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首次观测 · 记录 #1CVF · CVPR 2026:cvf:58e4ef16d30da9fcf8152a74409a6675CVF · CVPR 2026

Multi-Hierarchical Contrastive Spectral Fusion for Multi-View Clustering

Cai, Bing, Wang, Xiaoli, Lu, Gui-Fu, Li, Zechao

原文摘要

Multi-view contrastive clustering has emerged as a powerful paradigm for learning comprehensive representations from heterogeneous data sources. However, prevailing approaches typically overlook the intrinsic geometric and clustering structures, rendering them structure-agnostic. In this paper, we propose a novel framework that performs Multi-Hierarchical Contrastive Spectral Fusion (MCSF) to address these limitations. MCSF integrates deep spectral embedding into the encoder to preserve local manifold structure, guiding the learned representations to be clustering-friendly. To enhance cross-view consistency, MCSF introduces a multi-hierarchical contrastive loss jointly optimizing (1) view-specific structure preservation, (2) view-consensus alignment, and (3) consensus structure refinement. This mechanism enables the construction of an accurate and semantically consistent consensus representation, effectively fusing multi-view information and uncovering authentic cluster structures. Extensive experiments on benchmarks validate the effectiveness of multi-hierarchical contrastive spectral fusion in clustering accuracy and representation 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:58fe4c74f53ba285572b99720af734b6CVF · CVPR 2026

BiGMINT: Biologically-guided Hierarchical Multimodal Integration for Modeling Multiple Compound Activities in Drug Discovery

Pati, Pushpak, Li, Bo, Khan, Abbas Rayabat, Albuquerque, Tomé, Jaensch, Steffen, Mollaysa, Amina, Abdelmoula, Walid M., Allen, Samantha J. 等 15 位作者

原文摘要

Compound activity modeling is critical for drug discovery, where accurate *in silico* predictions can significantly reduce reliance on expensive, time-consuming target-specific experimental assays. Traditional machine learning approaches for compound activity modeling typically rely on either chemoproteomics-centric molecular data or phenotype-centric imaging screens, limiting their ability to capture complementary biological signals. While multimodal approaches show promise, they often fail to capture the interplay between molecular mechanisms and cellular responses. In this paper, we present **BiGMINT**, a **Bi**ologically **G**uided **M**ultimodal framework that hierarchically **INT**egrates chemoproteomic and high-content imaging (HCI) data, introducing chemoproteomics-guided phenotypic aggregation, task-aware cross-modal fusion, and protein-protein interaction priors for modeling activities. On two large-scale in-house datasets, with 99K and 40K compound-HCI pairs from U2OS and iNeuron, **BiGMINT** improves mean AUCROC by up to 10.0% and 4.2%, and high-performing task coverage by up to 103% and 56% over best unimodal and multimodal methods. Thorough analysis revealed mechanistic insights, showing these gains stem from modality complementarity, and protein-protein priors enhance modeling of challenging activities.

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:5906f89dce31aa2df3bef4bd9c0dc966CVF · CVPR 2026

Goal-Driven Reward by Video Diffusion Models for Reinforcement Learning

Wang, Qi, Wu, Mian, Zhang, Yuyang, Yuan, Mingqi, Zhang, Wenyao, You, Haoxiang, Wang, Yunbo, Jin, Xin 等 10 位作者

原文摘要

Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Designing such reward functions can be challenging and may not generalize well across different tasks. To address this limitation, we leverage the rich world knowledge contained in pretrained video diffusion models to provide goal-driven reward signals for RL agents without ad-hoc design of reward. Our key idea is to exploit off-the-shelf video diffusion models pretrained on large-scale video datasets as informative reward functions in terms of video-level and frame-level goals. For video-level rewards, we first finetune a pretrained video diffusion model on domain-specific datasets and then employ its video encoder to evaluate the alignment between the latent representations of agent's trajectories and the generated goal videos. To enable more fine-grained goal-achievement, we derive a frame-level goal by identifying the most relevant frame from the generated video using CLIP, which serves as the goal state. We then employ a learned forward-backward representation that represents the probability of visiting the goal state from a given state-action pair as frame-level reward, promoting more coherent and goal-driven trajectories. Experiments on Meta-World and Distracting Control Suite demonstrate the effectiveness of our approach.

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:5909aaa27d1a56d99dc3a891158f7cbcCVF · CVPR 2026

RI-Mamba: Rotation-Invariant Mamba for Robust Text-to-Shape Retrieval

Nguyen, Khanh, de Silva Edirimuni, Dasith, Hassan, Ghulam Mubashar, Mian, Ajmal

原文摘要

3D assets have rapidly expanded in quantity and diversity due to the growing popularity of virtual reality and gaming. As a result, text-to-shape retrieval has become essential in facilitating intuitive search within large repositories. However, existing methods require canonical poses and support few object categories, limiting their real-world applicability where objects can belong to diverse classes and appear in random orientations. To address this challenge, we propose RI-Mamba, the first rotation-invariant state-space model for point clouds. RI-Mamba defines global and local reference frames to disentangle pose from geometry and uses Hilbert sorting to construct token sequences with meaningful geometric structure while maintaining rotation invariance. We further introduce a novel strategy to compute orientational embeddings and reintegrate them via feature-wise linear modulation, effectively recovering spatial context and enhancing model expressiveness. Our strategy is inherently compatible with state-space models and operates in linear time. To scale up retrieval, we adopt cross-modal contrastive learning with automated triplet generation, allowing training on diverse datasets without manual annotation. Extensive experiments demonstrate RI-Mamba's superior representational capacity and robustness, achieving state-of-the-art performance on the OmniObject3D benchmark across more than 200 object categories under arbitrary orientations. The project page is available at ndkhanh360.github.io/project-rimamba.

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:59180968df37b1b87b4f57898c88e33fCVF · CVPR 2026

Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment

Amoyal, Roy, Freifeld, Oren, Baskin, Chaim

原文摘要

We present Gaussian Splatting Alignment (GSA), a novel method for aligning two independent 3D Gaussian Splatting (3DGS) models via a similarity transformation (rotation, translation, and scale), even when they are of different objects in the same category (e.g., different cars). In contrast, existing methods can only align 3DGS models of the same object (e.g., the same car) and often must be given true scale as input, while we estimate it successfully. GSA leverages viewpoint-guided spherical map features to obtain robust correspondences and introduces a two-step optimization framework that aligns 3DGS models while keeping them fixed. First, we apply an iterative feature-guided absolute orientation solver as our coarse registration, which is robust to poor initialization (e.g., 180deg misalignment or a 10x scale gap). Next, we use a fine registration step that enforces multi-view feature consistency, inspired by inverse radiance-field formulations. The first step already achieves state-of-the-art performance, and the second further improves results. In the same-object case, GSA outperforms prior works, often by a large margin, even when the other methods are given the true scale. In the harder case of different objects in the same category, GSA vastly surpasses them, providing the first effective solution for category-level 3DGS registration and unlocking new applications. Project webpage: https://bgu-cs-vil.github.io/GSA-project

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:591e23f981ac795111ef625c7fb31d99CVF · CVPR 2026

Recover to Predict: Progressive Retrospective Learning for Variable-Length Trajectory Prediction

Zhou, Hao, Qi, Lu, Li, Xiangtai, Zhang, Jie, Liu, Yi, Yang, Xu, Fan, Mingyu, Luo, Fei

原文摘要

Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic. Most existing methods optimize prediction accuracy under fixed-length observations. However, real-world driving often yields variable-length, incomplete observations, posing a challenge to these methods. A common strategy is to directly map features from incomplete observations to those from complete ones. This one-shot mapping, however, struggles to learn accurate representations for short trajectories due to significant information gaps. To address this issue, we propose a Progressive Retrospective Framework (PRF), which gradually aligns features from incomplete observations with those from complete ones via a cascade of retrospective units. Each unit consists of a Retrospective Distillation Module (RDM) and a Retrospective Prediction Module (RPM), where RDM distills features and RPM recovers previous timesteps using the distilled features. Moreover, we propose a Rolling-Start Training Strategy (RSTS) that enhances data efficiency during PRF training. PRF is plug-and-play with existing methods. Extensive experiments on datasets Argoverse 2 and Argoverse 1 demonstrate the effectiveness of PRF. Code is available at https://github.com/zhouhao94/PRF.

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:5920303f89a4a46d50d1c98876b18e59CVF · CVPR 2026

RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework

Wang, Xiao, Wang, Haiyang, Wang, Shiao, Chen, Qiang, Jin, Jiandong, Song, Haoyu, Jiang, Bo, Li, Chenglong

原文摘要

Existing pedestrian attribute recognition methods are generally developed based on RGB frame cameras. However, these approaches are constrained by the limitations of RGB cameras, such as sensitivity to lighting conditions and motion blur, which hinder their performance. Furthermore, current attribute recognition primarily focuses on analyzing pedestrians' external appearance and clothing, lacking an exploration of emotional dimensions. In this paper, we revisit these issues and propose a novel multi-modal RGB-Event attribute recognition task by drawing inspiration from the advantages of event cameras in low-light, high-speed, and low-power consumption. Specifically, we introduce the first large-scale multi-modal pedestrian attribute recognition dataset, termed EventPAR, comprising 100K paired RGB-Event samples that cover 50 attributes related to both appearance and six human emotions, diverse scenes, and various seasons. By retraining and evaluating mainstream PAR models on this dataset, we establish a comprehensive benchmark and provide a solid foundation for future research in terms of data and algorithmic baselines. In addition, we propose a novel RWKV-based multi-modal pedestrian attribute recognition framework, featuring an RWKV visual encoder and an asymmetric RWKV fusion module. Extensive experiments are conducted on our proposed dataset as well as two simulated datasets (MARS-Attribute and DukeMTMC-VID-Attribute), achieving state-of-the-art results. The source code and dataset will be released upon acceptance.

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:592309253983612a00adddd228fc8ca3CVF · CVPR 2026

Dynamics-Aware Preference Optimization for Vision-Language Models

Zhang, Jusheng, Cai, Kaitong, Yang, Jing, Wang, Jian, Wang, Keze

原文摘要

Preference-based finetuning of vision-language models (VLMs) is notoriously unstable, as trivially wrong negatives inject uninformative gradients that distort optimization and degrade calibration. This work revisits this issue through the lens of learning dynamics and identifies a core pathology, the squeezing effect, where easy negatives retain large, misaligned gradients despite having negligible loss.To address this, we propose Cooling-Weighted Direct Preference Optimization (CW-DPO), a two-stage framework that first smooths and then stabilizes the alignment process. Stage 1 employs a constrained SFT phase with low-weight "gentle negatives" to regularize overconfident distributions and flatten the loss landscape. Stage 2 introduces a competence-aware cooling weight that adaptively scales negative gradients according to the model's average per-token log-probability, suppressing uninformative updates while emphasizing hard, on-policy contrasts. This dynamics-aware weighting effectively mitigates the squeezing effect and enables smoother convergence.Extensive experiments on mainstream benchmarks--including COCO, Flickr30k, NoCaps, MMMU, and MMBench1.1--show that CW-DPO achieves state-of-the-art performance, for example +3.4 CIDEr over PPO and +2.4% absolute accuracy on MMMU, while improving calibration and halving convergence steps. This demonstrates that smoothing before cooling constitutes a simple yet general principle for robust VLM preference optimization.

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:59364bb0a925da633405baf4f944ec95CVF · CVPR 2026

MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectioanl Blending with Hierarchical Densification

Kwak, Sangwoon, Kwon, Weeyoung, Jeong, Jun Young, Kim, Geonho, Cheong, Won-Sik, Oh, Jihyong

原文摘要

Recent advances in 4D Gaussian Splatting (4DGS) have extended the high-speed rendering capability of 3D Gaussian Splatting (3DGS) into the temporal domain, enabling real-time rendering of dynamic scenes.However, one of the major remaining challenges lies in modeling long-range motion-contained dynamic videos, where a naive extension of existing methods leads to severe memory explosion, temporal flickering, and failure to handle appearing or disappearing occlusions over time. To address these challenges, we propose a novel 4DGS framework characterized by an Anchor Relay-based Bidirectional Blending (ARBB) mechanism, named MoRel, which enables temporally consistent and memory-efficient modeling of long-range dynamic scenes.Our method progressively constructs locally canonical anchor spaces at key-frame time index and models inter-frame deformations at the anchor level, enhancing temporal coherence. By learning bidirectional deformations between KfA and adaptively blending them through learnable opacity control, our approach mitigates temporal discontinuities and flickering artifacts.We further introduce a Feature-variance-guided Hierarchical Densification (FHD) scheme that effectively densifies KfA's while keeping rendering quality, based on an assigned level of feature-variance.To effectively evaluate our model's capability to handle real-world long-range 4D motion, we newly compose long-range 4D motion-contained dataset, called SelfCap_ \text LR . It has larger average dynamic motion magnitude, captured at spatially wider spaces, compared to previous dynamic video datasets.Overall, our MoRel achieves temporally coherent and flicker-free long-range 4D reconstruction while maintaining bounded memory usage, demonstrating both scalability and efficiency in dynamic Gaussian-based representations. The code and project page will be publicly 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:59395b7f5527374714380e4d40532866CVF · CVPR 2026

Depth Peeling for High-Fidelity Gaussian-Enhanced Surfel Rendering

Ye, Keyang, Wu, Hongzhi, Zhou, Kun

原文摘要

Novel view synthesis has been significantly advanced by NeRFs and 3D Gaussian Splatting (3DGS), which require ordering volumetric samples or primitives for correct color blending. While the recent Gaussian-Enhanced Surfels (GES) enable high-performance, sort-free rendering, they suffer from aliasing artifacts and suboptimal reconstruction. To address these limitations, we propose DP-GES, a novel representation that combines 2D opaque surfels with semi-transparent boundaries to represent coarse-scale geometry and appearance, and 3D Gaussians surrounding the surfels to supplement fine-scale details. We employ Depth Peeling to achieve accurate per-pixel ordering for surfel rendering, which enables sort-free Gaussian splatting with correct transmittance modulation, effectively eliminating aliasing and popping artifacts while facilitating a fully differentiable joint optimization. Extensive experiments demonstrate that our method achieves superior novel view synthesis quality and compares favorably against state-of-the-art techniques across a wide range of scenes.

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:5950466eba9b62356612abc7f38daae0CVF · CVPR 2026

SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception

Xie, Mingjie, He, Guangjun, Xu, Dongli, Lin, Youtian, Li, Hongjue, Feng, Pengming, Guan, Jian, Deng, Yue

原文摘要

Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge. Despite the remarkable progress of recent CLIP-based approaches, we identify a critical limitation: synonym-induced grounding inconsistency, where semantically equivalent expressions yield disparate spatial attention patterns. This inconsistency undermines the robustness and performance of existing methods in real-world OVDP applications. To address this issue, we propose SynCLIP, a Synonym-Coherent Language-Image Pretraining framework that enhances synonym-robust grounding for OVDP. SynCLIP introduces a Semantic-consistent Spatial Attention alignment (SSA) module to enhance spatial attention consistency by minimizing discrepancies between attention maps of original and synonymous expressions. Furthermore, a Spatial Attention Refinement (SAR) module selectively strengthens the most semantically relevant spatial regions within aligned maps for more precise and stable grounding. To support synonym-coherent pretraining, we also construct a Synonym-Enriched Visual Corpus (SEViC), which augments each category with multiple synonyms and textual definitions. Extensive experiments on multiple benchmarks demonstrate that SynCLIP substantially improves grounding consistency under diverse linguistic variants and achieves state-of-the-art performance among CLIP-based OVDP methods. Code is available at https://github.com/Justlovesmile/SynCLIP.

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:597260d1c81d921850237a8d8890065bCVF · CVPR 2026

DFD-HR: Generalizable Deepfake Detection via Hierarchical Routing Learning

Sun, Jiamu, Yan, Zhiyuan, Zhang, Ke-Yue, Yao, Taiping, Ding, Shouhong

原文摘要

Developing generalizable deepfake detectors has become increasingly important with the rapid advancement of generative models. Adapting visual foundation models (VFMs), e.g., CLIP, through parameter-efficient finetuning (PEFT), with only a small subset of parameters updated, has been proven highly effective for generalizable detection. However, the success of "fewer-parameters" training raises an important question: although only a few parameters are tuned, have existing PEFT-based detectors truly exploited the most informative ones while eliminating redundant parameters for better generalization? In this work, we move beyond standard PEFT by proposing a joint optimization strategy that operates at both the layer and token levels. Since latent features across layers capture different semantic abstractions and tokens within the same layer convey varied forgery cues, we propose integrating both layer-level and token-level routing to maximize representational synergy. Specifically, at the layer level, we introduce "Early Layer Pruning", an adaptive truncation mechanism that enables the model to adaptively learn distinct forward depths for different types of instances. At the token level, "Token Selection" is guided by the Spearman rank loss to filter tokens irrelevant to forgery learning, enabling the model to focus on the most discriminative cues. Furthermore, a unified MoE architecture is applied that encourages diversity and thus reduces the potential model's overfitting to specific forgery types. Extensive benchmarking results demonstrate the effectiveness of our designs and show the superior performance of our method over existing state-of-the-arts.

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:597aca875da46d2a188c376731744514CVF · CVPR 2026

MuCo: Multi-turn Contrastive Learning for Multimodal Embedding Model

Gu, Geonmo, Heo, Byeongho, Yu, Jaemyung, Hwang, Jaehui, Kim, Taekyung, Lee, Sangmin, Jun, HeeJae, Kang, Yoohoon 等 10 位作者

原文摘要

Universal Multimodal embedding models built on Multimodal Large Language Models (MLLMs) have traditionally employed contrastive learning, which aligns representations of query-target pairs across different modalities. Yet, despite its empirical success, they are primarily built on a "single-turn" formulation where each query-target pair is treated as an independent data point. This paradigm leads to computational inefficiency when scaling, as it requires a separate forward pass for each pair and overlooks potential contextual relationships between multiple queries that can relate to the same context. In this work, we introduce Multi-Turn Contrastive Learning (MuCo), a dialogue-inspired framework that revisits this process. MuCo leverages the conversational nature of MLLMs to process multiple, related query-target pairs associated with a single image within a single forward pass. This allows us to extract a set of multiple query and target embeddings simultaneously, conditioned on a shared context representation, amplifying the effective batch size and overall training efficiency. Experiments exhibit MuCo with a newly curated 5M multimodal multi-turn dataset (M3T), which yields state-of-the-art retrieval performance on MMEB and M-BEIR benchmarks, while markedly enhancing both training efficiency and representation coherence across modalities. Code and M3T are available at https://github.com/naver-ai/muco

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:599b16c0314ca441cc01fec508c79bb1CVF · CVPR 2026

Seeing What Matters: Visual Preference Policy Optimization for Visual Generation

Ni, Ziqi, Liang, Yuanzhi, Li, Rui, Zhou, Yi, Huang, Haibin, Zhang, Chi, Li, Xuelong

原文摘要

Reinforcement learning (RL) has become a powerful tool for post-training visual generative models, with Group Relative Policy Optimization (GRPO) increasingly used to align generators with human preferences. However, existing GRPO pipelines rely on a single scalar reward per sample, treating each image or video as a holistic entity and ignoring the rich spatial and temporal structure of visual content. This coarse supervision hinders the correction of localized artifacts and the modeling of fine-grained perceptual cues. We introduce Visual Preference Policy Optimization (ViPO), a GRPO variant that lifts scalar feedback into structured, pixel-level advantages. ViPO employs a Perceptual Structuring Module that uses pretrained vision backbones to construct spatially and temporally aware advantage maps, redistributing optimization pressure toward perceptually important regions while preserving the stability of standard GRPO. Across both image and video benchmarks, ViPO consistently outperforms vanilla GRPO, improving in-domain alignment with human-preference rewards and enhancing generalization on out-of-domain evaluations. The method is architecture-agnostic, lightweight, and fully compatible with existing GRPO training pipelines, providing a more expressive and informative learning signal for visual generation.

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:599d62a0bd069b4f055e6c9a11dc08d0CVF · CVPR 2026

Dual Ascent Diffusion for Inverse Problems

Kim, Minseo, Levy, Axel, Wetzstein, Gordon

原文摘要

Ill-posed inverse problems are fundamental in many domains, ranging from astrophysics to medical imaging. Emerging diffusion models provide a powerful prior for solving these problems. Existing maximum-a-posteriori (MAP) or posterior sampling approaches, however, rely on different computational approximations, leading to inaccurate or suboptimal samples. To address this issue, we introduce a new approach to solving MAP problems with diffusion model priors using a dual ascent optimization framework. Our framework achieves better image quality as measured by various metrics for image restoration problems, it is more robust to high levels of measurement noise, it is faster, and it estimates solutions that represent the observations more faithfully than the state of the art.

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:59a38faeedc151553e0156a45c87680aCVF · CVPR 2026

Fighting Hallucinations with Counterfactuals: Diffusion-Guided Perturbations for LVLM Hallucination Suppression

Dastmalchi, Hamidreza, An, Aijun, Cheraghian, Ali, Barzamini, Hamed

原文摘要

While large vision-language models (LVLMs) achieve strong performance on multimodal tasks, they frequently generate hallucinations--unfaithful outputs misaligned with the visual input. To address this issue, we introduce CIPHER (Counterfactual Image Perturbations for Hallucination Extraction and Removal), a training-free method that suppresses vision-induced hallucinations via lightweight feature-level correction. Unlike prior training-free approaches that primarily focus on text-induced hallucinations, CIPHER explicitly targets hallucinations arising from the visual modality. CIPHER operates in two phases. In the offline phase, we construct OHC-25K (Object-Hallucinated Counterfactuals, 25,000 samples), a counterfactual dataset consisting of diffusion-edited images that intentionally contradict the original ground-truth captions. We pair these edited images with the unchanged ground-truth captions and process them through an LVLM to extract hallucination-related representations. Contrasting these representations with those from authentic (image, caption) pairs reveals structured, systematic shifts spanning a low-rank subspace characterizing vision-induced hallucination. In the inference phase, CIPHER suppresses hallucinations by projecting intermediate hidden states away from this subspace. Experiments across multiple benchmarks show that CIPHER significantly reduces hallucination rates while preserving task performance, demonstrating the effectiveness of counterfactual visual perturbations for improving LVLM faithfulness.

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:59b4563d4b76ef8d9575dd2e076f396fCVF · CVPR 2026

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models

Li, Chen, Dong, Shanshan, Qiu, Sheng, Han, Jianmin, Zhao, Yibo, Gao, Zan, Komura, Taku, Huang, Kemeng

原文摘要

Reconstructing dynamic fluids from sparse views is a long-standing and challenging problem, due to the severe lack of 3D information from insufficient view coverage. While several pioneering approaches have attempted to address this issue using differentiable rendering or novel view synthesis, they are often limited by time-consuming optimization under ill-posed conditions. We propose SmokeSVD, an efficient and effective framework to progressively reconstruct dynamic smoke from a single video by integrating the generative capabilities of diffusion models with physically guided consistency optimization. Specifically, we first propose a physically guided side-view synthesizer based on diffusion models, which explicitly incorporates velocity field constraints to generate spatio-temporally consistent side-view images frame by frame, significantly alleviating the ill-posedness of single-view reconstruction. Subsequently, we iteratively refine novel-view images and reconstruct 3D density fields through a progressive multi-stage process that renders and enhances images from increasing viewing angles, generating high-quality multi-view sequences. Finally, we estimate fine-grained density and velocity fields via differentiable advection by leveraging the Navier-Stokes equations. Our approach supports re-simulation and downstream applications while achieving superior reconstruction quality and computational efficiency compared to state-of-the-art methods.

cs.CV

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

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Dual-Granularity Memory for Efficient Video Generation

Wang, Hongjun, Liu, Lin, Li, Jianguo, Lin, Tao

原文摘要

Video generation using recurrent architectures offers compelling efficiency advantages over attention-based transformers, particularly for long-sequence generation. However, chunked processing in recurrent models creates temporal discontinuities that harm long-range consistency. We introduce two complementary memory mechanisms to address this challenge at different granularities: (1) Context Memory maintains persistent global context within attention chunks through learnable sink columns and boundary buffers, adding only 150K parameters (\textless 0.1% overhead); (2) Latent Context-as-Memory (LCaM) extends memory across video segments by storing and retrieving historical latent embeddings, enabling cross-segment consistency without requiring camera annotations or frame reconstruction. Applied to Generalized Spatial-temporal Propagation Networks (GSTPN), our dual-memory approach achieves 1.54xfaster inference than attention-based transformers, while excelling in visual quality metrics. Our approach is particularly effective for knowledge distillation scenarios where only pre-extracted latent embeddings are available. This work demonstrates compelling efficiency-quality trade-offs for practical long video generation.

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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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Illuminating Visual Identity in Universal Multimodal Embeddings

Cao, Jiawei, Feng, Junyi, Hua, Jiashen, Huang, Ziheng, Deng, Bing, Wu, Kaijie, Gu, Chaochen, Ye, Jieping

原文摘要

Universal Multimodal Embeddings (UMEs) aim to unify various modalities and tasks into a shared representation space. In recent years, this field has witnessed substantial progress driven by the development of Multimodal Large Language Models (MLLMs). However, a crucial capability, visual identity discrimination, remains underexplored in existing UME methods, despite its critical role in a wide range of tasks, including instance retrieval, re-identification, and identity preservation in AI-generated content. To bridge this gap, we propose a unified formulation for visual identity discrimination (VisID) and introduce MVEB (Multimodal Visual Identity Embedding Benchmark), a large-scale benchmark curated from both real-world and synthetic datasets to support evaluation and training. Furthermore, we present a simple yet effective learning framework that jointly optimizes general multimodal and visual identity representations through a carefully designed identity-aware sampling mechanism. Extensive experiments demonstrate that our approach successfully endows UMEs with strong identity discrimination capability and maintains competitive general multimodal performance. We believe this work not only illuminates a critical yet neglected capability, but also takes a step toward more holistic universal multimodal embeddings. Code and data are available at \href https://chrisclear3.github.io/MVEB MVEB .

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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AnchorFlow: Training-Free 3D Editing via Latent Anchor-Aligned Flows

Zhou, Zhenglin, Ma, Fan, Gui, Chengzhuo, Xia, Xiaobo, Fan, Hehe, Yang, Yi, Chua, Tat-Seng

原文摘要

Training-free 3D editing aims to modify 3D shapes based on human instructions without model finetuning. It plays a crucial role in 3D content creation. However, existing approaches often struggle to produce strong or geometrically stable edits, largely due to inconsistent latent anchors introduced by timestep-dependent noise during diffusion sampling. To address these limitations, we introduce AnchorFlow, which is built upon the principle of latent anchor consistency. Specifically, AnchorFlow establishes a global latent anchor shared between the source and target trajectories, and enforces coherence using a relaxed anchor-alignment loss together with an anchor-aligned update rule. This design ensures that transformations remain stable and semantically faithful throughout the editing process. By stabilizing the latent reference space, AnchorFlow enables more pronounced semantic modifications. Moreover, AnchorFlow is mask-free. Without mask supervision, it effectively preserves geometric fidelity. Experiments on the Eval3DEdit benchmark show that AnchorFlow consistently delivers semantically aligned and structurally robust edits across diverse editing types. Project page: https://zhenglinzhou.github.io/AnchorFlow/.

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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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Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images

Ji, Yikun, Hong, Yan, Deng, Bowen, Lan, Jun, Zhu, Huijia, Wang, Weiqiang, Zhang, Liqing, Zhang, Jianfu

原文摘要

The rapid growth of AI-generated imagery has blurred the boundary between real and synthetic content, raising practical concerns for digital integrity. Vision-language models (VLMs) can provide natural language explanations, but standard one-pass classifiers often miss subtle artifacts in high-quality synthetic images and offer limited grounding in the pixels. We propose Locate-Then-Examine (LTE), a two-stage VLM-based forensic framework that first localizes suspicious regions and then re-examines these crops together with the full image to refine the real vs. AI-generated verdict and its explanation. LTE explicitly links each decision to localized visual evidence through region proposals and region-aware reasoning. To support training and evaluation, we introduce TRACE, a dataset of 20,000 real and high-quality synthetic images with region-level annotations and automatically generated forensic explanations, constructed by a VLM-based pipeline with additional consistency checks and quality control. Across TRACE and multiple external benchmarks, LTE achieves competitive accuracy and improved robustness while providing human-understandable, region-grounded explanations suitable for forensic deployment.

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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CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization

Chen, Weilin, Rao, Jiahao, Wang, Wenhao, Li, Xinyang, Cheng, Xuan, Cao, Liujuan

原文摘要

The creation of high-fidelity, customizable 3D indoor scene textures remains a significant challenge. While text-driven methods offer flexibility, they lack the precision for fine-grained, instance-level control, and often produce textures with insufficient quality, artifacts, and baked-in shading. To overcome these limitations, we introduce CustomTex, a novel framework for instance-level, high-fidelity scene texturing driven by reference images. CustomTex takes an untextured 3D scene and a set of reference images specifying the desired appearance for each object instance, and generates a unified, high-resolution texture map. The core of our method is a dual-distillation approach that separates semantic control from pixel-level enhancement. We employ semantic-level distillation, equipped with an instances cross attention, to ensure semantic plausibility and "reference-instance" alignment, and pixel-level distillation to enforce high visual fidelity. Both are unified within a Variational Score Distillation optimization framework. Experiments demonstrate that CustomTex achieves precise instance-level consistency with reference images and produces textures with superior sharpness, reduced artifacts, and minimal baked-in shading compared to state-of-the-art methods. Our work establishes a more direct and user-friendly path to high-quality, customizable 3D scene appearance editing.

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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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DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction

Wang, Yufu, Ng, Evonne, Shin, Soyong, Khirodkar, Rawal, Dong, Yuan, Su, Zhaoen, Park, Jinhyung, Kitani, Kris 等 11 位作者

原文摘要

We present DuoMo, a generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Reconstructing such motion requires solving a fundamental trade-off: generalizing from diverse and noisy video inputs while maintaining global motion consistency. Our approach addresses this problem by factorizing motion learning into two diffusion models. The camera-space model first estimates motion from videos in camera coordinates. The world-space model then lifts this initial estimate into world coordinates and refines it to be globally consistent. Together, the two models can reconstruct motion across diverse scenes and trajectories, even from highly noisy or incomplete observations. Moreover, our formulation is general, generating the motion of mesh vertices directly (bypassing parametric models). DuoMo achieves state-of-the-art performance. On EMDB, our method obtains a 16% reduction in world-space MPJPE error while maintaining low foot skating. On RICH, it obtains a 30% reduction in world-space MPJPE error. Project page: yufu-wang.github.io/duomo/

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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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ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

Siddiqui, Yawar, Frost, Duncan, Aroudj, Samir, Avetisyan, Armen, Howard-Jenkins, Henry, DeTone, Daniel, Moulon, Pierre, Wu, Qirui 等 12 位作者

原文摘要

Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casually captured sequences. Given a image sequence, we leverage off-the-shelf visual-inertial SLAM,3D detection algorithms and VLMs to extract for each object, a set of sparse SLAM points, posed multi-view images, and machine-generated captions. A rectified flow transformer trained to effectively condition on these modalities then generates high-fidelity metric 3D shapes. To ensure robustness to the challenges of casually captured data, we employ a range of techniques including on-the-fly compositional augmentations, a curriculum training scheme spanning object- and scene-level datasets, and strategies to handle background clutter. Additionally, we introduce a new evaluation benchmark comprising 178 in the wild objects across 7 real-world scenes with geometry annotations. Experiments show that ShapeR significantly outperforms existing approaches in this challenging setting, achieving an improvement of 2.7x in Chamfer distance compared to SoTA.

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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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The Missing Point in Vision Transformers for Universal Image Segmentation

Shahabodini, Sajjad, Mansoori, Mobina, Bayatmakou, Farnoush, Abouei, Jamshid, Plataniotis, Konstantinos, Mohammadi, Arash

原文摘要

Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, accurately classifying these masks, especially in the presence of ambiguous boundaries and imbalanced class distributions, remains an open challenge. In this work, we introduce ViT-P, a novel two-stage segmentation framework that decouples mask generation from classification. The first stage employs a proposal generator to produce class-agnostic mask proposals, while the second stage utilizes a point-based classification model built on the Vision Transformer (ViT) to refine predictions. ViT-P serves as a pre-training-free adapter, allowing the integration of various pre-trained vision transformers without modifying their architecture, ensuring adaptability to dense prediction tasks. Furthermore, we demonstrate that coarse and bounding box annotations can effectively enhance classification without requiring additional training on fine annotation datasets, reducing annotation costs while maintaining strong performance. Extensive experiments across COCO, ADE20K, and Cityscapes datasets validate the effectiveness of ViT-P, achieving state-of-the-art results with 54.0 PQ on ADE20K panoptic segmentation, 87.4 mIoU on Cityscapes semantic segmentation, and 63.6 mIoU on ADE20K semantic segmentation.

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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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CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation

Huang, Kaiyi, Huang, Yukun, Li, Yu, Bai, Jianhong, Wang, Xintao, Lin, Zinan, Ning, Xuefei, Yu, Jiwen 等 10 位作者

原文摘要

Cinematic video production requires control over scene-subject composition and camera movement, but live-action shooting remains costly due to the need for constructing physical sets. To address this, we introduce the task of cinematic video generation with decoupled scene context: given multiple images of a static environment, the goal is to synthesize high-quality videos featuring dynamic subject while preserving the underlying scene consistency and following a user-specified camera trajectory. We present CineScene, a framework that leverages implicit 3D-aware scene representation for cinematic video generation. Our key innovation is a novel context conditioning mechanism that injects 3D-aware features in an implicit way: By encoding scene images into visual representations through VGGT, CineScene injects spatial priors into a pretrained text-to-video generation model by additional context concatenation, enabling camera-controlled video synthesis with consistent scenes and dynamic subjects. To further enhance the model's robustness, we introduce a simple yet effective random-shuffling strategy for the input scene images during training. To address the lack of training data, we construct a scene-decoupled dataset with Unreal Engine 5, containing paired videos of scenes with and without dynamic subjects, panoramic images representing the underlying static scene, along with their camera trajectories. Experiments show that CineScene achieves state-of-the-art performance in scene-consistent cinematic video generation, handling large camera movements and demonstrating generalization across diverse environments.

cs.CV

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

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OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation

Li, Han, Peng, Xinyu, Wang, Yaoming, Peng, Zelin, Chen, Xin, Weng, Rongxiang, Wang, Jingang, Cai, Xunliang 等 10 位作者

原文摘要

We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a single decoder-only transformer architecture. OneCAT uniquely eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference, leading to significant efficiency gains, especially for high-resolution image inputs and outputs. This is achieved through a modality-specific Mixture-of-Experts (MoE) design trained with a unified autoregressive (AR) objective, which also natively supports dynamic resolutions. Furthermore, we pioneer to achieve multi-scale visual autoregressive mechanism within the Large Language Model (LLM) with proposed scale-aware adapter (SAA) that drastically reduces decoding latency compared to diffusion-based methods while maintaining state-of-the-art performance. Our findings demonstrate the powerful potential of pure autoregressive modeling as an elegant foundation for unified multimodal intelligence. As a result, OneCAT outperforms existing unified models across benchmarks for multimodal understanding, generation, and editing.

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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FlashPortrait: 6x Faster Infinite Portrait Animation with Adaptive Latent Prediction

Tu, Shuyuan, Pan, Yueming, Huang, Yinming, Han, Xintong, Xing, Zhen, Dai, Qi, Qiu, Kai, Luo, Chong 等 9 位作者

原文摘要

Current diffusion-based acceleration methods for long-portrait animation struggle to ensure identity (ID) consistency. This paper presents FlashPortrait, an end-to-end video diffusion transformer capable of synthesizing ID-preserving, infinite-length videos while achieving up to 6xacceleration in inference speed. In particular, FlashPortrait begins by computing the identity-agnostic facial expression features with an off-the-shelf extractor. It then introduces a Normalized Facial Expression Block to align facial features with diffusion latents by normalizing them with their respective means and variances, thereby improving identity stability in facial modeling. During inference, FlashPortrait adopts a dynamic sliding-window scheme with weighted blending in overlapping areas, ensuring smooth transitions and ID consistency in long animations. In each context window, based on the latent variation rate at particular timesteps and the derivative magnitude ratio among diffusion layers, FlashPortrait utilizes higher-order latent derivatives at the current timestep to directly predict latents at future timesteps, thereby skipping several denoising steps and achieving 6xspeed acceleration. Experiments on benchmarks show the effectiveness of FlashPortrait both qualitatively and quantitatively.

cs.CV

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

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CG-Floor: Centroid-Guided Diffusion for Large-Scale Floorplan Generation

Lian, Hongjin, Ma, Jian, Chen, Hongjie, Li, Jia, Hu, Ruizhen, Lai, Yu-Kun, Li, Kun

原文摘要

Large-scale floorplan generation is critical for virtual space planning and architectural simulation. Although existing methods have shown success in generating small-scale floorplans with simple room shapes, they struggle to handle complex room connections and irregular room shapes that arise in large-scale floorplans. In this paper, we propose CG-Floor, a centroid-guided hierarchical framework that explicitly decouples room position and shape generation to address these issues. We first introduce the size-aware semantic centroid heatmap, derived from predicted room centroids and sizes, which provides a structured representation to guide the effective generation of a coarse-to-fine floorplan generator while ensuring semantic alignment. Additionally, we train a vector quantized codebook of floorplans with complex room shapes to capture the diversity of room shapes and employ a latent diffusion transformer to generate large-scale floorplans featuring non-Manhattan room shapes. CG-Floor achieves state-of-the-art performance on the large-scale MSD dataset, and supports 3D floorplan conversion and editing, demonstrating the practicality of our approach. The code is available at https://cic.tju.edu.cn/faculty/likun/projects/CG-Floor.

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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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MoRGS: Efficient Per-Gaussian Motion Reasoning for Streamable Dynamic 3D Scenes

Lee, Wonjoon, Woo, Sungmin, Kim, Donghyeong, Lee, Jungho, Park, Sangheon, Lee, Sangyoun

原文摘要

Online reconstruction of dynamic scenes aims to learn from streaming multi-view inputs under low-latency constraints. The fast training and real-time rendering capabilities of 3D Gaussian Splatting have made on-the-fly reconstruction practically feasible, enabling online 4D reconstruction. However, existing online approaches, despite their efficiency and visual quality, fail to learn per-Gaussian motion that reflects true scene dynamics. Without explicit motion cues, appearance and motion are optimized solely under photometric loss, causing per-Gaussian motion to chase pixel residuals rather than true 3D motion. To address this, we propose MoRGS, an efficient online per-Gaussian motion reasoning framework that treats Gaussian movement as a core modeling object. Specifically, we efficiently leverage optical flow on a sparse set of key views as a lightweight motion cue to guide per-Gaussian motion toward the scene's true dynamics. To compensate for the sparsity and view-dependence of flow, we learn a per-Gaussian motion offset field that reconciles discrepancies between projected 3D motion and observed flow across views and time. In addition, we introduce a per-Gaussian motion confidence that separates dynamic from static Gaussians and weights Gaussian attribute residual updates, thereby suppressing redundant motion in static regions for better temporal consistency and accelerating the modeling of large motions. Extensive experiments demonstrate that MoRGS achieves state-of-the-art reconstruction quality and motion fidelity among online methods, while maintaining streamable performance.

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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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