GenAI4ELab/papercli-papers huggingface dataset:
Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
Authors: Yingxin Lai; Zitong YU; Jun Wang; Linlin Shen; Yong Xu; Xiaochun Cao
Venue: CVPR 2026 (main)

Abstract: Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy, which we attribute to the ecological invalidity of training data. This work introduces Agent4FaceForgery to address two fundamental problems: (1) how to capture the diverse intents and iterative processes of human forgery creation, and (2) how to model the complex, often adversarial, text-image interactions that accompany forgeries in social media. To solve this, we propose a multi-agent framework where LLM-powered agents, equipped with profile and memory modules, simulate the forgery creation process. Crucially, these agents interact in a simulated social environment to generate samples labeled for nuanced text-image consistency, moving beyond simple binary classification. An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity. Extensive experiments validate that the data generated by our simulation-driven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.
DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation
Authors: Zehong Ma; Longhui Wei; Shuai Wang; Shiliang Zhang; Qi Tian
Venue: CVPR 2026 (main)

Abstract: Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the two-stage latent diffusion, offering higher model capacity. Existing pixel diffusion models suffer from slow training and inference, as they usually model both high-frequency signals and low-frequency semantics within a single diffusion transformer (DiT). To pursue a more efficient pixel diffusion paradigm, we propose the frequency-**DeCo**upled pixel diffusion framework. With the intuition to decouple the generation of high and low frequency components, we leverage a lightweight pixel decoder to generate high-frequency details conditioned on semantic guidance from the DiT. This thus frees the DiT to specialize in modeling low-frequency semantics. In addition, we introduce a frequency-aware flow-matching loss that emphasizes visually salient frequencies while suppressing insignificant ones. Extensive experiments show that DeCo achieves superior performance among pixel diffusion models, attaining FID of **1.62** (256x256) and **2.22** (512x512) on ImageNet, closing the gap with latent diffusion methods. Furthermore, our pretrained text-to-image model achieves a leading overall score of 0.86 on GenEval in system-level comparison.
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Authors: Shihao Hou; Chikai Shang; Zhiheng Yang; Jiacheng Yang; Xinyi Shang; Junlong Gao; Yiqun Zhang; Yang Lu
Venue: CVPR 2026 (main)

Abstract: Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world scenarios often face the co-occurrence of non-IID data and long-tailed class distributions, presenting unique challenges that remain underexplored in PFL. In this paper, we investigate this long-tailed personalized federated learning and observe that current methods suffer from two limitations: (i) Fine-tuning degrades performance below zero-shot baselines due to the erosion of inherent class balance in foundation models; (ii) Conventional personalization techniques further transfer this bias to local models through parameter or feature-level fusion. To address these challenges, we propose Federated Learning via Gradient Purification and Residual Learning (FedPuReL), which preserves balanced knowledge in the global model while enabling unbiased personalization. Specifically, we purify local gradients using zero-shot predictions to maintain a class-balanced global model, and model personalization as residual corrections atop the frozen global model. Extensive experiments demonstrate that FedPuReL consistently outperforms state-of-the-art methods, achieving superior performance on both global and personalized models across diverse long-tailed scenarios. The code is available at https://github.com/shihaohou/FedPuReL.
Modeling Cross-vision Synergy for Unified Large Vision Model
Authors: Shengqiong Wu; Lanhu Wu; Mingyang Bao; Wenhao Xu; Hanwang Zhang; Shuicheng Yan; Hao Fei; Tat-Seng Chua
Venue: CVPR 2026 (main)

Abstract: Recent advances in large vision models (LVMs) have shifted from modality-specific designs toward unified architectures that jointly process images, videos, and 3D data. However, existing unified LVMs primarily pursue functional integration, while overlooking the deeper goal of cross-vision synergy: the ability to reason over complementary priors across visual modalities.  To address this, we present PolyV, a unified LVM that achieves cross-vision synergy at both the architectural and training levels. Architecturally, PolyV adopts a sparse Mixture-of-Experts LVM coordinated by a dynamic modality router, allowing each expert to specialize in modality-specific priors while enabling bidirectional interaction and mutual refinement across modalities. Training-wise, a synergy-aware paradigm combines modality-specific pretraining with coarse-to-fine synergy tuning via knowledge distillation and object-/relation-level alignment. Extensive experiments on 10 benchmarks spanning image, video, and 3D understanding, including synergy-focused datasets requiring spatial or temporal priors, demonstrate that PolyV consistently outperforms existing models, achieving over 10% average improvement over its backbone. Overall, PolyV establishes a unified framework for synesthetic visual reasoning, advancing toward truly synergistic LVMs.
D2FANet: Enhancing Video Object Detection with Dual-Domain Feature Aggregation Network
Authors: Qiang Qi; Wenqi Shang; Meifang Wang; Xiao Wang
Venue: CVPR 2026 (main)

Abstract: Accurately capturing and aggregating spatiotemporal information has become crucial for video object detection. Previous methods mainly perform feature aggregation in the spatiotemporal domain, treating all regions indiscriminately and overlooking both their relative importance and the frequency characteristics that capture periodic motion patterns. This limits the capability of these methods to capture dynamic interactions and adapt to complex scene variations. In this paper, we propose a novel Dual-Domain Feature Aggregation Network (D2FANet) for video object detection, which, to the best of our knowledge, is the first work to introduce the frequency-domain feature aggregation into the video object detection task. By collaboratively modeling spatiotemporal and frequency information, our D2FANet enhances motion awareness and temporal consistency, thereby improving detection accuracy. First, we develop a frequency-domain feature aggregation module that decomposes frame features into high- and low-frequency distributions and reinforces object query representations through aggregating multi-scale frequency features. Second, we design a spatiotemporal-domain feature aggregation module that leverages an importance guidance mechanism to dynamically emphasize regions of different importance and reinforces object query representations via guiding the aggregation of spatiotemporal features. Experiments on the ImageNet VID and EPIC-KITCHENS datasets demonstrate that D2FANet achieves state-of-the-art performance.
A Geometric Algebra-Informed 3DGS Framework for Wireless Channel Prediction
Authors: Jingzhou Shen; Tianya Zhao; Xuyu Wang
Venue: CVPR 2026 (main)

Abstract: In this paper, we introduce Geometric Algebra-Informed 3D Gaussian Splatting (GAI-GS), a framework for wireless modeling that couples 3D Gaussian splatting with a geometric algebra-based attention mechanism to explicitly model ray-object interactions in complex propagation environments. GAI-GS encodes joint spatial-electromagnetic (EM) relations into token representations, enabling scene-level aggregation within a unified, end-to-end neural architecture. This design grounds wireless ray propagation in electromagnetic principles, allowing token interactions to model key effects such as multipath, attenuation, and reflection/diffraction. Through extensive evaluations on multiple real-world indoor datasets, GAI-GS consistently surpasses current baselines across various wireless tasks.
Radar-Guided Polynomial Fitting for Metric Depth Estimation
Authors: Patrick Rim; Hyoungseob Park; Vadim Ezhov; Jeffrey Moon; Alex Wong
Venue: CVPR 2026 (main)

Abstract: We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps. Unlike existing approaches that rely on complex architectures or expensive sensors, our method is grounded in a fundamental insight: although MDE models often infer reasonable local depth structure within each object or local region, they may misalign these regions relative to one another, making a linear scale and shift (affine) transformation insufficient given three or more of these regions. To address this limitation, we use polynomial coefficients predicted from cheap, ubiquitous radar data to adaptively adjust predictions non-uniformly across depth ranges. In this way, POLAR generalizes beyond affine transformations and is able to correct such misalignments by introducing inflection points. Importantly, our polynomial fitting framework preserves structural consistency through a novel training objective that enforces local monotonicity via first-derivative regularization. POLAR achieves state-of-the-art performance across three datasets, outperforming existing methods by an average of 24.9% in MAE and 33.2% in RMSE, while also achieving state-of-the-art efficiency in terms of latency and computational cost.
QuadSync: Quadrifocal Tensor Synchronization via Tucker Decomposition
Authors: Daniel Miao; Gilad Lerman; Joe Kileel
Venue: CVPR 2026 (main)

Abstract: In structure from motion, quadrifocal tensors capture more information than their pairwise counterparts (essential matrices), yet they have often been thought of as impractical and only of theoretical interest. In this work, we challenge such beliefs by providing a new framework to recover n cameras from the corresponding collection of quadrifocal tensors. We form the block quadrifocal tensor and show that it admits a Tucker decomposition whose factor matrices are the stacked camera matrices, and which thus has a multilinear rank of (4,4,4,4) independent of n. We develop the first synchronization algorithm for quadrifocal tensors, using Tucker decomposition, alternating direction method of multipliers, and iteratively reweighted least squares. We further establish relationships between the block quadrifocal, trifocal, and bifocal tensors, and introduce an algorithm that jointly synchronizes these three entities. Numerical experiments demonstrate the effectiveness of our methods on modern datasets, indicating the potential and importance of using higher-order information in synchronization.
A Polynomial Chaos Framework for Causal Discovery in Nonlinear Uncertain Systems
Authors: Liang Cao
Venue: CVPR 2026 (main)

Abstract: In safety-critical industrial applications, accurately identifying causal relationships and quantifying uncertainty is essential for tasks such as root cause analysis, feature selection, and process optimization. Traditional causal discovery methods inadequately handle nonlinearities and complex uncertainties prevalent in industrial sensor data. To address this, we introduce a novel causal discovery framework that integrates Polynomial Chaos Expansion (PCE) representations of stochastic noise into structural equations. This method effectively captures complex nonlinear couplings and arbitrary noise distributions characteristic of industrial data. We rigorously prove the identifiability of causal structures under mild sparsity conditions on the chaos coefficients, extending linear non-Gaussian acyclic model (LiNGAM) identifiability results. Extensive experiments demonstrate superior accuracy, robustness under non-Gaussian noise conditions, and practical uncertainty quantification. This framework presents a principled, interpretable, and computationally feasible approach to causal analysis in nonlinear uncertain industrial environments.
RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation
Authors: Linfei Li; Lin Zhang; Ying Shen
Venue: CVPR 2026 (main)

Abstract: Visual-language grounding aims to establish semantic correspondences between natural language and visual entities, enabling models to accurately identify and localize target objects based on textual instructions. Existing VLG approaches focus on coarse-grained, object-level localization, while traditional robotic grasping methods rely predominantly on geometric cues and lack language guidance, which limits their applicability in language-driven manipulation scenarios. To address these limitations, we propose the RealVLG framework, which integrates the RealVLG-11B dataset and the RealVLG-R1 model to unify real-world visual-language grounding and grasping tasks. RealVLG-11B dataset provides multi-granularity annotations including bounding boxes, segmentation masks, grasp poses, contact points, and human-verified fine-grained language descriptions, covering approximately 165,000 images, over 800 object instances, 1.3 million segmentation, detection, and language annotations, and roughly 11 billion grasping examples. Building on this dataset, RealVLG-R1 employs Reinforcement Fine-tuning on pretrained large-scale vision-language models to predict bounding boxes, segmentation masks, grasp poses, and contact points in a unified manner given natural language instructions. Experimental results demonstrate that RealVLG supports zero-shot perception and manipulation in real-world unseen environments, establishing a unified semantic-visual multimodal benchmark that provides a comprehensive data and evaluation platform for language-driven robotic perception and grasping policy learning. All data and code are publicly available at https://github.com/lif314/RealVLG-R1.
LoPrune: Efficient Data Pruning for LoRA-Based Fine-Tuning of Vision Transformer
Authors: Qiang He; Yaozong Yang; Kaibin Wang; Ziteng Wei; Feifei Chen; Caslon Chua; Yun Yang
Venue: CVPR 2026 (main)

Abstract: Visual models are deployed on many Internet-of-Things (IoT) devices to power a variety of visual applications at the network edge. These models often need to be fine-tuned on-device continually to adapt to changing operating environments timely. However, the computing and energy overheads incurred are often overwhelming for resource-constrained IoT devices. Existing methods score sample importance based on the entire model via multi-epoch training, incurring overhead that may even exceed the training overhead reduction. To reduce these fine-tuning overheads, this paper presents LoPrune, a novel data pruning method that identifies and removes samples with negligible contributions to model adaptation. The key idea is to evaluate sample importance via a Trainable Subspace Alignment (TSA) Score to align the importance estimation with accurate update directions of the learnable adapter, i.e., Low-Rank Adaptation (LoRA). Specifically, LoPrune projects the influence function onto the LoRA subspace, enforcing consistency between the importance score and the model's updatable directions while substantially reducing the problem's dimensionality. It then leverages Kronecker-Factored Approximate Curvature to approximate the change of learnable adapter induced by a sample as its TSA score, retaining higher-scoring samples. Experiments with four representative visual models fine-tuned on three datasets demonstrate that compared with the best state-of-the-art data pruning baselines, LoPrune can reduce fine-tuning overhead by up to 72.9%, achieving a 3.69 xtraining speedup while improving fine-tuning accuracy by 3.50%.
Enhancing Out-of-Distribution Detection with Extended Logit Normalization
Authors: Yifan Ding; Xixi Liu; Jonas Unger; Gabriel Eilertsen
Venue: CVPR 2026 (main)

Abstract: Out-of-distribution (OOD) detection is essential for the safe deployment of machine learning models. While extensive work has focused on designing effective scoring functions for OOD detection, relatively few studies explore training neural networks with calibration-oriented objectives, which often compromise predictive accuracy and restrict the choice of scoring functions. In this work, we first identify feature collapse in Logit Normalization (LogitNorm), and then propose a novel hyperparameter-free training formulation that significantly improves a wide range of post-hoc detection methods. Specifically, we introduce a feature distance-aware normalization objective, termed ELogitNorm, which enhances both OOD detection performance and in-distribution (ID) confidence calibration. Extensive experiments on standard benchmarks demonstrate that our approach outperforms state-of-the-art training-time methods in OOD detection while preserving strong ID classification performance. Our code is available at: https://github.com/limchaos/ElogitNorm.
FlashLips: 100-FPS Mask-Free Latent Lip-Sync using Reconstruction Instead of Diffusion or GANs
Authors: Andreas Zinonos; Michał Stypułkowski; Antoni Bigata; Stavros Petridis; Maja Pantic; Nikita Drobyshev
Venue: CVPR 2026 (main)

Abstract: We present FlashLips, a two-stage, mask-free lip-sync system that decouples lips control from rendering and achieves real-time performance, with our U-Net variant running at over 100 FPS on a single GPU, while matching the visual quality of larger state-of-the-art models. Stage 1 is a compact, one-step latent-space editor that reconstructs an image using a reference identity, a masked target frame, and a low-dimensional lips-pose vector, trained purely with reconstruction losses - no GANs or diffusion. To remove explicit masks at inference, we use self-supervision via mouth-altered target variants as pseudo ground truth, teaching the network to localize lip edits while preserving the rest. Stage 2 is an audio-to-pose transformer trained with a flow-matching objective to predict lips-pose vectors from speech. Together, these stages form a simple and stable pipeline that combines deterministic reconstruction with robust audio control, delivering high perceptual quality and faster-than-real-time speed.
Towards Robust Multimodal Large Language Models Against Jailbreak Attacks
Authors: Ziyi Yin; Yuanpu Cao; Han Liu; Ting Wang; Jinghui Chen; Fenglong Ma
Venue: CVPR 2026 (main)

Abstract: While multimodal large language models (MLLMs) have achieved remarkable success in recent advancements, their susceptibility to jailbreak attacks has come to light. In such attacks, adversaries exploit carefully crafted prompts to coerce models into generating harmful or undesirable content. Existing defense mechanisms often rely on external inference steps or safety alignment training, both of which are less effective and impractical when facing sophisticated adversarial perturbations in white-box scenarios. To address these challenges and bolster MLLM robustness, we introduce SAFEMLLM by adopting an adversarial training framework that alternates between an attack step for generating adversarial noise and a model updating step. At the attack step, SAFEMLLM generates adversarial perturbations through a newly proposed contrastive embedding attack (CoE-Attack), which optimizes token embeddings under a contrastive objective. SAFEMLLM then updates model parameters to neutralize the perturbation effects while preserving model utility on benign inputs. We evaluate SAFEMLLM across multiple MLLMs and six jailbreak methods spanning multiple modalities. Experimental results show that SAFEMLLM effectively defends against diverse attacks, maintaining robust performance and utilities.
Bridging Domain Expertise and Generalization for Performance Estimation
Authors: Shuxuan Li; Zhilin Zhao; Quyu Kong; Wei-Shi Zheng
Venue: CVPR 2026 (main)

Abstract: Performance estimation under distribution shift aims to predict how a model behaves on an unlabeled test set whose distribution differs from the training data, a scenario that requires reliable indicators that can faithfully reflect model behavior without ground-truth labels. Existing approaches rely solely on the outputs of the given model whose biases are amplified once the distribution shifts, weakening the correlation with the true performance. Motivated by this limitation, we propose Fused Reference Alignment Prediction (FRAP), which leverages the complementary strengths of an external foundation model and the base model to construct a more reliable surrogate of the ground-truth labels. FRAP aligns the prediction distribution of the foundation model with that of the base model by applying temperature-scaled calibration that minimizes their divergence. The aligned predictions are fused through confidence-based weighting into a refined reference distribution that integrates robustness from the foundation model and domain-specific expertise from the base model, and performance estimation is obtained by measuring how closely the base model predictions agree with this reference. Extensive experiments across diverse datasets and architectures show that FRAP provides consistent and substantial improvements over representative performance-estimation methods under distribution shift.
VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Authors: Yufei Yin; Qianke Meng; Minghao Chen; Jiajun Ding; Zhenwei Shao; Zhou Yu
Venue: CVPR 2026 (main)

Abstract: Long-form video understanding remains challenging due to the extended temporal structure and dense multimodal cues. Despite recent progress, many existing approaches still rely on hand-crafted reasoning pipelines or employ token-consuming video preprocessing to guide MLLMs in autonomous reasoning. To overcome these limitations, we introduce VideoARM, an agentic reasoning-over-hierarchical-memory paradigm for long-form video understanding. Instead of static, exhaustive preprocessing, VideoARM performs adaptive, on-the-fly agentic reasoning and memory construction. Specifically, VideoARM performs an adaptive and continuous loop of  observing, thinking, acting, and memorizing, where a controller autonomously invokes tools to interpret the video in a coarse-to-fine manner, thereby substantially reducing token consumption. In parallel, a hierarchical multimodal memory continuously captures and updates multi-level clues throughout the operation of the agent, providing precise contextual information to support the controller in decision-making. Experiments on prevalent benchmarks demonstrate that VideoARM outperforms the state-of-the-art method, DVD, while significantly reducing token consumption for long-form videos.