GenAI4ELab/papercli-papers huggingface dataset:
Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
Authors: Yu Wang; Shengjie Zhao
Venue: CVPR 2026 (main)

Abstract: Weakly supervised video anomaly detection (WS-VAD) involves identifying the temporal intervals that contain anomalous events in untrimmed videos, where only video-level annotations are provided as supervisory signals. However, a key limitation persists in WS-VAD, as dense frame-level annotations are absent, which often leaves existing methods struggling to learn anomaly semantics effectively. To address this issue, we propose a novel framework named LAS-VAD, short for Learning Anomaly Semantics for WS-VAD, which integrates anomaly-connected component mechanism and intention awareness mechanism. The former is designed to assign video frames into distinct semantic groups within a video, and frame segments within the same group are deemed to share identical semantic information. The latter leverages an intention-aware strategy to distinguish between similar normal and abnormal behaviors (e.g., taking items and stealing). To further model the semantic information of anomalies, as anomaly occurrence is accompanied by distinct characteristic attributes (i.e., explosions are characterized by flames and thick smoke), we additionally incorporate anomaly attribute information to guide accurate detection. Extensive experiments on two benchmark datasets, XD-Violence and UCF-Crime, demonstrate that our LAS-VAD outperforms current state-of-the-art methods with remarkable gains.
Duala: Dual-Level Alignment of Subjects and Stimuli for Cross-Subject fMRI Decoding
Authors: Shumeng Li; Jintao Guo; Jian Zhang; Yulin Zhou; Luyang Cao; Yinghuan Shi
Venue: CVPR 2026 (main)

Abstract: Cross-subject visual decoding aims to reconstruct visual experiences from brain activity across individuals, enabling more scalable and practical brain-computer interfaces. However, existing methods often suffer from degraded performance when adapting to new subjects with limited data, as they struggle to preserve both the semantic consistency of stimuli and the alignment of brain responses. To address these challenges, we propose Duala, a dual-level alignment framework designed to achieve stimulus-level consistency and subject-level alignment in fMRI-based cross-subject visual decoding. (1) At the stimulus level, Duala introduces a semantic alignment and relational consistency strategy that preserves intra-class similarity and inter-class separability, maintaining clear semantic boundaries during adaptation. (2) At the subject level, a distribution-based feature perturbation mechanism is developed to capture both global and subject-specific variations, enabling adaptation to individual neural representations without overfitting. Experiments on the Natural Scenes Dataset (NSD) demonstrate that Duala effectively improves alignment across subjects. Remarkably, even when fine-tuned with only about one hour of fMRI data, Duala achieves over 81.1% image-to-brain retrieval accuracy and consistently outperforms existing fine-tuning strategies in both retrieval and reconstruction. Our code is available at https://github.com/ShumengLI/Duala.
MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis
Authors: Di Luo; Shuhui Yang; Mingxin Yang; Jiawei Lu; Yixuan Tang; Xintong Han; Zhuo Chen; Beibei Wang; Chunchao Guo
Venue: CVPR 2026 (main)

Abstract: Physically-based rendering (PBR) materials are fundamental to photorealistic graphics, yet their creation remains labor-intensive and requires specialized expertise. While generative models have advanced material synthesis, existing methods lack a unified representation bridging natural image appearance and PBR properties, leading to fragmented task-specific pipelines and inability to leverage large-scale RGB image data. We present MatPedia, a foundation model built upon a novel joint RGB-PBR representation that compactly encodes materials into two interdependent latents: one for RGB appearance and one for the four PBR maps encoding complementary physical properties. By formulating them as a 5-channel sequence and employing video diffusion architectures, MatPedia naturally captures their correlations while transferring visual priors from RGB generation models. This joint representation enables a unified framework handling multiple material tasks--text-to-material generation, image-to-material generation, and intrinsic decomposition--within a single architecture. Trained on MatHybrid-410K, a mixed corpus combining PBR datasets with large-scale RGB images, MatPedia achieves native 1024x1024 synthesis that substantially surpasses existing approaches in both quality and diversity.
Affordance Field Intervention: Enabling VLAs to Escape Memory Traps in Robotic Manipulation
Authors: Siyu Xu; Zijian Wang; Yunke Wang; Chenghao Xia; Tao Huang; Chang Xu
Venue: CVPR 2026 (main)

Abstract: Vision-Language-Action (VLA) models have shown great performance in robotic manipulation by mapping visual observations and language instructions directly to actions. However, they remain brittle under distribution shifts: when test scenarios change, VLAs often reproduce memorized trajectories instead of adapting to the updated scene, which is a failure mode we refer to as the "Memory Trap". This limitation stems from the end-to-end design, which lacks explicit 3D spatial reasoning and prevents reliable identification of actionable regions in unfamiliar environments. To compensate for this missing spatial understanding, 3D Spatial Affordance Fields (SAFs) can provide a geometric representation that highlights where interactions are physically feasible, offering explicit cues about regions the robot should approach or avoid. We therefore introduce Affordance Field Intervention (AFI), a lightweight hybrid framework that uses SAFs as an on-demand plug-in to guide VLA behavior. Our system detects memory traps through proprioception, repositions the robot to recent high-affordance regions, and proposes affordance-driven waypoints that anchor VLA-generated actions. A SAF-based scorer then selects trajectories with the highest cumulative affordance. Extensive experiments demonstrate that our method achieves an average improvement of 23.5% across different VLA backbones (pi-0 and pi-0.5) under out-of-distribution scenarios on real-world robotic platforms, and 20.2% on the LIBERO-Pro benchmark, validating its effectiveness in enhancing VLA robustness to distribution shifts. Project page: https://vla-afi.github.io/.
CSF: Black-box Fingerprinting via Compositional Semantics for Text-to-Image Models
Authors: Junhoo Lee; Mijin Koo; Nojun Kwak
Venue: CVPR 2026 (main)

Abstract: Text-to-image models are commercially valuable assets often distributed under restrictive licenses, but such licenses are enforceable only when violations can be detected. Existing methods require pre-deployment watermarking or internal model access, which are unavailable in commercial API deployments. We present Compositional Semantic Fingerprinting (CSF), the first black-box method for attributing fine-tuned text-to-image models to protected lineages using only query access. CSF treats models as semantic category generators and probes them with compositional underspecified prompts that remain rare under fine-tuning. This gives IP owners an asymmetric advantage: new prompt compositions can be generated after deployment, while attackers must anticipate and suppress a much broader space of fingerprints. Across 6 model families (FLUX, Kandinsky, SD1.5/2.1/3.0/XL) and 13 fine-tuned variants, our Bayesian attribution framework enables controlled-risk lineage decisions, with all variants satisfying the dominance criterion.
CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model
Authors: Houji Wen; Jiangyong Yu; Dawei Yang; Jun Li
Venue: CVPR 2026 (main)

Abstract: Segment Anything Models (SAMs) are extensively used in computer vision for universal image segmentation, but deploying them on resource-constrained devices is challenging due to their high computational and memory demands. Post-Training Quantization (PTQ) is a widely used technique for model compression and acceleration. However, existing PTQ methods fail to consider the cross-attention architecture in the SAM decoder. This degradation primarily stems from the unique challenges posed by SAMs: (1) Attention dissipation, where the attention information in the decoder, which is crucial for representing segmentation masks, collapses into a diffuse and non-semantic form under low-bit quantization; and (2) Reconstruction oscillation, where bidirectional coupling within the two-way transformer introduces cross-branch error interference and destabilizes convergence.  To tackle these issues, we propose CAR-SAM, a unified quantization framework tailored for SAMs. Firstly, to mitigate attention dissipation, we introduce MatMul-Aware Compensation (MAC) mechanism that transfers activation-induced quantization errors from MatMul to preceding linear weights. Secondly, to mitigate oscillation in decoder optimization, we develop a Joint Cross-Attention Reconstruction (JCAR) strategy that jointly reconstructs coupled attention branches, suppressing oscillatory behavior and promoting stable convergence. Extensive experiments show that CAR-SAM robustly quantizes SAM models down to 4-bit precision, surpassing existing methods by 14.6% and 6.6% mAP on SAM-B and SAM-L respectively.
Vision Transformers Need More Than Registers
Authors: Cheng Shi; Yizhou Yu; Sibei Yang
Venue: CVPR 2026 (main)

Abstract: Vision Transformers (ViTs), when pre-trained on large-scale data, provide general-purpose representations for diverse downstream tasks. However, artifacts in ViTs are widely observed across different supervision paradigms and downstream tasks. Through systematic analysis of artifacts in ViTs, we find that their fundamental mechanisms have yet to be sufficiently elucidated. In this paper, through systematic analysis, we conclude that these artifacts originate from a lazy aggregation behavior: ViT uses semantically irrelevant background patches as shortcuts to represent global semantics, driven by global attention and Coarse-grained semantic supervision. Our solution selectively integrates patch features into the CLS token, reducing the influence of background-dominated shortcuts and consistently improving performance across 12 benchmarks under label-, text-, and self-supervision. We hope this work offers a new perspective on ViT behavior. All the code and weights will be made publicly available.
Evidential Neural Radiance Fields
Authors: Ruxiao Duan; Alex Wong
Venue: CVPR 2026 (main)

Abstract: Understanding sources of uncertainty is fundamental to trustworthy three-dimensional scene modeling. While recent advances in neural radiance fields (NeRFs) achieve impressive accuracy in scene reconstruction and novel view synthesis, the lack of uncertainty estimation significantly limits their deployment in safety-critical settings. Existing uncertainty quantification methods for NeRFs fail to separately capture both aleatoric and epistemic uncertainties. Among those that do quantify one or the other, many of them either compromise rendering quality or incur significant computational overhead to obtain uncertainty estimates. To address these issues, we introduce Evidential Neural Radiance Fields, a probabilistic approach that seamlessly integrates with the NeRF rendering process, enabling direct quantification of both aleatoric and epistemic uncertainties from a single forward pass. We compare multiple uncertainty quantification methods on three standardized benchmarks, where our approach demonstrates state-of-the-art scene reconstruction fidelity and uncertainty estimation quality. Code is available at https://github.com/KerryDRX/EvidentialNeRF.
ThinkingViT: Matryoshka Thinking Vision Transformer for Elastic Inference
Authors: Ali Hojjat; Janek Haberer; Sören Pirk; Olaf Landsiedel
Venue: CVPR 2026 (main)

Abstract: ViTs deliver SOTA performance, yet their fixed computational budget prevents scalable deployment across heterogeneous hardware. Recent Matryoshka-style Transformer architectures mitigate this by embedding nested subnetworks within a single model to enable scalable inference. However, these models allocate the same amount of compute to all inputs, regardless of their complexity, which leads to inefficiencies. To address this, we introduce ThinkingViT, a nested ViT architecture that employs progressive thinking stages to dynamically adjust inference computation based on input difficulty. ThinkingViT first activates a small subset of the most important attention heads to produce an initial prediction. If the prediction confidence exceeds a predefined threshold, inference terminates early. Otherwise, within the same backbone, it activates a larger subset of attention heads and conducts a new forward pass. This process continues iteratively until the model reaches the predefined confidence level or exhausts its maximum capacity. To boost the performance of subsequent rounds, we introduce a Token Recycling approach that fuses the input embeddings with the embeddings from the previous stage. Experiments show that ThinkingViT surpasses nested baselines by up to 2.0 percentage points (p.p.) in accuracy at the same throughput and by up to 2.9 p.p. at equal GMACs on ImageNet-1K. We show that the backbone-preserving design of ThinkingViT allows it to serve as a plug-in upgrade for ViTs in downstream tasks such as semantic segmentation. We also demonstrate that ThinkingViT transfers effectively to other architectures such as Swin Transformers.
Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation
Authors: Joonhyung Park; Hyeongwon Jang; Joowon Kim; Eunho Yang
Venue: CVPR 2026 (main)

Abstract: Recent visual autoregressive (AR) models have shown promising capabilities in text-to-image generation, operating in a manner similar to large language models. While test-time computation scaling yields higher-quality outputs on challenging natural language tasks, its adaptation to visual AR models remains unexplored and poses unique challenges. Naively applying test-time scaling strategies such as Best-of-N can be suboptimal: they consume full-length computation on erroneous generation trajectories, while the raster-scan decoding scheme lacks a blueprint of the entire canvas, limiting scaling benefits as only a few prompt-aligned candidates are generated. To address these, we introduce GridAR, a test-time scaling framework designed to elicit the best possible results from visual AR models. GridAR employs a grid-partitioned progressive generation scheme in which multiple partial candidates for the same position are generated within a canvas, infeasible ones are pruned early, and viable ones are fixed as anchors to guide subsequent decoding. Coupled with this, we present a layout-specified prompt reformulation strategy that inspects partial views to infer a feasible layout for satisfying the prompt. The reformulated prompt guides subsequent image generation to mitigate the blueprint deficiency. GridAR achieves higher-quality results under limited test-time scaling: with N=4, it even outperforms Best-of-N (N=8) by 14.4% on T2I-CompBench++ while reducing cost by 25.6%. It also generalizes to autoregressive image editing, showing a 14.5% gain in semantic preservation on PIE-Bench over larger-N baselines.
Cluster-Aware Neural Collapse Prompt Tuning for Long-Tailed Generalization of Vision-Language Models
Authors: Boyang Guo; Liang Li; Lin Peng; Yuhan Gao; Xichun Sheng; Chenggang Yan
Venue: CVPR 2026 (main)

Abstract: Prompt learning has emerged as an efficient alternative to fine-tuning pre-trained vision-language models (VLMs).Despite its promise, current methods still struggle to maintain tail-class discriminability when adapting to class-imbalanced datasets. In this work, we propose cluster-aware neural collapse prompt tuning (CPT), which enhances the discriminability of tail classes in prompt-tuned VLMs without sacrificing their overall generalization.First, we design a cluster-invariant space by mining semantic assignments from the pre-trained VLM and mapping them to prompt-tuned features.This computes cluster-level boundaries and restricts the constraints to local neighborhoods, which reduces interference with the global semantic structure of the pre-trained VLM.Second, we introduce neural-collapse-driven discriminability optimization with three losses: textual Equiangular Tight Frame (ETF) separation loss, class-wise convergence loss, and rotation stabilization loss.These losses work together to shape intra-cluster geometry for better inter-class separation and intra-class alignment.Extensive experiments on 11 diverse datasets demonstrate that CPT outperforms SOTA methods, with stronger performance on long-tail classes and good generalization to unseen classes.
Prompt Yourself: Awakening Textual Semantics in 1D Visual Tokenizers
Authors: Hualiang Wang; Siming Fu; Weinan Jia; Yuning Lu; Mu Liu; Jidong Jiang; Xiaomeng Li
Venue: CVPR 2026 (main)

Abstract: One-dimensional (1D) visual tokenizers offer notable semantic compactness by discarding local spatial priors, and have become increasingly popular for image reconstruction and generation tasks. However, such global and sequential representations struggle to preserve fine-grained visual content; simply increasing network size or token count offers only superficial mitigation. To address this, we introduce VLTok, a novel 1D hybrid tokenizer that unifies visual and language representations in a shared token space through a self-prompted training paradigm. During training, VLTok simultaneously generates 1D visual and textual tokens from images, aligning the textual tokens with embeddings from a pre-trained language model. This cross-modal alignment infuses implicit linguistic cues into the tokenizer, enhancing fine-grained image encoding. At inference, the self-prompted paradigm eliminates the need for external text, maintaining the simplicity of the image-only framework while benefiting from multi-modal guidance. Extensive experiments on the ImageNet benchmark demonstrate that VLTok achieves state-of-the-art performance in both image reconstruction and image generation. For example, under the same model parameter budget, our method yields relative reduction of 11.1% in rFID and 18.7% in gFID compared to GigaTok.
Gaussian-Mixture Latent Flow for Stochastic 3D Human Motion Prediction
Authors: Yue Ma; Frederick W. B. Li; Xiaohui Liang
Venue: CVPR 2026 (main)

Abstract: Stochastic human motion prediction aims to forecast future motion distributions. Although recent studies have achieved strong performance in terms of accuracy and diversity, they often overlook plausibility (e.g., resulting in physically unrealistic predictions) and uncertainty quantification, which is essential for real-world applications and downstream tasks. To address these issues, we propose a latent flow-based model equipped with a data-driven Gaussian mixture prior that more effectively disentangles diverse human behaviors than conventional single-modal priors. This prior is derived from patterns in the training data without requiring additional annotations. Furthermore, the fully invertible nature of our model enables natural uncertainty quantification through tractable likelihood computation. Experiments on the Human3.6M and AMASS datasets demonstrate that our approach achieves state-of-the-art performance in both accuracy and plausibility, while also providing reliable uncertainty estimates.
DreamStyle: A Unified Framework for Video Stylization
Authors: Mengtian Li; Jinshu Chen; Songtao Zhao; Wanquan Feng; Pengqi Tu; Qian He
Venue: CVPR 2026 (main)

Abstract: Video stylization, an important downstream task of video generation models, has not yet been thoroughly explored. Its input style conditions typically include text, style image, and stylized first frame. Each condition has a characteristic advantage: text is more flexible, style image provides a more accurate visual anchor, and stylized first frame makes long-video stylization feasible. However, existing methods are largely confined to a single type of style condition, which limits their scope of application. Additionally, their lack of high-quality datasets leads to style inconsistency and temporal flicker. To address these limitations, we introduce DreamStyle, a unified framework for video stylization, supporting (1) text-guided, (2) style-image-guided, and (3) first-frame-guided video stylization, accompanied by a well-designed data curation pipeline to acquire high-quality paired video data. DreamStyle is built on a vanilla Image-to-Video (I2V) model and trained using a Low-Rank Adaptation (LoRA) with token-specific up matrices that reduces the confusion among different condition tokens. Both qualitative and quantitative evaluations demonstrate that DreamStyle is competent in all three video stylization tasks, and outperforms the competitors in style consistency and video quality.
Uncertainty-Aware Modality Fusion for Unaligned RGB-T Salient Object Detection
Authors: Mianzhao Wang; Fan Shi; Xu Cheng; Chen Jia; Shengyong Chen
Venue: CVPR 2026 (main)

Abstract: Unaligned RGB-T salient object detection (SOD) remains challenging due to severe cross-modal spatial discrepancies and unreliable feature fusion. Existing methods often assume perfect alignment or rely on geometric registration, which is computationally demanding and sensitive to cross-modal inconsistencies. To address these limitations, we propose an uncertainty-aware modality fusion network (UMFNet) that reformulates RGB-T SOD as an uncertainty-aware representation learning problem. Specifically, the proposed uncertainty alignment module (UAM) models pixel-wise features as Gaussian latent distributions to estimate local uncertainty and identify cross-modal consistency regions within the feature space, thereby achieving implicit alignment without explicit registration. Furthermore, the confidence-guided global modulation (CGM) mechanism leverages confidence maps derived from uncertainty estimation to adaptively regulate the fusion of RGB and thermal features, enhancing salient cues in reliable regions while suppressing noisy or inconsistent information. Extensive experiments on five unaligned and three aligned RGB-T SOD benchmarks demonstrate that UMFNet achieves state-of-the-art performance across diverse alignment conditions.
MD2E: Modeling Depth-to-Edge Cues for Monocular Metric Depth Estimation
Authors: Chao Ning; Minghe Shen; Naoto Yokoya
Venue: CVPR 2026 (main)

Abstract: We study monocular metric depth estimation (MMDE) without camera intrinsics at training or inference. When focal length and scene depth vary together, depth changes are difficult to perceive in the image, yet the edge-frequency statistics exhibit systematic, scale-correlated shifts. Building on this observation, we introduce a spectral quantile estimator (SQE) that analyzes the Fourier spectrum of a predicted edge map and outputs a single score used as a proxy for metric scale. Consequently, we propose MD2E, a method that models depth-to-edge cues by deriving edge targets from depth annotations, calibrating metric scale using the spectral score, and using edge predictions to regularize depth boundaries while producing metric depth. Across diverse cameras and datasets, MD2E achieves state-of-the-art performance in MMDE in both zero-shot and fine-tuning settings. The project page is available at https://2j472no.github.io/MD2E/.