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首次观测 · 记录 #1CVF · CVPR 2026:cvf:ce3329f7aed2a5225a3e196a5e6fbb61CVF · CVPR 2026

Mitigating Multimodal Hallucinations via Gradient-based Self-Reflection

Wang, Shan, Shen, Maying, Chang, Nadine, Nguyen, Chuong, Li, Hongdong, Alvarez, Jose M.

原文摘要

Multimodal large language models (MLLMs) achieve strong performance across diverse tasks but remain prone to hallucinations, where outputs are not grounded in visual inputs. This issue can be attributed to two main biases: text-visual bias, the overreliance on prompts and prior outputs, and co-occurrence bias, spurious correlations between frequently paired objects. We propose Gradient-based Influence-Aware Constrained Decoding (GACD), an inference-based method, that addresses both biases without auxiliary models, and is readily applicable to existing models without finetuning. The core of our approach is bias estimation, which uses first-order Taylor gradients to understand the contribution of individual tokens--visual features and text tokens--to the current output. Based on this analysis, GACD mitigates hallucinations through two components: (1) suppressing spurious visual features correlated with the output objects, and (2) rebalancing cross-modal contributions by strengthening visual features relative to text. Experiments across multiple benchmarks demonstrate that GACD effectively reduces hallucinations and improves the visual grounding of MLLMs outputs.

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:ce4882168d360851372fa0fc22bf44f8CVF · CVPR 2026

Gau-Occ: Geometry-Completed Gaussians for Multi-Modal 3D Occupancy Prediction

Lv, Chengxin, Li, Yihui, Yang, Hongyu, Wang, YunHong

原文摘要

3D semantic occupancy prediction is crucial for autonomous driving, yet vision-only approaches suffer from weak geometric cues, and existing multi-modal frameworks often depend on dense voxel or BEV tensors that impose heavy computational cost. We present Gau-Occ, a multi-modal framework that models the scene as a compact collection of semantic 3D Gaussians, enabling geometry-guided fusion without dense volumetric processing. To enhance geometric completeness, a learned LiDAR Completion Diffuser (LCD) trained on real-world priors recovers missing structures from sparse LiDAR, and the completed points are encoded as semantic Gaussian anchors. To further integrate multi-view image semantics, we introduce Gaussian Anchor Fusion (GAF), a geometry-aligned aggregation module that performs anchor-guided 2D sampling, local neighborhood encoding, and cross-modal alignment. By constructing locally aggregated Gaussian descriptors that capture spatial consistency and semantic discriminability, GAF facilitates accurate feature association across modalities. Through anchor-driven refinement of Gaussian attributes, Occ-GS supports detailed 3D occupancy prediction. Extensive experiments across challenging benchmarks demonstrate that Occ-GS achieves state-of-the-art performance.

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:ce4f4ed656d72e11516ccede58f86f18CVF · CVPR 2026

Adaptive Auxiliary Prompt Blending for Target-Faithful Diffusion Generation

Lee, Kwanyoung, Cha, SeungJu, Ahn, Yebin, Oh, Hyunwoo, Koh, Sungho, Kim, Dong-Jin

原文摘要

Diffusion-based text-to-image (T2I) models have made remarkable progress in generating photorealistic and semantically rich images. However, when the target concepts lie in low-density regions of the training distribution, these models often produce semantically misaligned or structurally inconsistent results. This limitation arises from the long-tailed nature of text-image datasets, where rare concepts or editing instructions are underrepresented. To address this, we introduce Adaptive Auxiliary Prompt Blending (AAPB) -- a unified framework that stabilizes the diffusion process in low-density regions. AAPB leverages auxiliary anchor prompts to provide semantic support in rare concept generation and structural support in image editing, ensuring faithful guidance toward the target prompt. Unlike prior heuristic prompt alternation methods, AAPB derives a closed-form adaptive coefficient that optimally balances the influence between the auxiliary anchor and the target prompt at each diffusion step. Grounded in Tweedie's identity, our formulation provides a principled and training-free framework for adaptive prompt blending, ensuring stable and target-faithful generation. We demonstrate the effectiveness of adaptive interpolation over fixed interpolation through controlled experiments and empirically show consistent improvements on the RareBench and FlowEdit datasets, achieving superior semantic accuracy and structural fidelity compared to prior training-free baselines.

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:ce51b22cff0349f05ea2d73566377c29CVF · CVPR 2026

Granulon: Awakening Pixel-Level Visual Encoders with Adaptive Multi-Granularity Semantics for MLLM

Mao, Junyuan, Li, Qiankun, Meng, Linghao, He, Zhicheng, Zhou, Xinliang, Wang, Kun, Liu, Yang, Jin, Yueming

原文摘要

Recent advances in multimodal large language models largely rely on CLIP-based visual encoders, which emphasize global semantic alignment but struggle with fine-grained visual understanding. In contrast, DINOv3 provides strong pixel-level perception yet lacks coarse-grained semantic abstraction, leading to limited multi-granularity reasoning. To address this gap, we propose Granulon, a novel DINOv3-based MLLM with adaptive granularity augmentation. Granulon introduces a text-conditioned granularity Controller that dynamically adjusts the visual abstraction level according to the semantic scope of the textual input, and an Adaptive Token Aggregation module that performs granularity-guided pooling and relation-aware clustering to produce compact, semantically rich visual tokens. This design enables unified "pixel-to-fine-to-coarse" reasoning within a single forward pass. Extensive and interpretable experiments demonstrate that Granulon improves accuracy by 30% and reduces hallucination by 20%, outperforming all visual encoders under identical settings. Code is available at the Supplementary.

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:ce617799f91bd17641b52af590e0ef06CVF · CVPR 2026

Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting

Yu, Tianjiao, Shah, Vedant, Wahed, Muntasir, Shen, Ying, Nguyen, Kiet A., Lourentzou, Ismini

原文摘要

Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part^ 2 GS, a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry and physically consistent articulation. Part^ 2 GS leverages a part-aware 3D Gaussian representation that encodes articulated components with learnable attributes, enabling structured, disentangled transformations that preserve high-fidelity geometry. To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints, including contact enforcement, velocity consistency, and vector-field alignment. Furthermore, we introduce a field of repel points to prevent part collisions and maintain stable articulation paths, significantly improving motion coherence over baselines. Extensive evaluations on both synthetic and real-world datasets show that Part^ 2 GS consistently outperforms state-of-the-art methods by up to 10xin Chamfer Distance for movable parts.

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:ce989e118c06e4d34c3b4e6d145aa803CVF · CVPR 2026

Neighbor-Aware Localized Concept Erasure in Text-to-Image Diffusion Models

Shi, Zhuan, Farashah, Alireza Dehghanpour, de Vries, Rik, Farnadi, Golnoosh

原文摘要

Concept erasure in text-to-image diffusion models seeks to remove undesired concepts while preserving overall generative capability. Localized erasure methods aim to restrict edits to the spatial region occupied by the target concept. However, we observe that suppressing a concept can unintentionally weaken semantically related neighbor concepts, reducing fidelity in fine-grained domains. We propose Neighbor-Aware Localized Concept Erasure (NLCE), a training-free framework designed to better preserve neighboring concepts while removing target concepts. It operates in three stages: (1) a spectrally-weighted embedding modulation that attenuates target concept directions while stabilizing neighbor concept representations, (2) an attention-guided spatial gate that identifies regions exhibiting residual concept activation, and (3) a spatially-gated hard erasure that eliminates remaining traces only where necessary. This neighbor-aware pipeline enables localized concept removal while maintaining the surrounding concept neighborhood structure. Experiments on fine-grained datasets (Oxford Flowers, Stanford Dogs) show that our method effectively removes target concepts while better preserving closely related categories. Additional results on celebrity identity, explicit content and artistic style demonstrate robustness and generalization to broader erasure scenarios.

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:cec4ce0d3a7002c78d65478bca1bcdb0CVF · CVPR 2026

SFR-Net: Steering-Fusion-Refining Network in Multi-label Zero-Shot Sewer Defect Detection

Chen, Zhao-Min, Huang, Xinjian, Ge, Yisu, Li, Yu

原文摘要

Due to the prohibitive cost of data annotation and the inability to obtain sufficient sample data for all defect categories, municipal sewer pipe defect detection poses significant generalization challenges for traditional models. Multi-Label Zero-Shot Learning (ML-ZSL) offers a viable solution to address this challenge. However, existing methods struggle to establish robust and fine-grained visual-semantic alignment between the complex visual environment inside the pipes and the often sparse semantic descriptions, leading to a critical issue: Alignment Ambiguity. To mitigate this, we propose a novel Steering-Fusion-Refining Network (SFR-Net) that follows a three-stage paradigm to progressively dissolve this ambiguity. This is achieved as the Representation Steering (RS) module first integrates a parameter-efficient feature steering mechanism to continuously adapt the representation to the pipe scene; the Multi-Granularity Evidence Fusion (MEF) module subsequently aggregates unambiguous multi-granularity visual evidence through decoupled global and local paths; and the Generalized Relational Score Refining (GR) module ultimately learns and transfers relational logic from seen defects to gain a universal score correction ability, directly refining preliminary prediction scores and significantly boosting the model's zero-shot generalization and prediction consistency. Extensive experiments on the public Sewer-ML dataset and our private WZ-Pipe dataset demonstrate that the proposed SFR-Net achieves state-of-the-art (SOTA) performance in multi-label zero-shot learning task.

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:cecb6de0cba42c749418917b0d4f5180CVF · CVPR 2026

ManifoldNeuS: Manifold-aware View Optimizability for Pose-Free Neural Surface Reconstruction

Liu, Xinxin, Wang, Xue, Zhou, Guoqing, Wang, Qing

原文摘要

Jointly optimizing camera poses and object geometry from unposed images is a challenging task in neural surface reconstruction. Existing methods often suffer from pose drift and geometric distortion, stemming from the easy-view bias --- uniform view optimization favors easy-to-optimize views with abundant texture and good overlap that dominate gradient updates, while hard-to-optimize counterparts with weak texture or limited overlap yet critical for geometric completeness are progressively marginalized. To address this, we propose ManifoldNeuS, a novel method that explicitly models and leverages per-view optimizability to guide pose-free neural surface reconstruction. Specifically, we introduce the manifold-aware view optimizability score (MaVOS), which jointly assesses immediate fitness (the ease of optimizing each view) and long-term coverage gain (the value of optimizing each view) over the view-coherent manifold. Building on the MaVOS, we further devise a reconstruction pipeline that incorporates the per-view optimizability as a state control signal to guide the joint optimization process through three key components: dynamic view scheduling, gated positional encoding, and anti-score loss weighting. Experimental results on the benchmark dataset demonstrate that ManifoldNeuS outperforms existing methods in terms of accurate pose estimation and high-quality reconstruction, achieving robust joint optimization without known camera poses.

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:ced4171ae7803044601aac000cc757bcCVF · CVPR 2026

Particulate: Feed-Forward 3D Object Articulation

Li, Ruining, Yao, Yuxin, Zheng, Chuanxia, Rupprecht, Christian, Lasenby, Joan, Wu, Shangzhe, Vedaldi, Andrea

原文摘要

We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate maps the output of the network back to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate also works on AI-generated 3D assets, enabling the generation of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D model. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Empirically, Particulate significantly outperforms 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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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ced7109d62bb4514d4670f869e6899c8CVF · CVPR 2026

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving

Han, Jianhua, Tian, Meng, Zhu, Jiangtong, He, Fan, Zhang, Huixin, Guo, Sitong, Zhu, Dechang, Tang, Hao 等 19 位作者

原文摘要

Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex interactions. However, current vision-language models are weak at spatial grounding and understanding, and VLA systems built on them therefore show limited perception and localization ability.To address these challenges, we introduce Percept-WAM, a perception-enhanced World-Awareness-Action Model that is the first to implicitly integrate 2D/3D scene understanding abilities within a single vision-language model (VLM).Instead of relying on QA-style spatial reasoning, Percept-WAM unifies 2D/3D perception tasks into World-PV and World-BEV tokens, which encode both spatial coordinates and confidence.We propose a grid-conditioned prediction mechanism for dense object perception, incorporating IoU-aware scoring and parallel autoregressive decoding, improving stability in long-tail, far-range, and small-object scenarios. Additionally, Percept-WAM leverages pretrained VLM parameters to retain general intelligence (e.g., logical reasoning) and can output perception results and trajectory control outputs directly. Experiments show that Percept-WAM matches or surpasses classical detectors and segmenters on downstream perception benchmarks, achieving 51.7/58.9 mAP on COCO 2D detection and nuScenes BEV 3D detection. When integrated with trajectory decoders, it further improves planning performance on nuScenes and NAVSIM, e.g., surpassing DiffusionDrive by 2.1 in PMDS on NAVSIM.Qualitative results further highlight its strong open-vocabulary and long-tail generalization.

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:cef8b2db5cb6b51f187f307e000db53fCVF · CVPR 2026

Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models

Zhao, Dachuan, Li, Weiyue, Shen, Zhenda, Qiu, Yushu, Xu, Bowen, Chen, Haoyu, Chen, Yongchao

原文摘要

Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches attempt to mitigate bias by replacing the most attribute-correlated embedding coordinates with neutral values. However, our systematic analysis reveals three critical limitations of this coordinate-wise approach: feature entanglement, poor cross-dataset generalization, and incomplete bias removal. We find that bias is not localized to a few coordinates but is instead distributed across a few linear subspaces. To address these limitations, we propose Subspace Projection Debiasing (SPD), a geometrically principled framework that identifies and removes the entire subspace of linearly decodable bias while reinserting a neutral mean component to preserve semantic fidelity. Extensive experiments across zero-shot classification, text-to-image retrieval, and image generation validate the effectiveness of SPD: our method achieves more robust debiasing with an average improvement of 18.5% across four fairness metrics, while maintaining minimal loss in task performance compared to the best debiasing baseline.

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:cefd880e5246032cac5bdb309eb52495CVF · CVPR 2026

WonderZoom: Multi-Scale 3D World Generation

Cao, Jin, Yu, Hong-Xing, Wu, Jiajun

原文摘要

We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited to single-scale synthesis and cannot produce coherent scene contents at varying granularities. The fundamental challenge is the lack of a scale-aware 3D representation capable of generating and rendering content with largely different spatial sizes. WonderZoom addresses this through two key innovations: (1) scale-adaptive Gaussian surfels for generating and real-time rendering of multi-scale 3D scenes, and (2) a progressive detail synthesizer that iteratively generates finer-scale 3D contents. Our approach enables users to "zoom into" a 3D region and auto-regressively synthesize previously non-existent fine details from landscapes to microscopic features. Experiments demonstrate that WonderZoom significantly outperforms state-of-the-art video and 3D models in both quality and alignment, enabling multi-scale 3D world creation from a single image. We show video results and an interactive viewer of generated multi-scale 3D worlds on the website of the supplementary materials.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:cf30c5c25f9a3e66d1aa830a96a3c7c1CVF · CVPR 2026

Is Parameter Isolation Better for Prompt-Based Continual Learning?

Li, Jiangyang, Ding, Chenhao, Dong, SongLin, Wang, Qiang, Zhao, Jianchao, He, Yuhang, Gong, Yihong

原文摘要

Prompt-based continual learning methods effectively mitigate catastrophic forgetting. However, most existing methods assign a fixed set of prompts to each task, completely isolating knowledge across tasks and resulting in suboptimal parameter utilization. To address this, we consider the practical needs of continual learning and propose a prompt-sharing framework. This framework constructs a global prompt pool and introduces a task-aware gated routing mechanism that sparsely activates a subset of prompts to achieve dynamic decoupling and collaborative optimization of task-specific feature representations. Furthermore, we introduce a history-aware modulator that leverages cumulative prompt activation statistics to protect frequently used prompts from excessive updates, thereby mitigating inefficient parameter usage and knowledge forgetting. Extensive analysis and empirical results demonstrate that our approach consistently outperforms existing static allocation strategies in effectiveness and efficiency. Code and models will be released.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:cf32fc76393915976d2bd8c8fdaf620eCVF · CVPR 2026

NaTex: Seamless Texture Generation as Latent Color Diffusion

Lai, Zeqiang, Zhao, Yunfei, Zhao, Zibo, Yang, Xin, Huang, Xin, Huang, Jingwei, Yue, Xiangyu, Guo, Chunchao

原文摘要

We present NaTex, a native texture generation framework that predicts texture color directly in 3D space. In contrast to previous approaches that rely on baking 2D multi-view images synthesized by geometry-conditioned Multi-View Diffusion models (MVDs), NaTex avoids several inherent limitations of the MVD pipeline. These include difficulties in handling occluded regions that require inpainting, achieving precise mesh-texture alignment along boundaries, and maintaining cross-view consistency and coherence in both content and color intensity. NaTex features a novel paradigm that addresses the aforementioned issues by viewing texture as a dense color point cloud. Driven by this idea, we propose latent color diffusion, which comprises a geometry-awared color point cloud VAE and a multi-control diffusion transformer (DiT), entirely trained from scratch using 3D data, for texture reconstruction and generation. To enable precise alignment, we introduce native geometry control that conditions the DiT on direct 3D spatial information via positional embeddings and geometry latents. We co-design the VAE-DiT architecture, where the geometry latents are extracted via a dedicated geometry branch tightly coupled with the color VAE, providing fine-grained surface guidance that maintains strong correspondence with the texture. With these designs, NaTex demonstrates strong performance, significantly outperforming previous methods in texture coherence and alignment. Moreover, NaTex also exhibits strong generalization capabilities, either training-free or with simple tuning, for various downstream applications, e.g., material generation, texture refinement, and part segmentation and texturing.

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:cf3e972a6f149228bb4593b6b2a68690CVF · CVPR 2026

CrossHOI-Bench: A Unified Benchmark for HOI Evaluation across Vision-Language Models and HOI-Specific Methods

Lei, Qinqian, Wang, Bo, Tan, Robby T.

原文摘要

HOI detection has long been dominated by task-specific models, sometimes with early vision-language backbones such as CLIP. With the rise of large generative VLMs, a key question is whether standalone VLMs can perform HOI detection competitively against specialized HOI methods. Existing benchmarks such as HICO-DET require exact label matching under incomplete annotations, so any unmatched prediction is marked wrong. This unfairly penalizes valid outputs, especially from less constrained VLMs, and makes cross-paradigm comparison unreliable. To address this limitation, we introduce CrossHOI-Bench, a multiple-choice HOI benchmark with explicit positives and curated negatives, enabling unified and reliable evaluation of both VLMs and HOI-specific models. We further focus on challenging scenarios, such as multi-person scenes and fine-grained interaction distinctions, which are crucial for revealing real differences between the two paradigms. Experiments show that large VLMs achieve competitive, sometimes superior, zero-shot performance, yet they struggle with multiple concurrent actions and with correctly assigning interactions to the target person. Conversely, HOI-specific methods remain weaker in general HOI reasoning but demonstrate stronger multi-action recognition and more reliable identification of which person performs which action. These findings expose complementary strengths and weaknesses of VLMs and HOI-specific methods, which existing benchmarks fail to reveal due to incorrect penalization.

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:cf3ecb82d9aa3d4a6f7b4571126ac438CVF · CVPR 2026

CausalLens: Sensitivity-Guided Multi-Head Causal Intervention for Hallucination Mitigation in Large Vision-Language Models

Ji, Junyang, Liu, Qifan, Yang, Wenming, He, Zhihai

原文摘要

Recent Large Vision-Language Models (LVLMs) have shown impressive capabilities in multimodal understanding and generation. Despite this progress, they remain prone to *hallucination*, where model outputs conflict with the visual input due to an over-reliance on textual priors. Existing inference-time mitigation approaches frequently depend on multi-pass or contrastive decoding, which increases latency and limits their applicability in real-time settings. To address this limitation, we propose **CausalLens**, a training-free and single-pass intervention that directly adjusts the decoder hidden states to strengthen visual grounding. By decomposing attention heads into visual, textual, and system prompt pathways, CausalLens identifies visually reliable heads using a sensitivity measure and selectively adjusts their mid-layer hidden-state contributions. A projection-aligned correction further stabilizes these adjusted states after multi-head fusion, ensuring that the enhanced visual information is preserved throughout decoding. Extensive experiments across multiple hallucination benchmarks and LVLM architectures demonstrate that CausalLens consistently improves visual fidelity while adding negligible computational overhead. The method requires no fine-tuning or architectural changes, making it well-suited for practical, latency-sensitive applications.

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:cf40511f8fa9df875a65efebfce9a38fCVF · CVPR 2026

Structure-to-Intensity Diffusion for Adverse-Weather LiDAR Generation

Ni, Peiyang, Yang, Longyu, Zhang, Lu, Saito, Kuniaki, Tan, Yap-Peng, Shen, Fumin, Shen, Heng Tao, Zhu, Xiaofeng 等 9 位作者

原文摘要

Adverse-weather LiDAR point cloud generation is challenged by complex weather-induced degradations. These degradations affect geometry and reflectance in fundamentally different ways, making joint modeling difficult and ambiguous, especially when diverse real-world training data is limited. To address this, we propose Structure-to-Intensity Diffusion (SiD), a diffusion-based framework that explicitly factorizes the denoising process at each time step: it first reconstructs the geometric structure, then conditions reflectance intensity denoising on the estimated structure. This structure-conditioned design decomposes the joint distribution, reduces modeling ambiguity, and leads to point clouds that are both geometrically coherent and radiometrically realistic. To mitigate data scarcity, we introduce Real-Prior Weather Simulation (RPWS), a degradation module that leverages real-world sensor statistics to synthesize physically plausible adverse-weather point clouds from clear scans. Extensive experiments demonstrate that, with similar model complexity, our approach outperforms the previous state-of-the-art in generating adverse-weather LiDAR scans with both structural and radiometric properties more closely aligned with real-world data.

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:cf66900faa91e5722bd20dc8fa4fe529CVF · CVPR 2026

Think-as-You-See: Streaming Chain-of-Thought Reasoning for Large Vision-Language Models

Zhang, Jialiang, Tong, Junlong, Lin, Junyan, Wu, Hao, Sun, Yirong, Ma, Yunpu, Shen, Xiaoyu

原文摘要

Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities in Chain-of-Thought (CoT) reasoning. However, existing LVLM reasoning paradigms only begin reasoning after the entire video becomes available, introducing unnecessary latency and diminishing attention to early visual cues in dynamic scenes. Inspired by the human ability to think while watching, we introduce a streaming reasoning paradigm for LVLMs, where reasoning unfolds sequentially with incoming frames and deepens after the full video is observed. We instantiate this paradigm through Think-as-You-See (TaYS), a unified framework that enables LVLMs to reason while watching by integrating streaming CoT generation, stream-constrained training, and stream-parallel inference. Specifically, TaYS employs temporally aligned streaming reasoning units with precise CoT supervision, enforces ordered reasoning via streaming attention masks and positional encodings, and utilizes a parallel KV caches mechanism that decouples input encoding from reasoning generation, ensuring alignment and true concurrency. We evaluate TaYS on the Qwen2.5-VL model family across representative video CoT tasks, including event dynamics analysis, causal reasoning, and thematic understanding. Experimental results show that TaYS achieves superior reasoning performance compared with batch-mode CoT, while reducing pre-reasoning latency to under one second and overall answer delay by more than 50%. These findings demonstrate the effectiveness of the streaming paradigm in enabling real-time, human-like reasoning for LVLMs.

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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HDW-SR: High-Frequency Guided Diffusion Model based on Wavelet Decomposition for Image Super-Resolution

Yang, Chao, Zhang, Boqian, Xu, Jinghao, Jiang, Guang

原文摘要

Diffusion-based methods have shown great promise in single image super-resolution (SISR); however, existing approaches often produce blurred fine details due to insufficient guidance in the high-frequency domain. To address this issue, we propose a High-Frequency Guided Diffusion Network based on Wavelet Decomposition (HDW-SR), which replaces the conventional U-Net backbone in diffusion frameworks. Specifically, we perform diffusion only on the residual map, allowing the network to focus more effectively on high-frequency information restoration. We then introduce wavelet-based downsampling in place of standard CNN downsampling to achieve multi-scale frequency decomposition, enabling sparse cross-attention between the high-frequency subbands of the pre-super-resolved image and the low-frequency subbands of the diffused image for explicit high-frequency guidance. Moreover, a Dynamic Thresholding Block (DTB) is designed to refine high-frequency selection during the sparse attention process. During upsampling, the invertibility of the wavelet transform ensures low-loss feature reconstruction. Experiments on both synthetic and real-world datasets demonstrate that HDW-SR achieves competitive super-resolution performance, excelling particularly in recovering fine-grained image details. Code is available at https://github.com/Baty2023/HDW-SR.

cs.CV

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

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Detecting AI-Generated Forgeries via Iterative Manifold Deviation Amplification

Zhang, Jiangling, Gao, Shuxuan, Liu, Bofan, Feng, Siqiang, Huang, Jirui, Chen, Yaxiong, Chen, Ziyu

原文摘要

The proliferation of highly realistic AI-generated images poses critical challenges for digital forensics, demanding precise pixel-level localization of manipulated regions. Existing methods predominantly learn discriminative patterns of specific forgeries, struggling with novel manipulations as editing techniques evolve. We propose the Iterative Forgery Amplifier Network (IFA-Net), which shifts from learning "what is fake" to modeling "what is real". Grounded in the principle that all manipulations deviate from the natural image manifold, IFA-Net leverages a frozen Masked Autoencoder (MAE) pretrained on real images as a universal realness prior. Our framework operates through a two-stage closed-loop process: an initial Dual-Stream Segmentation Network (DSSN) fuses the original image with MAE reconstruction residuals for coarse localization, then a Task-Adaptive Prior Injection (TAPI) module converts this coarse prediction into guiding prompts that steer the MAE decoder to amplify reconstruction failures in suspicious regions, enabling precise refinement. Extensive experiments on four diffusion-based inpainting benchmarks show that IFA-Net achieves an average improvement of 6.5% in IoU and 8.1% in F1-score over the second-best method, while demonstrating strong generalization to traditional manipulation types.

cs.CV

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

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Anomaly-Related Residual Fields for Cross-domain Anomaly Detection

Gao, Kewei, Xie, Jiayi, Shen, Zhengda, Qin, Weijun, Jia, Lingxiang, Chen, Kejia, Feng, Zunlei, Bei, Yijun

原文摘要

Label-free image anomaly detection is difficult because anomalies must be separated from intra-normal variability. Diffusion models learn a manifold for normal data, and, under the common assumption that off-manifold anomalies are harder to generate and yield larger prediction errors, many methods build detectors from prediction residuals; yet reverse-process stochasticity and complex but normal structure also produce large residuals, so magnitude alone is non-diagnostic. To clarify what is recoverable from such noisy residuals, the theory examines how residual signals propagate through later reverse steps, showing that variability consistent with normal statistics is gradually absorbed toward stationarity, whereas anomalous regions retain an additional non-stationary signal that persists. Building on this insight, the Residual-Evolution Field (REF) isolates this persistent signal, with labeled source data calibrating the extractor and Cross-domain Field Alignment (CFA) transferring it to unlabeled targets. A theoretical framework with formal analysis is established, and experiments across multiple benchmarks under substantial domain shifts demonstrate state-of-the-art performance, improving over strong baselines by 2.01-14 percentage points (pp).

cs.CV

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

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Ref4D-VideoBench: Four-Dimensional Reference-Based Evaluation of Text-to-Video Generative Models

Wei, Jiajia, He, Yujia, Hou, Yuhan, Qi, Hang, Wang, Sihua, Shi, Jincheng, Li, Kwok Fung, Zheng, Zibin 等 9 位作者

原文摘要

Most existing evaluations of generated videos adopt a no-reference paradigm. Although recent benchmarks cover multiple dimensions and show moderate correlation with human preferences, relying solely on textual prompts weakens real-world constraints and makes it difficult to produce accountable and interpretable judgments on instance-level issues such as target behavior deviation, temporal inconsistency, and commonsense violations. In scenarios with explicit expectations, such as controlled generation, reference videos naturally provide rich, unambiguous spatio-temporal evidence, enabling stricter and more trustworthy assessment. Motivated by this, we propose Ref4D-VideoBench, a reference-based, fine-grained, multi-dimensional benchmark for generated video evaluation. Ref4D-VideoBench contains 600 high-quality reference videos with tightly evidence-bounded prompts, and introduces a 12-metric structured evaluation suite along four key dimensions: basic semantic alignment, motion consistency, event temporal consistency, and world knowledge consistency. Experiments on eight text-to-video models show that our method achieves stronger agreement with human judgments than representative no-reference frameworks. Our code is available at https://github.com/TAILab-W/Ref4D-VideoBench.

cs.CV

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

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UniAVGen: Unified Audio and Video Generation with Asymmetric Cross-Modal Interactions

Zhang, Guozhen, Zhou, Zixiang, Hu, Teng, Peng, Ziqiao, Zhang, Youliang, Chen, Yi, Zhou, Yuan, Lu, Qinglin 等 9 位作者

原文摘要

Due to the lack of effective cross-modal modeling, existing open-source audio-video generation methods often exhibit compromised lip synchronization and insufficient semantic consistency. To mitigate these drawbacks, we propose UniAVGen, a unified framework for human-centric joint audio and video generation. UniAVGen is anchored in a dual-branch joint synthesis architecture, incorporating two parallel Diffusion Transformers (DiTs) to build a cohesive cross-modal latent space. At its heart lies an Asymmetric Cross-Modal Interaction mechanism, which enables bidirectional, temporally aligned cross-attention, thus ensuring precise spatiotemporal synchronization and semantic consistency. Furthermore, this cross-modal interaction is augmented by a Face-Aware Modulation (FAM) module, which dynamically prioritizes salient regions in the interaction process. To enhance generative fidelity during inference, we additionally introduce Modality-Aware Classifier-Free Guidance (MA-CFG), a novel strategy that explicitly amplifies cross-modal correlation signals. Notably, UniAVGen's robust joint synthesis design enables the seamless unification of pivotal audio-visual tasks within a single model. Furthermore, we demonstrate that joint multi-task training can further boost the performance of joint generation. Comprehensive experiments validate that, with far fewer training samples (1.3M vs. 30.1M), UniAVGen delivers overall advantages in audio-video synchronization, timbre consistency, and emotion consistency.

cs.CV

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

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Iris: Bringing Real-World Priors into Diffusion Model for Monocular Depth Estimation

Cai, Xinhao, Pei, Gensheng, Sun, Zeren, Yao, Yazhou, Shen, Fumin, Wang, Wenguan

原文摘要

In this paper, we propose Iris, a deterministic framework for Monocular Depth Estimation (MDE) that integrates real-world priors into the diffusion model. Conventional feed-forward methods rely on massive training data, yet still miss details. Previous diffusion-based methods leverage rich generative priors yet struggle with synthetic-to-real domain transfer. Iris, in contrast, preserves fine details, generalizes strongly from synthetic to real scenes, and remains efficient with limited training data. To this end, we introduce a two-stage Priors-to-Geometry Deterministic (PGD) schedule: the prior stage uses Spectral-Gated Distillation (SGD) to transfer low-frequency real priors while leaving high-frequency details unconstrained, and the geometry stage applies Spectral-Gated Consistency (SGC) to enforce high-frequency fidelity while refining with synthetic ground truth. The two stages share weights and are executed with a high-to-low timestep schedule. Extensive experimental results confirm that Iris achieves significant improvements in MDE performance with strong in-the-wild generalization.

cs.CV

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

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UniDAC: Universal Metric Depth Estimation for Any Camera

Ganesan, Girish Chandar, Guo, Yuliang, Ren, Liu, Liu, Xiaoming

原文摘要

Monocular metric depth estimation (MMDE) is a core challenge in computer vision, playing a pivotal role in real-world applications that demand accurate spatial understanding. Although prior works have shown promising zero-shot performance in MMDE, they often struggle with generalization across diverse camera types, such as fisheye and 360^\circ cameras. Recent advances have addressed this through unified camera representations or canonical representation spaces, but they require either including large FoV camera data during training or separately trained models for different domains. We propose UniDAC, an MMDE framework that presents universal robustness in all domains and generalizes across diverse cameras using a single model. We achieve this by decoupling metric depth estimation into relative depth prediction and spatially varying scale estimation, enabling robust performance across different domains. We propose a lightweight Depth-Guided Scale Estimation module that upsamples a coarse scale map to high resolution using the relative depth map as guidance to account for local scale variations. Furthermore, we introduce RoPE-\phi, a distortion-aware positional embedding that respects the spatial warping in Equi-Rectangular Projections (ERP) via latitude-aware weighting. UniDAC achieves state of the art (SoTA) in cross-camera generalization by consistently outperforming prior methods across all datasets.

cs.CV

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

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AURA: Multi-modal Shared Autonomy for Urban Navigation

Ma, Yukai, He, Honglin, Song, Selina, Wu, Wayne, Zhou, Bolei

原文摘要

Long-horizon navigation in complex urban environments relies heavily on continuous human operation, which leads to fatigue, reduced efficiency, and safety concerns. Shared autonomy, where a Vision-Language AI agent and a human operator collaborate on maneuvering the mobile machine, presents a promising solution to address these issues. However, existing shared autonomy methods often require humans and AI to operate within the same action space, leading to high cognitive overhead. We present Assistive Urban Robot Autonomy (AURA), a new multi-modal framework that decomposes urban navigation into high-level human instruction and low-level AI control. AURA incorporates a Spatial-Aware Instruction Encoder to align various human instructions with visual and spatial context. To facilitate training, we construct MM-CoS, a large-scale dataset comprising teleoperation and vision-language descriptions. Experiments in simulation and the real world demonstrate that AURA effectively follows human instructions, reduces manual operation effort, and improves navigation stability, while enabling online adaptation. Moreover, under similar takeover conditions, our shared autonomy framework reduces the frequency of takeovers by more than 44%. Demo video and more detail are provided in the project page.

cs.CV · cs.AI

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

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M3Grounder: Mask-Based Multi-Span and Multi-Granular Grounding for Document QA

Venna, Venkata Kesav, Gunda, Sai Madhusudan, Jinka, Jyothi Swaroopa, Rachakonda, Hrithik Sagar, Srinivasan, Anirudh, Sarvadevabhatla, Ravi Kiran

原文摘要

**Document QA** requires not only accurate answers but also identifying where each answer is grounded on the page. Most models treat the task as text-only generation, while existing answer grounding methods generate coarse bounding boxes that fail to capture curved text. We introduce **M3Grounder, a hybrid vision-language and segmentation architecture that formulates document grounding as pixel-level segmentation. It produces fine-grained evidence masks** refined by a bleed-suppression loss to prevent spillover. M3Grounder autoregressively generates answer text interleaved with [GROUND] tokens that link individual answer spans to their corresponding evidence regions. Also, **M3Grounder grounds evidence hierarchically across phrase, line, and block levels** using an enclosure loss that enforces spatial containment. We release **GroundingDocQA dataset (200K documents, 2M multi-span and multi-granular QA pairs with pixel-level grounding masks)**, built through a data engine that handles complex layouts, curved-text, and graphics-rich documents. We also release **GroundingDocQA-Bench, a diverse and challenging human-verified benchmark**. M3Grounder sets **a new state of the art in grounded DocVQA, advancing from coarse boxes to hierarchical, fine-grained and contextually grounded evidence.**

cs.CV

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

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HieraMamba: Video Temporal Grounding via Hierarchical Anchor-Mamba Pooling

An, Joungbin, Grauman, Kristen

原文摘要

Video temporal grounding, the task of localizing the start and end times of a natural language query in untrimmed video, requires capturing both global context and fine-grained temporal detail. This challenge is particularly pronounced in long videos, where existing methods often compromise temporal fidelity by over-downsampling or using fixed windows. We present HieraMamba, a hierarchical architecture that preserves temporal structure and semantic richness across scales. At its core are Anchor-MambaPooling (AMP) blocks, which utilize Mamba's selective scanning to produce compact anchor tokens summarizing video content across scales. We further introduce anchor-conditioned and segment-pooled contrastive losses-two complementary objectives that encourage anchors to retain local detail while remaining globally discriminative. HieraMamba sets a new state-of-the-art on Ego4D-NLQ, MAD, and TACoS, demonstrating precise, temporally faithful localization in long, untrimmed videos.

cs.CV

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

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Denoise and Align: Towards Source-Free UDA for Robust Panoramic Semantic Segmentation

Chang, Yaowen, Cao, Zhen, Zheng, Xu, Mi, Xiaoxin, Dong, Zhen

原文摘要

Panoramic semantic segmentation is pivotal for comprehensive 360deg scene understanding in critical applications like autonomous driving and virtual reality. However, progress in this domain is constrained by two key challenges: the severe geometric distortions inherent in panoramic projections and the prohibitive cost of dense annotation. While Unsupervised Domain Adaptation (UDA) from label-rich pinhole-camera datasets offers a viable alternative, many real-world tasks impose a stricter source-free (SFUDA) constraint where source data is inaccessible for privacy or proprietary reasons. This constraint significantly amplifies the core problems of domain shift, leading to unreliable pseudo-labels and dramatic performance degradation, particularly for minority classes. To overcome these limitations, we propose the DAPASS framework. DAPASS introduces two synergistic modules to robustly transfer knowledge without source data. First, our Panoramic Confidence-Guided Denoising (PCGD) module generates high-fidelity, class-balanced pseudo-labels by enforcing perturbation consistency and incorporating neighborhood-level confidence to filter noise. Second, a Contextual Resolution Adversarial Module (CRAM) explicitly addresses scale variance and distortion by adversarially aligning fine-grained details from high-resolution crops with global semantics from low-resolution contexts. DAPASS achieves state-of-the-art performances on outdoor (Cityscapes-to-DensePASS) and indoor (Stanford2D3D) benchmarks, yielding 55.04% (+2.05%) and 70.38% (+1.54%) mIoU, respectively.

cs.CV

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

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D2Cache: Second-Order Delta Caching for Higher Video Diffusion Acceleration

Liu, Enhuai, Wang, Yunke, Sun, Changming, Xu, Chang

原文摘要

Video diffusion models achieve impressive visual fidelity but remain computationally prohibitive for real-time or interactive generation due to their sequential denoising process. Recent caching methods accelerate inference by reusing outputs across timesteps, typically estimating each new output from the first-order residual, which is the difference between adjacent model predictions.To mitigate the accumulated error in caching methods, we propose D2Cache, a training-free method that leverages the smoothness of second-order residual delta, which is temporal differences between consecutive first-order residuals, to predict future timesteps more accurately. We theoretically show that this second-order correction improves prediction accuracy and effectively suppresses cumulative errors. Moreover, D2Cache adaptively scales second-order deltas using error estimates derived from timestep embeddings, maintaining accuracy across varying cache intervals.Empirically, D2Cache outperforms the state-of-the-art TeaCache across four video diffusion models (Latte, Open-Sora, LTX-video, and Wan2.1) at comparable acceleration rates, showing even larger gains under higher acceleration settings.

cs.CV

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

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