructAttack, a simple yet effective single-query jailbreak framework under black-box settings. StructAttack decomposes a harmful query into a central topic and a set of benign-looking slot types, then embeds them as structured visual prompts (e.g., mind maps, tables, or sunburst diagrams) with small random perturbations. Paired with a completion-guided instruction, LVLMs automatically recompose the concealed semantics and generate unsafe outputs without triggering safety mechanisms. Although each slot appears benign in isolation (local benignness), StructAttack exploits LVLMs' reasoning to assemble these slots into coherent harmful semantics. Extensive experiments on multiple models and benchmarks show the efficacy of our proposed StructAttack.

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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IDperturb: Enhancing Variation in Synthetic Face Generation via Angular Perturbations

Boutros, Fadi, Caldeira, Eduarda, Chettaoui, Tahar, Damer, Naser

原文摘要

Synthetic data has emerged as a practical alternative to authentic face datasets for training face recognition (FR) systems, especially as privacy and legal concerns increasingly restrict the use of real biometric data. Recent advances in identity-conditional diffusion models have enabled the generation of photorealistic and identity-consistent face images. However, many of these models suffer from limited intra-class variation, an essential property for training robust and generalizable FR models. In this work, we propose IDPERTURB, a simple yet effective geometric-driven sampling strategy to enhance diversity in synthetic face generation. IDPERTURB perturbs identity embeddings within a constrained angular region of the unit hyper-sphere, producing a diverse set of embeddings without modifying the underlying generative model. Each perturbed embedding serves as a conditioning vector for a pre-trained diffusion model, enabling the synthesis of visually varied yet identity-coherent face images suitable for training generalizable FR systems. Empirical results demonstrate that training FR on datasets generated using IDPERTURB yields improved performance across multiple FR benchmarks, compared to existing synthetic data generation approaches. Code and generated datasets are publicly available https://github.com/fdbtrs/IDperturb.

cs.CV

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

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Hugging Visual Prompt and Segmentation Tokens: Consistency Learning for Fine-Grained Visual Understanding in MLLMs

Yang, Jing, Yang, Sen, Duan, Boqiang, Dai, Ming, Zhang, Wei, Tan, Xiao, Chen, Kunbin, He, Wei 等 10 位作者

原文摘要

Recently, multimodal large language models (MLLMs) have achieved remarkable success in general multimodal tasks. Increasing attention has been given to leveraging MLLMs for fine-grained visual understanding, such as region-level captioning and pixel-level grounding. However, most existing approaches are task-specific. While some recent unified approaches attempt to handle both types simultaneously, they still fall short of deeply exploring the underlying associations across tasks. To bridge this gap, we propose FCLM, a large multimodal model designed to jointly support fine-grained visual understanding through consistency learning. The central premise is that pixel-level captioning and grounding are mutually beneficial and complementary, each enhancing the other in achieving a fine-grained understanding of visual content. Specifically, FCLM analyzes the representation features - visual prompt and segmentation tokens - required for the two types of visual tasks, and achieves advanced reasoning and perception through a newly-designed consistency learning loss and a two-stage training framework. Moreover, a hybrid region extractor is designed to enhance visual prompt embeddings, yielding semantically discriminative representations for detailed captioning. Furthermore, a novel detailed localized referring expression segmentation (DL-RES) task is introduced to evaluate the model's ability to localize targets from detailed textual descriptions. Extensive experiments on seven visual understanding tasks demonstrate the superior performance and strong generalization of FCLM.

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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GGPT: Geometry-Grounded Point Transformer

Chen, Yutong, Wang, Yiming, Zhang, Xucong, Prokudin, Sergey, Tang, Siyu

原文摘要

Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer from geometric inconsistencies and limited fine-grained accuracy due to the absence of explicit multi-view constraints. We introduce the Geometry-Grounded Point Transformer (GGPT), a framework that augments feed-forward reconstruction with reliable sparse geometric guidance. We first propose an improved Structure-from-Motion pipeline based on dense feature matching and lightweight geometric optimisation to efficiently estimate accurate camera poses and partial 3D point clouds from sparse input views. We propose a geometry-guided 3D point transformer that refines dense point maps under explicit partial-geometry supervision using an optimised guidance encoding. Extensive experiments demonstrate that our method provides a principled mechanism for integrating geometric priors with dense feed-forward predictions, producing reconstructions that are both geometrically consistent and spatially complete, recovering fine structures and filling gaps in textureless areas.Trained solely on ScanNet++ with VGGT predictions, GGPT generalises across architectures and datasets, substantially outperforming state-of-the-art feed-forward 3D reconstruction models in both in-domain and out-of-domain settings.

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

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Relightable Holoported Characters: Capturing and Relighting Dynamic Human Performance from Sparse Views

Singh, Kunwar Maheep, Chen, Jianchun, Golyanik, Vladislav, Garbin, Stephan J., Beeler, Thabo, Dabral, Rishabh, Habermann, Marc, Theobalt, Christian

原文摘要

We present _Relightable Holoported Characters_ (RHC), a novel person-specific method for free-view rendering and relighting of full-body and highly dynamic humans solely observed from sparse-view RGB videos at inference. In contrast to classical one-light-at-a-time (OLAT)-based human relighting, our transformer-based RelightNet predicts relit appearance within a single network pass, avoiding costly OLAT-basis capture and generation. For training such a model, we introduce a new capture strategy and dataset recorded in a multi-view lightstage, where we alternate frames lit by random environment maps with uniformly lit tracking frames, simultaneously enabling accurate motion tracking and diverse illumination as well as dynamics coverage. Inspired by the rendering equation, we derive physics-informed features that encode geometry, albedo, shading, and the virtual camera view from a coarse human mesh proxy and the input views. Our RelightNet then takes these features as input and cross-attends them with a novel lighting condition, and regresses the relit appearance in the form of texel-aligned 3D Gaussian splats attached to the coarse mesh proxy. Consequently, our RelightNet implicitly learns to efficiently compute the rendering equation for novel lighting conditions within a single feed-forward pass. Experiments demonstrate our method's superior visual fidelity and lighting reproduction compared to state-of-the-art approaches.

cs.CV

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

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Learning Multi-View Spatial Reasoning from Cross-View Relations

Jeong, Suchae, Song, Jaehwi, Lee, Haeone, Kim, Hanna, Kim, Jian, Lee, Dongjun, Shin, Dong Kyu, Kim, Changyeon 等 12 位作者

原文摘要

Vision-language models (VLMs) have achieved impressive results on single-view vision tasks, but lack the multi-view spatial reasoning capabilities essential for embodied AI systems to understand 3D environments and manipulate objects across different viewpoints. In this work, we introduce Cross-View Relations (XVR), a large-scale dataset designed to teach VLMs spatial reasoning across multiple views. XVR comprises 100K vision-question-answer samples derived from 18K diverse 3D scenes and 70K robotic manipulation trajectories, spanning three fundamental spatial reasoning tasks: Correspondence (matching objects across views), Verification (validating spatial relationships), and Localization (identifying object positions). VLMs fine-tuned on XVR achieve substantial improvements on established multi-view and robotic spatial reasoning benchmarks (MindCube and RoboSpatial). When integrated as backbones in Vision-Language-Action models, XVR-trained representations improve success rates on RoboCasa. Our results demonstrate that explicit training on cross-view spatial relations significantly enhances multi-view reasoning and transfers effectively to real-world robotic manipulation.

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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