GenAI4ELab/papercli-papers huggingface dataset:
Few-Step Diffusion Sampling Through Instance-Aware Discretizations
Authors: Liangyu Yuan; Ruoyu Wang; Tong Zhao; Dingwen Fu; Mingkun Lei; Beier Zhu; Chi Zhang
Venue: CVPR 2026 (main)

Abstract: Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable prior distribution. The probability flow ODE formulation enables the use of advanced numerical solvers to accelerate sampling. Orthogonal yet vital to solver design is the discretization strategy. While early approaches employed handcrafted heuristics and recent methods adopt optimization-based techniques, most existing strategies enforce a globally shared timestep schedule across all samples. This uniform treatment fails to account for instance-specific complexity in the generative process, potentially limiting performance.Motivated by controlled experiments on synthetic data, which reveals the suboptimality of global schedules under instance-specific dynamics, we propose an instance-aware discretization framework. Our method learns to adapt timestep allocations based on input-dependent priors, extending gradient-based discretization search to the conditional generative setting. Empirical results across diverse settings, including synthetic data, pixel-space diffusion, latent-space images and video flow matching models, demonstrate that our method consistently improves generation quality with marginal tuning cost compared to training and negligible inference overhead.
Revisiting 3D Reconstruction Kernels as Low-Pass Filters
Authors: Shengjun Zhang; Min Chen; Yibo Wei; Mingyu Dong; Yueqi Duan
Venue: CVPR 2026 (main)

Abstract: 3D reconstruction is to recover 3D signals from the sampled discrete 2D pixels, with the goal to converge continuous 3D spaces.In this paper, we revisit 3D reconstruction from the perspective of signal processing, identifying the periodic spectral extension induced by discrete sampling as the fundamental challenge.Previous 3D reconstruction kernels, such as Gaussians, Exponential functions, and Student's t distributions, serve as the low pass filters to isolate the baseband spectrum.However, their unideal low-pass property results in the overlap of high-frequency components with low-frequency components in the discrete-time signal's spectrum.To this end, we introduce Jinc kernel with an instantaneous drop to zero magnitude exactly at the cutoff frequency, which is corresponding to the ideal low pass filters.As Jinc kernel suffers from low decay speed in the spatial domain, we further propose modulated kernels to strick an effective balance, and achieves superior rendering performance by reconciling spatial efficiency and frequency-domain fidelity.Experimental results have demonstrated the effectiveness of our Jinc and modulated kernels.
Mining Instance-Centric Vision-Language Contexts for Human-Object Interaction Detection
Authors: Soo Won Seo; KyungChae Lee; Hyungchan Cho; Taein Son; Nam Ik Cho; Jun Won Choi
Venue: CVPR 2026 (main)

Abstract: Human-Object Interaction (HOI) detection aims to localize human-object pairs and classify their interactions from a single image, a task that demands strong visual understanding and nuanced contextual reasoning. Recent approaches have leveraged Vision-Language Models (VLMs) to introduce semantic priors, significantly improving HOI detection performance. However, existing methods often fail to fully capitalize on the diverse contextual cues distributed across the entire scene. To overcome these limitations, we propose the Instance-centric Context Mining Network (InCoM-Net)--a novel framework that effectively integrates rich semantic knowledge extracted from VLMs with instance-specific features produced by an object detector. This design enables deeper interaction reasoning by modeling relationships not only within each detected instance but also across instances and their surrounding scene context. InCoM-Net comprises two core components: Instance-centric Context Refinement (ICR), which separately extracts intra-instance, inter-instance, and global contextual cues from VLM-derived features, and Progressive Context Aggregation (ProCA), which iteratively fuses these multi-context features with instance-level detector features to support high-level HOI reasoning. Extensive experiments on the HICO-DET and V-COCO benchmarks show that InCoM-Net achieves state-of-the-art performance, surpassing previous HOI detection methods. Code is available at https://github.com/nowuss/InCoM-Net.
P2GS: Physical Prior-guided Gaussian Splatting for Photometrically Consistent Urban Reconstruction
Authors: Kota Shimomura; Hidehisa Arai; Tsubasa Takahashi; Takayoshi Yamashita; Hironobu Fujiyoshi
Venue: CVPR 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) has recently emerged as a powerful explicit representation enabling fast, high-fidelity rendering, making it a promising foundation for closed-loop simulators and perception models in autonomous driving. However, conventional 3DGS implicitly assumes consistent exposure and tone mapping across views. Real driving data violates this assumption due to heterogeneous camera pipelines and dynamic outdoor illumination, baking exposure discrepancies and sensor noise into the radiance field and producing artifacts and inconsistent illumination especially in static backgrounds crucial for realistic simulation. These issues are amplified in autonomous driving, where sparse viewpoints, varying exposures, and outdoor lighting interact, while prior work mainly targets dynamic-object reconstruction and overlooks cross-view photometric consistency.To address this limitation, we introduce P2GS, a physically consistent Gaussian Splatting framework that jointly decomposes a view-invariant linear HDR radiance field, per-view exposure scales, and tone-mapping functions from only LDR images without HDR supervision. P2GS employs a unified optimization strategy grounded in the physical image-formation process, enforcing relative-exposure consistency and HDR-domain radiance regularization. This yields a radiance field robust to inter-camera illumination differences while preserving the real-time efficiency of standard 3DGS. Experiments across real and simulated driving environments show that P2GS matches or surpasses prior methods in LDR reconstruction while providing substantially improved photometric consistency, reliable exposure normalization, and physically coherent illumination across diverse scenes.
UNI-OOD: Unified Object- and Image-level Out-of-Distribution Detection via Cross-Context Attentive Vision-Language Modeling
Authors: Yuchuan Li; Azadeh Motamedi; Hyock Ju Kwon; Chul B Park; Il-Min Kim
Venue: CVPR 2026 (main)

Abstract: Out-of-distribution (OOD) detection is a key requirement for reliable deployment in open-world environments, where a model must recognize inputs that fall outside the semantic scope of known concepts. While recent advances in vision-language models (VLMs) have achieved strong results in image-level OOD detection, most methods still assume that each image contains a single dominant object. This assumption severely limits their applicability to real-world settings where scenes are naturally composed of multiple objects that each demand independent OOD assessment. Existing object-level approaches, including the current SOTA method RUNA, remain constrained by coarse global representations and insufficient modeling of contextual dependencies between objects and their backgrounds. We propose UNI-OOD, a unified framework that performs both object- and image-level OOD detection within a single VLM, without requiring prior knowledge of which task is being addressed at inference time. The key idea is to leverage cross-context attentive modeling that captures complementary visual and textual semantics. UNI-OOD learns to attend to fine-grained spatial details within each object, aligns visual and linguistic embeddings to strengthen semantic correspondence, and models interactions between target objects and their surrounding context. By jointly reasoning over object-centric and background cues, the framework disentangles informative visual evidence from spurious correlations and enables a consistent OOD scoring mechanism across different visual granularities. Extensive experiments on object- and image-level benchmarks demonstrate that UNI-OOD achieves substantial and consistent improvements over previous approaches, establishing new SOTA performance in both object-level and image-level OOD detection.
SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
Authors: Jiahao Wang; Yufeng Yuan; Rujie Zheng; Youtian Lin; Jian Gao; Lin-Zhuo Chen; Yajie Bao; Chang Zeng; Yanxi Zhou; Xiao-Xiao Long; Hao Zhu; Zhaoxiang Zhang; Xun Cao; Yao Yao
Venue: CVPR 2026 (main)

Abstract: Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets provide camera pose information, they are typically limited in scale, diversity, and annotation richness, particularly for real-world dynamic scenes with ground-truth camera motion.To this end, we collect **SpatialVID**, a dataset consisting of a large corpus of in-the-wild videos with diverse scenes, camera movements and dense 3D annotations such as per-frame camera poses, depth, and motion instructions.Specifically, we collect more than 21,000 hours of raw video, and process them into 2.7 million clips through a hierarchical filtering pipeline, totaling 7,089 hours of dynamic content. A subsequent annotation pipeline enriches these clips with detailed spatial and semantic information, including camera poses, depth maps, dynamic masks, structured captions, and serialized motion instructions.Analysis of SpatialVID's data statistics reveals a richness and diversity that directly foster improved model generalization and performance, establishing it as a key asset for the video and 3D vision research community.Through extensive validation experiments, we demonstrate SpatialVID's effectiveness across tasks such as controllable video generation, world simulation and geometric reconstruction, providing a strong foundation for spatial intelligence research.
Air-Know: Arbiter-Calibrated Knowledge-Internalizing Robust Network for Composed Image Retrieval
Authors: Zhiheng Fu; Yupeng Hu; Qianyun Yang; Shiqi Zhang; Zhiwei Chen; Zixu Li
Venue: CVPR 2026 (main)

Abstract: Composed Image Retrieval (CIR) has attracted significant attention due to its flexible multimodal query method, yet its development is severely constrained by the Noisy Triplet Correspondence (NTC) problem. Most existing robust learning methods rely on the "small loss hypothesis", but the unique semantic ambiguity in NTC, such as "partial matching", invalidates this assumption, leading to unreliable noise identification. This entraps the model in a self dependent vicious cycle where the learner is intertwined with the arbiter, ultimately causing catastrophic "representation pollution". To address this critical challenge, we propose a novel "Expert-Proxy-Diversion" decoupling paradigm, named Air-Know (ArbIteR calibrated Knowledge iNternalizing rObust netWork). Air-Know incorporates three core modules: (1) The External Prior Arbitration (EPA) module, which utilizes Multimodal Large Language Models (MLLMs) as an offline expert to construct a high precision anchor dataset; (2) Expert Knowledge Internalization (EKI) module, which efficiently guides a lightweight proxy "arbiter" to internalize the expert's discriminative logic; (3) Dual Stream Reconciliation (DSR) module, which leverages the EKI's matching confidence to divert the training data, achieving a clean alignment stream and a representation feedback reconciliation stream. Extensive experiments on multiple CIR benchmark datasets demonstrate that Air-Know significantly outperforms existing SOTA methods under the NTC setting, while also showing strong competitiveness in traditional CIR.
MeshSplatting: Differentiable Rendering with Opaque Meshes
Authors: Jan Held; Sanghyun Son; Renaud Vandeghen; Daniel Rebain; Matheus Gadelha; Yi Zhou; Anthony Cioppa; Ming C. Lin; Marc Van Droogenbroeck; Andrea Tagliasacchi
Venue: CVPR 2026 (main)

Abstract: Primitive-based splatting methods like 3D Gaussian Splatting (3DGS) have revolutionized novel view synthesis with real-time rendering. However, their point-based representations remain incompatible with mesh-based pipelines that power AR/VR and game engines. We present Mesh Splatting, a mesh-based reconstruction approach that jointly optimizes geometry and appearance through differentiable rendering. By enforcing connectivity via restricted Delaunay triangulation and refining surface consistency, Mesh Splatting creates end-to-end smooth, high-fidelity meshes that render efficiently in real-time engines. On Mip-NeRF360 and Tanks&Temples, it boosts PSNR by +0.69dB, while training 2x faster and using 2x less memory, bridging neural rendering and interactive 3D graphics for seamless real-time scene interaction. The project page is available at https://meshsplatting.github.io/.
ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps
Authors: Sicheng Feng; Song Wang; Shuyi Ouyang; Lingdong Kong; Zikai Song; Jianke Zhu; Huan Wang; Xinchao Wang
Venue: CVPR 2026 (main)

Abstract: Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performance on more complex tasks involving mathematics and logic. However, their proficiency in tasks requiring both fine-grained visual understanding and spatial reasoning remains underexplored. To bridge this gap, we introduce ReasonMap, a novel benchmark specifically designed to evaluate these capabilities. ReasonMap encompasses high-resolution transit maps from 30 cities and includes 1,008 question-answer pairs spanning two question types and three templates. Furthermore, we design a two-level evaluation pipeline that properly assesses answer correctness and quality. Our comprehensive evaluation of 16 popular MLLMs reveals a counterintuitive pattern: among open-source models, base variants outperform their reasoning-tuned counterparts, whereas the opposite trend is observed in closed-source models. Further analysis under the visual-masking setting confirms that strong performance necessitates direct visual grounding, rather than relying solely on language priors. We further establish a training baseline with reinforcement fine-tuning, providing a reference for future exploration. We hope this benchmark study offers new insights into visual reasoning and helps investigate the gap between open- and closed-source models.
3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds
Authors: Ryousuke Yamada; Kohsuke Ide; Yoshihiro Fukuhara; Hirokatsu Kataoka; Gilles Puy; Andrei Bursuc; Yuki M. Asano
Venue: CVPR 2026 (main)

Abstract: Despite recent progress in 3D self-supervised learning, collecting large-scale 3D scene scans remains expensive and labor-intensive. In this work, we investigate whether 3D representations can be learned from unlabeled videos recorded without any real 3D sensors. We present Laplacian-Aware Multi-level 3D Clustering with Sinkhorn-Knopp (LAM3C), a self-supervised framework that learns from video-generated point clouds reconstructed from unlabeled videos. We first introduce RoomTours, a video-generated point cloud dataset constructed by collecting room-walkthrough videos from the web (e.g., real-estate tours) and generating 49,219 scenes using an off-the-shelf feed-forward reconstruction model.  We also propose a noise-regularized loss that stabilizes representation learning by enforcing local geometric smoothness and ensuring feature stability under noisy point clouds. Remarkably, without using any real 3D scans, LAM3C achieves better performance than previous self-supervised methods on indoor semantic and instance segmentation. These results suggest that unlabeled videos represent an abundant source of data for 3D self-supervised learning. Our source code is available at https://github.com/ryosuke-yamada/lam3c.
DK-DDIL: Adaptive Knowledge Retention for Dynamic Domain-Incremental Learning in Medical Imaging
Authors: Yuxi Ma; Sujie Liu; Jing Yang; Jiacheng Wang; Yiping Chen; Baptiste Magnier; Liansheng Wang
Venue: CVPR 2026 (main)

Abstract: Large-scale foundation models pretrained on massive datasets have demonstrated strong generalization capabilities in medical image analysis. However, they are typically trained on static datasets and struggle to cope with the continuously evolving nature of clinical data, where new imaging devices, institutions, and disease subtypes constantly emerge. While domain-incremental learning (DIL) provides a solution for sequential adaptation without revisiting historical data, existing methods typically assume fixed label spaces and limited domain heterogeneity, restricting their applicability to real-world clinical scenarios. To address these challenges, we propose DK-DDIL, a rehearsal-free framework for dynamic DIL that integrates two synergistic modules: a Dynamic Adaptation Module (DAM) employing dynamic rank selection and adaptive regularization to flexibly allocate model capacity under domain shifts, and a Knowledge Inheritance and Refinement (KIR) module that stabilizes cross-domain knowledge transfer through selective adapter fusion and prototype-level contrastive refinement. Experiments on the Skin Pathology Diagnosis dataset, the Cyst-X 3D MRI cohort, and the OfficeHome benchmark demonstrate that DK-DDIL consistently outperforms state-of-the-art DIL approaches, highlighting its effectiveness and versatility across dynamic 2D medical, 3D medical, and natural image domains.
CUPID: Generative 3D Reconstruction via Joint Object and Pose Modeling
Authors: Binbin Huang; Haobin Duan; Yiqun Zhao; Zibo Zhao; Yi Ma; Shenghua Gao
Venue: CVPR 2026 (main)

Abstract: We introduce Cupid, a generative 3D reconstruction framework that jointly models the full distribution over both canonical objects and camera poses. Our two-stage flow-based model first generates a coarse 3D structure and 2D-3D correspondences to estimate the camera pose robustly. Conditioned on this pose, a refinement stage injects pixel-aligned image features directly into the generative process, marrying the rich prior of a generative model with the geometric fidelity of reconstruction. This strategy achieves exceptional faithfulness, outperforming state-of-the-art reconstruction methods by over 3 dB PSNR and 10% in Chamfer Distance. As a unified generative model that decouples the object and camera pose, Cupid naturally extends to multi-view and scene-level reconstruction tasks without requiring post-hoc optimization or fine-tuning.
Gaussian Splatting-based Low-Rank Tensor Representation for Multi-Dimensional Image Recovery
Authors: Yiming Zeng; Xi-Le Zhao; Wei-Hao Wu; Teng-Yu Ji; Chao Wang
Venue: CVPR 2026 (main)

Abstract: Tensor singular value decomposition (t-SVD) is a promising tool for multi-dimensional image representation, which decomposes a multi-dimensional image into a latent tensor and an accompanying transform matrix. However, two critical limitations of t-SVD methods persist: (1) the approximation of the latent tensor (e.g., tensor factorizations) is coarse and fails to accurately capture spatial local high-frequency information; (2) the transform matrix is composed of fixed basis atoms (e.g., complex exponential atoms in DFT and cosine atoms in DCT) and cannot precisely capture local high-frequency information along the mode-3 fibers. To address these two limitations, we propose a  Gaussian Splatting-based Low-rank tensor Representation (GSLR) framework, which compactly and continuously represents multi-dimensional images. Specifically, we leverage tailored 2D Gaussian splatting and 1D Gaussian splatting to generate the latent tensor and transform matrix, respectively. The 2D and 1D Gaussian splatting are indispensable and complementary under this representation framework, which enjoys a powerful representation capability, especially for local high-frequency information. To evaluate the representation ability of the proposed GSLR, we develop an unsupervised GSLR-based  multi-dimensional image recovery model. Extensive experiments on multi-dimensional image recovery demonstrate that GSLR consistently outperforms state-of-the-art methods, particularly in capturing local high-frequency information.
Semantic-Guided Global-Local Collaborative Prompt Learning for Few-Shot Class Incremental Learning
Authors: Yongxin Yan; Weisen Chen; Xingye Chen; Yuanjie Shao; Zhengrong Zuo; Wenming Tan; Wenqi Ren; Changxin Gao; Nong Sang
Venue: CVPR 2026 (main)

Abstract: Few-Shot Class-Incremental Learning (FSCIL) poses a critical challenge in machine learning, requiring models to continuously integrate novel classes with limited samples while preserving knowledge of previously seen classes. While existing FSCIL approaches have demonstrated promising results, they still suffer from catastrophic forgetting and few-shot overfitting due to the challenge of balancing old knowledge retention with new knowledge acquisition. To address these challenges, we propose an innovative Semantic-Guided Global-Local Collaborative Prompt Learning (SGLC) framework. Built upon powerful pre-trained Vision-Language Models (VLMs), the framework first introduces a dual-alignment mechanism: globally aligning visual features with visual-textual prototypes and locally aligning multi-view visual features with local textual attribute features, which facilitates effective knowledge learning while preserving existing knowledge via frozen prototypes of previous classes. Furthermore, to alleviate overfitting, we incorporate Large Language Models (LLMs) to generate semantically rich textual descriptions, which simultaneously guide both global and local prompt learning through knowledge distillation. Extensive experiments on the miniImageNet, CIFAR-100, and CUB200 datasets demonstrate that SGLC performs favorably against the state-of-the-art methods.
Improving Adversarial Transferability with Local Perturbation Augmentation
Authors: Jian-Xun Mi; Xuanhui Zhong; Weisheng Li
Venue: CVPR 2026 (main)

Abstract: Adversarial examples expose fundamental vulnerabilities within deep neural networks, and their transferability highlights shared weaknesses across diverse models. Existing mainstream attack methods often rely on iterative processes with various strategies to improve transferability, but the limited knowledge of the target model restricts the success of these approaches. In this paper, we reveal that the iterative optimization process tends to over-specialize adversarial perturbations to the local gradient characteristics of the surrogate model, thereby hindering their transferability to other models. To address this limitation, we propose a novel attack method called Local Perturbation Augmentation Attack (LPAA). The key innovation of our approach lies in constructing multiple augmented local subspaces during each iteration, which steers perturbation updates towards a more generalizable direction, reducing over-reliance on the surrogate model. Additionally, to improve the initial performance and overcome sensitivity to initial perturbation, we introduce a dedicated perturbation initialization strategy that ensures the optimization process starts from a direction with greater transferability. Compared with existing random neighborhood sampling strategies, LPAA serves as an effective approach that leverages the characteristics of perturbations to overcome their limitations. Extensive experiments on CNNs and ViTs demonstrate that LPAA consistently generates highly transferable adversarial examples, significantly surpassing the performance of state-of-the-art methods.