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首次观测 · 记录 #1CVF · CVPR 2026:cvf:e0c09f19a70e22e722b8cc7ba26cd716CVF · CVPR 2026

Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving

Zhang, Zehan, Li, Yaoyi, Zhang, Neng, Cai, Jia

原文摘要

Learning-based motion planners, despite recent progress, often suffer from temporal inconsistency. Small perturbations across frames can accumulate into unstable trajectories, degrading comfort and safety in closed-loop driving. Several methods attempt to inject history as a static conditioning signal to stabilize outputs, only to induce the planner to copy historical patterns instead of adapting to environment contexts. To address this limitation, we propose Diffusion Forcing Planner (DFP), a diffusion-based planning framework driven by history-guided control. Specifically, DFP decomposes the full trajectory into history, current and future segments, and assign independent noise levels to each segment. The model jointly denoises the historical and the future segments, enforcing a heterogeneous joint diffusion process. At inference, classifier-free guidance (CFG) is applied to steer future sampling using annealed history in a controllable manner. Closed-loop evaluation and comprehensive ablations on nuPlan show that DFP achieves competitive performance while producing continuous, stable, and controllable motion plans in complex driving scenarios.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e0f940f56e9497e4f4ade68e633578e9CVF · CVPR 2026

PanoVGGT: Feed-Forward 3D Reconstruction from Panoramic Imagery

Guo, Yijing, Chao, Mengjun, Wang, Luo, Zhao, Tianyang, Dai, Haizhao, Zhang, Yingliang, Yu, Jingyi, Shi, Yujiao

原文摘要

Panoramic imagery offers a full 360^\circ field of view and is increasingly common in consumer devices. However, it introduces non-pinhole distortions that challenge joint pose estimation and 3D reconstruction. Existing feed-forward models, built for perspective cameras, generalize poorly to this setting. We propose PanoVGGT, a permutation-equivariant Transformer framework that jointly predicts camera poses, depth maps, and 3D point clouds from one or multiple panoramas in a single forward pass. The model incorporates spherical-aware positional embeddings and a panorama-specific three-axis SO(3) rotation augmentation, enabling effective geometric reasoning in the spherical domain. To resolve inherent global-frame ambiguity, we further introduce a stochastic anchoring strategy during training. In addition, we contribute PanoCity, a large-scale outdoor panoramic dataset with dense depth and 6-DoF pose annotations. Extensive experiments on PanoCity and standard benchmarks demonstrate that PanoVGGT achieves competitive accuracy, strong robustness, and improved cross-domain generalization. Code and dataset will be released.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e10a20b9aaa63f8164e4a253df57fb00CVF · CVPR 2026

Prompt-Free Universal Region Proposal Network

Tang, Qihong, Liu, Changhan, Zhang, Shaofeng, Li, Wenbin, Fan, Qi, Gao, Yang

原文摘要

Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by relying on exemplar images, predefined categories, or textual descriptions. However, their reliance on image and text prompts often limits flexibility, restricting adaptability in real-world scenarios. In this paper, we introduce a novel Prompt-Free Universal Region Proposal Network (PF-RPN), which identifies potential objects without relying on external prompts. First, the Sparse Image-Aware Adapter (SIA) module performs initial localization of potential objects using a learnable query embedding dynamically updated with visual features. Next, the Cascade Self-Prompt (CSP) module identifies the remaining potential objects by leveraging the self-prompted learnable embedding, autonomously aggregating informative visual features in a cascading manner. Finally, the Centerness-Guided Query Selection (CG-QS) module facilitates the selection of high-quality query embeddings using a centerness scoring network.Our method can be optimized with limited data (e.g., 5% of MS COCO data) and applied directly to various object detection application domains for identifying potential objects without fine-tuning, such as underwater object detection, industrial defect detection, and remote sensing image object detection. Experimental results across 19 datasets validate the effectiveness of our method.

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:e119090978e57cd18771828f2928355dCVF · CVPR 2026

HiconAgent: History Context-aware Policy Optimization for GUI Agents

Zhou, Xurui, Chen, Gongwei, Xie, Yuquan, Li, Zaijing, Zhou, Kaiwen, Wang, Shuai, Yang, Shuo, Tian, Zhuotao 等 9 位作者

原文摘要

Graphical User Interface (GUI) agents require effective utilization of historical context to perform sequential navigation tasks. While incorporating past actions and observations can significantly improve decision-making, naively using full history leads to excessive computational overhead and potential distraction from irrelevant information. In this work, we introduce ****HiconAgent****, a GUI agent trained with ****History Context-aware Policy Optimization (HCPO)**** for effective and efficient utilization of historical information. HCPO explicitly optimizes history usage in both sampling and policy updates by integrating two complementary components: ****(1) Dynamic Context Sampling (DCS)**** presents the agent with variable-length histories during sampling, enabling adaptive use of the most relevant historical context to improve sequential decision quality; ****(2) Anchor-guided History Compression (AHC)**** refines the policy update phase via a dual-branch optimization strategy, where the compressed branch drops history observations while keeping history actions as information flow anchors. The compressed and uncompressed branches are coupled through a history-enhanced alignment loss to enforce consistent history usage, achieving efficiency with minimal performance degradation. Extensive experiments on mainstream GUI navigation benchmarks demonstrate the strong performance of our model. Despite its smaller size, HiconAgent-3B outperforms GUI-R1-7B by ****+8.46****% grounding and ****+11.32****% step successful rate on GUI-Odyssey, while achieving comparable results on AndroidControl and AITW, with up to ****2.47x computational speedup**** and ****60% FLOPs reduction****.

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:e138f09acea72602cb2b0ea03a7aa1f8CVF · CVPR 2026

4D Local Modeling Toward Dynamic Global Perception for Ambiguity-free Rotation-Invariant Point Cloud Analysis

Guo, Jiaxun, Fan, Wentao, Amayri, Manar, Bouguila, Nizar

原文摘要

Rotation invariance remains a core challenge in point cloud analysis, where existing methods often struggle with structural ambiguities and insufficient global context. Most rotation-invariant (RI) representations are derived from local coordinate systems, which inherently suffer from point-pair ambiguities and fail to capture discriminative features in symmetric or repetitive structures, while discarding informative global pose cues. To overcome these limitations, we propose Ga4DPF, a novel framework that offers a robust, global-aware RI representation by converting rotation-equivariant geometric representations into invariant ones, while concurrently integrating global pose awareness. Specifically, Ga4DPF introduces a learnable steerable transform that equivariantly lifts point clouds into 4D space, facilitating robust local feature construction and mitigating point-pair ambiguities. Concurrently, we model a dynamic global pose reference using the Bingham distribution, which adaptively estimates a consistent global rotation and enhances global feature discriminability. Extensive experiments on multiple benchmark datasets demonstrate that Ga4DPF achieves state-of-the-art performance with high computational efficiency, offering a new paradigm for rotation-invariant point cloud analysis. The source code is available at: https://github.com/jiaxunguo/ga4dpf.

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:e14964bac8d0487c786691a97f222619CVF · CVPR 2026

Gravitation-Driven Semantic Alignment for Text Video Retrieval

Yang, Yi, Wang, Zheng, Xu, Xing, Song, Jingkuan, Shen, Heng Tao

原文摘要

The inherent semantic ambiguity of "many-to-many", where one video matches multiple texts and vice versa, aggravates the difficulty in text-video retrieval. The dominant deterministic embeddings only struggle to capture the mean semantics, while existing probabilistic methods fail to distinguish hard negatives for their imposing rigid uncertainty priors or ignoring the interaction between similarity and uncertainty. To this end, we propose a novel physics-inspired framework (GraviAlign) that decomposes the alignment of cross-modal semantic distributions into two orthogonal factors inspired by the Gravitational Force: (1) Semantic Attraction measuring gravitational alignment between distribution centers via uncertainty-derived "semantic mass" and "semantic distance"; (2) Geometric Overlap quantifying distribution intersection. Each factor has independent veto power to reject those matches with misalignment or poor overlap. Additionally, GraviAlign offers an efficient (O(D)), theoretically grounded alternative to intractable joint integrals. Extensive experiments on DiDeMo, MSR-VTT, and ActivityNet demonstrate our effectiveness and superiority, and solid ablation studies confirm the indispensability of two novel components.

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:e14bcb8377007a75fba0361786968caeCVF · CVPR 2026

Revisiting Token Compression for Accelerating ViT-based Sparse Multi-View 3D Object Detectors

Ji, Mingqian, Zhang, Shanshan, Yang, Jian

原文摘要

Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due to heavy token processing. To accelerate these models, token compression has been widely explored. However, our revisit of existing strategies, such as token pruning, merging, and patch size enlargement, reveals that they often discard informative background cues, disrupt contextual consistency, and lose fine-grained semantics, negatively affecting 3D detection. To overcome these limitations, we propose SEPatch3D, a novel framework that dynamically adjusts patch sizes while preserving critical semantic information within coarse patches. Specifically, we design Spatiotemporal-aware Patch Size Selection (SPSS) that assigns small patches to scenes containing nearby objects to preserve fine details and large patches to background-dominated scenes to reduce computation cost. To further mitigate potential detail loss, Informative Patch Selection (IPS) selects the informative patches for feature refinement, and Cross-Granularity Feature Enhancement (CGFE) injects fine-grained details into selected coarse patches, enriching semantic features. Experiments on the nuScenes and Argoverse 2 validation sets show that SEPatch3D achieves up to 57% faster inference than the StreamPETR baseline and 20% higher efficiency than the state-of-the-art ToC3D-faster, while preserving comparable detection accuracy.

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:e14dc8f360efbcb9843976e9764a451aCVF · CVPR 2026

Affostruction: 3D Affordance Grounding with Generative Reconstruction

Park, Chunghyun, Lee, Seunghyeon, Cho, Minsu

原文摘要

This paper addresses the problem of affordance grounding from RGBD images of an object, which aims to localize surface regions corresponding to a text query that describes an action on the object. While existing methods predict affordance regions only on visible surfaces, we propose Affostruction, a generative framework that reconstructs complete object geometry from partial RGBD observations and grounds affordances on the full shape including unobserved regions. Our approach introduces sparse voxel fusion of multi-view features for constant-complexity generative reconstruction, a flow-based formulation that captures the inherent ambiguity of affordance distributions, and an active view selection strategy guided by predicted affordances. Affostruction outperforms existing methods by large margins on challenging benchmarks, achieving 19.1 aIoU on affordance grounding and 32.67 IoU for 3D reconstruction.

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:e184e9e600f0df62246555336160e548CVF · CVPR 2026

Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

Tang, Minxue, Yu, Yangyang, Ding, Aolin, Pouyan, Maziyar Baran, Belkhouja, Taha, Bao, Yujia

原文摘要

Recognizing visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challenging for current pre-trained foundation models such as LLMs and VLMs. While finetuning pre-trained models can improve accuracy in recognizing such implicit patterns, it is usually infeasible due to a lack of training data and high computational overhead. In this paper, we propose ADAMAB, an efficient embedding calibration framework for few-shot pattern recognition. To maximally reduce the computational costs, ADAMAB trains embedder-agnostic light-weight calibrators on top of fixed embedding models without accessing their parameters. To mitigate the need for large-scale training data, we introduce an adaptive data augmentation strategy based on the Multi-Armed Bandit (MAB) mechanism. With a modified upper confidence bound algorithm, ADAMAB diminishes the gradient shifting and offers theoretically guaranteed convergence in few-shot training. Our multi-modal experiments justify the superior performance of ADAMAB, with up to 40% accuracy improvement when training with less than 5 initial data samples of each class.

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:e186c8b67ea1c88445f852360662f38aCVF · CVPR 2026

Learning Surgical Robotic Manipulation with 3D Spatial Priors

Sheng, Yu, Wang, Lidian, Chu, Xiaomeng, Deng, Jiajun, Cheng, Min, Zhang, Yanyong, Hua, Bei, Li, Houqiang 等 9 位作者

原文摘要

Achieving 3D spatial awareness is crucial for surgical robotic manipulation, where precise and delicate operations are required. Existing methods either explicitly reconstruct the surgical scene prior to manipulation, or enhance multi-view features by adding wrist-mounted cameras to supplement the default stereo endoscopes. However, both paradigms suffer from notable limitations: the former easily leads to error accumulation and prevents end-to-end optimization due to its multi-stage nature, while the latter is rarely adopted in clinical practice since wrist-mounted cameras can interfere with the motion of surgical robot arms. In this work, we introduce the Spatial Surgical Transformer (SST), an end-to-end visuomotor policy that empowers surgical robots with 3D spatial awareness by directly exploring 3D spatial cues embedded in endoscopic images. First, we build Surgical3D, a large-scale photorealistic dataset containing 30K stereo endoscopic image pairs with accurate 3D geometry, addressing the scarcity of 3D data in surgical scenes. Based on Surgical3D, we finetune a powerful geometric transformer to extract robust 3D latent representations from stereo endoscopes images. These representations are then seamlessly aligned with the robot's action space via a lightweight multi-level spatial feature connector (MSFC), all within an endoscope-centric coordinate frame. Extensive real-robot experiments demonstrate that SST achieves state-of-the-art performance and strong spatial generalization on complex surgical tasks such as knot tying and ex-vivo organ dissection, representing a significant step toward practical clinical deployment. The dataset and code will be released.

cs.CV

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

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e18ae2dc1f16c17ac055e2f2b4fd8ceeCVF · CVPR 2026

FlowFM: Advancing Dark Optical Flow Estimation with Flow Matching

Zuo, Fengyuan, Jin, Haiyan, Zhang, Yuanlin, Xiao, Zhaolin, Wang, Bin, Mu, Yuerong

原文摘要

Dark optical flow estimation (DOFE) faces critical challenges: discriminative models are less robust to noise and struggle with weakened motion patterns, while diffusion models suffer from discontinuous flow fields and low efficiency. Flow matching (FM), though efficient, remains underexplored for conditional generation in DOFE. In this paper, we propose FlowFM, the first flow matching model tailored to DOFE tasks. Instead of conventional vector field regression, FlowFM proposes estimating the global transformation path constrained by the ground truth optical flow. It generates noisy flow by mixing Gaussian noise with ground truth, then performs a one-step denoising process conditioned on the initial flow field, cost volume, and contextual features for optimal accuracy and efficiency. FlowFM incorporates an implicit Fourier denoising decoder (IFDD) for reliable motion understanding. By leveraging the Fourier transform, IFDD uses amplitude to characterize motion intensity and phase to encode target spatial relationships within flow fields, then directly enhances amplitude to restore dark-caused motion information loss. Experiments show that FlowFM significantly outperforms state-of-the-art methods on the FCDN and VBOF benchmarks, setting a new performance record for DOFE.

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:e18b690d11222c2e3db213ddba2928f7CVF · CVPR 2026

PureProof: Diffusion-Resistant Black-box Targeted Attack on Large Vision-Language Models

Cao, Yiming, Wang, Dong, Lyu, Xinqi, Xiao, Bin

原文摘要

Large Vision-Language Models (VLMs) are increasingly deployed across diverse applications, such as AI agents, yet remain vulnerable to targeted adversarial attacks. However, the practical robustness of such attacks often remains unclear with limited evaluation under defenses. Diffusion-based purification (DBP), a widely adopted black-box defense, effectively blocks current attacks by removing adversarial perturbations via generative diffusion. Prior DBP evasion attacks target white-box image classifiers and are ill-suited to VLMs, incurring high computational costs and gradient instability when adapted. In this paper, we present PureProof, a black-box targeted attack on VLMs resilient to DBP. It consists of three core components. Stochastic Reverse Alignment (SRA) guides adversarial optimization via single-step reverse prediction, avoiding costly full-trajectory backpropagation. Adaptive Re-noising Augmentation (ARA) mitigates diffusion stochasticity through timestep-adaptive re-noising. Self-Consistency Regularization (SCR) stabilizes optimization by promoting local temporal coherence. Extensive experiments on open-source and commercial VLMs show that PureProof consistently outperforms prior attacks against DBP and achieves strong noise resilience, revealing critical vulnerabilities in VLMs and offering broader safety implications for real-world VLM deployments, including emerging agentic settings.

cs.CV · cs.CL · cs.AI

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:e18d9bec114a4c24d0ee32b4c19bfb4dCVF · CVPR 2026

Towards Knowledge-augmented Bayesian Deep Learning For Computer Vision

Ma, Wang, Wang, Hanjing, Zhang, Yufei, Udayanga, Darsha, Ji, Qiang

原文摘要

Bayesian deep learning (BDL) integrates Bayesian inference with deep learning, improving predictive performance while enabling principled uncertainty quantification. However, existing BDLs often rely on non-informative random priors, limiting the benefits of Bayesian inference. In contrast, knowledge-augmented deep learning explicitly injects domain knowledge during training, yet lacks a probabilistic foundation. In this paper, we propose a knowledge-augmented BDL framework that integrates domain knowledge both as an informative prior and as an adaptive likelihood under a unified two-stage hybrid formulation. In the first stage, we learn a knowledge-informed prior p(\theta \mid \mathcal K ) by pre-training a model to satisfy domain-specific constraints. In the second stage, we perform Bayesian inference on task data with an adaptive knowledge likelihood p(\mathcal K \mid \theta, \mathcal D ), which dynamically enforces these constraints during optimization. This unified framework enables knowledge to guide both initialization and training, significantly improving prediction accuracy, robustness, adaptation and uncertainty estimation. Experiments on various computer vision tasks, including semi-synthetic and real-knowledge scenarios, demonstrate that our two-stage framework consistently outperforms state-of-the-art Bayesian and knowledge-augmented baselines.

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:e1acfe02bf6f152505bbc951e99cf880CVF · CVPR 2026

ConceptPose: Training-Free Zero-Shot Object Pose Estimation using Concept Vectors

Kuang, Liming, Velikova, Yordanka, Saleh, Mahdi, Zaech, Jan-Nico, Paudel, Danda Pani, Busam, Benjamin

原文摘要

Object pose estimation is a fundamental task in computer vision and robotics, yet most methods require extensive, dataset-specific training. Concurrently, large-scale vision language models show remarkable zero-shot capabilities. In this work, we bridge these two worlds by introducing ConceptPose, a framework for object pose estimation that is both training-free and model-free. ConceptPose leverages a vision-language-model (VLM) to create open-vocabulary 3D concept maps, where each point is tagged with a concept vector derived from saliency maps. By establishing robust 3D-3D correspondences across concept maps, our approach allows precise estimation of 6DoF relative pose. Without any object or dataset-specific training, our approach achieves state-of-the-art results on common zero shot relative pose estimation benchmarks, outperforming the strongest baseline by a relative 62% in average ADD(-S) score, including methods that utilize extensive dataset-specific training.

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:e1b15b20666d7d3edea9fa1d9d380743CVF · CVPR 2026

From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images

Zhang, Ruikun, Yang, Yan, Pan, Liyuan

原文摘要

Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding gene expression profile. However, these methods inherently lose the spatial resolution in gene expression:1) each spot often contains multiple cells with distinct gene expression profiles;2) spots are typically defined at fixed spatial resolutions, limiting the ability to predict gene expression at varying scales. To address these limitations, this paper presents PixNet, a dense prediction network capable of predicting spatially resolved gene expression across spots of varying sizes and scales directly from histopathology slide images. Different from previous methods that map individual spots to gene expression values, we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression. Our PixNet outperforms state-of-the-art methods on four common ST datasets in multiple spatial scales. The source code is available at: https://github.com/wangzrk/From-Spots-to-Pixels.

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:e1c7c2b0ba804bf0304d90a863fa720eCVF · CVPR 2026

Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selection

Qi, Dacheng, Wang, Chenyu, Xu, Jingwei, Chu, Tianzhe, Zhao, Zibo, Liu, Wen, Ding, Wenrui, Ma, Yi 等 9 位作者

原文摘要

Constructing computer-aided design (CAD) models is labor-intensive but essential for engineering and manufacturing. Recent advances in Large Language Models (LLMs) have inspired the LLM-based CAD generation by representing CAD as command sequences. But these methods struggle in practical scenarios because command sequence representation does not support entity selection (e.g. faces or edges), limiting its ability to support complex editing operations such as chamfer or fillet. Further, the discretization of a continuous variable during sketch and extrude operations may result in topological errors. To address these limitations, we present Pointer-CAD, a novel LLM-based CAD generation framework that leverages a pointer-based command sequence representation to explicitly incorporate the geometric information of B-rep models into sequential modeling. In particular, Pointer-CAD decomposes CAD model generation into steps, conditioning the generation of each subsequent step on both the textual description and the B-rep generated from previous steps. Whenever an operation requires the selection of a specific geometric entity, the LLM predicts a Pointer that selects the most feature-consistent candidate from the available set. Such a selection operation also reduces the quantization error in the command sequence-based representation. To support the training of Pointer-CAD, we develop a data annotation pipeline that produces expert-level natural language descriptions and apply it to build a dataset of approximately 575K CAD models. Extensive experimental results demonstrate that Pointer-CAD effectively supports the generation of complex geometric structures and reduces segmentation error to an extremely low level, achieving a significant improvement over prior command sequence methods, thereby significantly mitigating the topological inaccuracies introduced by quantization error. Our code is available at https://github.com/Snitro/Pointer-CAD.

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:e1d0aae5dff28b6d28e4c02f43087bceCVF · CVPR 2026

FoSS: Modeling Long-Range Dependencies and Multimodal Uncertainty in Trajectory Prediction via Fourier-State Space Integration

Huang, Yizhou, Jiang, Genze, Cheng, Yihua, Wang, Kezhi

原文摘要

Accurate trajectory prediction is vital for safe autonomous driving, yet existing approaches struggle to balance modeling power and computational efficiency. Attention-based architectures incur quadratic complexity with increasing agents, while recurrent models struggle to capture long-range dependencies and fine-grained local dynamics. Building upon this, we present FoSS, a dual-branch framework that unifies frequency-domain reasoning with linear-time sequence modeling. The frequency-domain branch performs a discrete Fourier transform to decompose trajectories into amplitude components encoding global intent and phase components capturing local variations, followed by a progressive helix reordering module that preserves spectral order; two selective state-space submodules, Coarse2Fine-SSM and SpecEvolve-SSM, refine spectral features with \mathcal O (N) complexity. In parallel, a time-domain dynamic selective SSM reconstructs self-attention behavior in linear time to retain long-range temporal context. A cross-attention layer fuses temporal and spectral representations, while learnable queries generate multiple candidate trajectories, and a weighted fusion head expresses motion uncertainty. Experiments on Argoverse 1 and Argoverse 2 benchmarks demonstrate that FoSS achieves state-of-the-art accuracy while reducing computation by 22.5% and parameters by over 40%. Comprehensive ablations confirm the necessity of each component.

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:e1ddea4af22bf7241a6b1a4591c4bca9CVF · CVPR 2026

Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation

Wolf, Filip, Rolih, Blaž, Zajc, Luka Čehovin

原文摘要

Foundation models are transforming Earth Observation (EO), yet the diversity of EO sensors and modalities makes a single universal model unrealistic. Multiple specialized EO foundation models (EOFMs) will likely coexist, making efficient knowledge transfer across modalities essential. Most existing EO pretraining relies on masked image modeling, which emphasizes local reconstruction but provides limited control over global semantic structure. To address this, we propose a dual-teacher contrastive distillation framework for multispectral imagery that aligns the student's pretraining objective with the contrastive self-distillation paradigm of modern optical vision foundation models (VFMs). Our approach combines a multispectral teacher with an optical VFM teacher, enabling coherent cross-modal representation learning. Experiments across diverse optical and multispectral benchmarks show that our model adapts to multispectral data without compromising performance on optical-only inputs, achieving state-of-the-art results in both settings, with average improvements of 3.64 percentage points in semantic segmentation, 1.2 in change detection, and 1.31 in classification. This demonstrates that contrastive distillation provides a principled and efficient approach to scalable representation learning across heterogeneous EO data sources.

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Thermal Diffusion Matters: Infrared Spatial-Temporal Video Super-Resolution through Heat Conduction Priors

Zhou, Mingxuan, Li, Shuang, Zhang, Yutang, Geng, Jing, Shen, Yirui, Kang, Jingxuan, Zhuang, Fuzhen, Wang, Shuigen

原文摘要

Infrared video acquisition inherently suffers from low spatial resolution and limited frame rates due to the physical constraints of thermal imaging sensors. These limitations make infrared video enhancement uniquely challenging, as it requires restoring spatial details and temporal continuity from highly undersampled thermal signals. To address this challenge, we propose THERIS, a unified THERmal-physics inspired framework for Infrared spatial-temporal video Super-resolution. Grounded in the physical principles of thermal diffusion, THERIS leverages heat conduction dynamics that govern the spatiotemporal evolution of infrared pixel intensities. Specifically, the proposed Thermal Diffusion Interpolation Module (TDIM) treats temporal feature sequences as one-dimensional heat fields and performs frequency-domain diffusion to synthesize temporally coherent intermediate frames. Building on this foundation, the Thermo-Aware State Space Module (TSSM) refines spatiotemporal representations through learnable spectral filtering and selective state-space modeling, while maintaining consistency guided by the thermodynamic prior inherited from TDIM. Additionally, a Temperature Field Modeling Loss is introduced to enforce adherence to the heat conduction equation, promoting temporal coherence and spatial stability in the generated results. Extensive experiments demonstrate that THERIS achieves state-of-the-art performance while producing visually coherent results. To facilitate further research in the infrared video processing domain, we also introduce IRVAL, a high-resolution dataset comprising 108,512 video frames at 512x512 resolution.

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ORIC: Benchmarking Object Recognition under Contextual Incongruity in Large Vision-Language Models

Li, Zhaoyang, Ling, Zhan, Zhou, Yuchen, Gong, Litian, Biyik, Erdem, Su, Hao

原文摘要

Large Vision-Language Models (LVLMs) excel at captioning, visual question answering, and robotics by combining vision and language, yet they often miss obvious objects or hallucinate nonexistent ones in atypical scenes. We examine these failures through the lens of uncertainty, focusing on contextual incongruity, where objects appear unexpectedly or fail to appear in expected contexts, and show that such cases increase recognition difficulty for state-of-the- art LVLMs. To study this regime, we introduce the Object Recognition in Incongruous Context (ORIC) framework, which constructs incongruous object-context pairs through two complementary strategies: (1) LLM-guided sampling to identify hard-to-recognize objects present in the image and (2) CLIP-guided sampling to mine plausible but absent ones. Applied to MSCOCO, ORIC creates ORIC-Bench and ORIC-style training data. Evaluating 18 LVLMs and 2 open-vocabulary detectors reveals significant degradation and bias under incongruous contexts. Visual Reinforcement Fine-Tuning of Qwen3-VL-8B-Instruct on 600 ORIC sam- ples improves performance on ORIC-Bench, AMBER, and HallusionBench. Overall, we show that contextual incongruity is a key source of uncertainty and provide tools for more reliable LVLMs. The dataset and code are publicly available at https://github.com/ZhaoyangLi-1/ORIC.

cs.CV · cs.CL

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PolySLGen: Online Multimodal Speaking-Listening Reaction Generation in Polyadic Interaction

Lin, Zhi-Yi, Markhorst, Thomas, Chew, Jouh Yeong, Zhang, Xucong

原文摘要

Human-like multimodal reaction generation is essential for natural group interactions between humans and intelligent embodied AI. However, existing approaches are often limited to single-modality or speaking-only responses in dyadic interactions, making them unsuitable for realistic social scenarios. Many also overlook nonverbal cues and the complex dynamics of polyadic interactions, both critical for engagement and conversational coherence. In this work, we present PolySLGen, an online framework for Polyadic multimodal Speaking and Listening reaction Generation. Given past conversation and motion from all participants, PolySLGen generates a future speaking or listening reaction for a target participant, including speech, body motion, and speaking state score.To model group interactions effectively, we propose a pose fusion module and a social cue encoder that jointly aggregate motion and social signals from the group. Extensive experiments, along with quantitative and qualitative evaluations, show that PolySLGen produces contextually appropriate and temporally coherent multimodal reactions, outperforming several adapted and state-of-the-art baselines in motion quality, motion-speech alignment, speaking state prediction, and human-perceived realism.

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IF-Bench: Benchmarking and Enhancing MLLMs for Infrared Images with Generative Visual Prompting

Zhang, Tao, Hong, Yuyang, Xia, Yang, Ding, Kun, Zhang, Zeyu, Wang, Ying, Xiang, Shiming, Pan, Chunhong

原文摘要

Recent advances in multimodal large language models (MLLMs) have led to impressive progress across various benchmarks. However, their capability in understanding infrared images remains unexplored. To address this gap, we introduce **IF-Bench**, the first high-quality benchmark designed for evaluating multimodal understanding of infrared images. IF-Bench consists of 499 images sourced from 23 infrared datasets and 680 carefully curated visual question-answer pairs, covering 10 essential dimensions of image understanding. Based on this benchmark, we systematically evaluate over 40 open-source and closed-source MLLMs, employing cyclic evaluation, bilingual assessment, and hybrid judgment strategies to enhance the reliability of the results. Our analysis reveals how model scale, architecture, and inference paradigms affect infrared image comprehension, providing valuable insights for this area. Furthermore, we propose a training-free generative visual prompting (**GenViP**) method, which leverages advanced image editing models to translate infrared images into semantically and spatially aligned RGB counterparts, thereby mitigating domain distribution shifts. Extensive experiments demonstrate that our method consistently yields significant performance improvements across a wide range of MLLMs.

cs.CV · cs.CL

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WildCap: Facial Albedo Capture in the Wild via Hybrid Inverse Rendering

Han, Yuxuan, Ming, Xin, Li, Tianxiao, Shen, Zhuofan, Zhang, Qixuan, Xu, Lan, Xu, Feng

原文摘要

Existing methods achieve high-quality facial albedo capture under controllable lighting, which increases capture cost and limits usability. We propose WildCap, a novel method for high-quality facial albedo capture from a smartphone video recorded in the wild. To disentangle high-quality albedo from complex lighting effects in in-the-wild captures, we propose a novel hybrid inverse rendering framework. We first apply a data-driven method, i.e., SwitchLight, to convert the captured images into more constrained conditions and then adopt model-based inverse rendering. However, unavoidable local artifacts in network predictions, such as shadow-baking, are non-physical and thus hinder accurate inverse rendering of lighting and material. To address this, we propose a novel texel grid lighting model to explain non-physical effects as clean albedo illuminated by local physical lighting. During optimization, we jointly sample a diffusion prior for the albedo map and optimize the lighting, effectively resolving scale ambiguity between local lights and albedo. Other reflectance maps are then predicted from the albedo. Our method achieves significantly better results than prior arts in the same capture setup, closing the quality gap between in-the-wild and controllable recordings by a large margin.

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Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

Gedik, Hakan Emre, Gupta, Shashank, Bovik, Alan

原文摘要

No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly trained on heterogeneous IQA datasets with mismatched MOS scales, or require prohibitive computation. We present ReLIQS, a model for Resolution-agnostic Learning for Image Quality with Saliency, which is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive. ReLIQS is a CLIP-based multiscale patch-driven architecture that learns both where to look and how to judge quality. Fixed-size patches are sampled across multiple resolutions, including the original resolution, and encoded with a CLIP vision backbone. A lightweight Perceptual Importance Estimator then predicts IQA-specific importance maps to select a small set of informative patches, and a Quality Aspect Module aggregates their embeddings into a single image-level score. Across authentic, synthetic, and AIGC benchmarks spanning diverse resolutions and distortions, ReLIQS generalizes better than strong CNN-, CLIP-, and MLLM-based baselines with matching or reduced computational cost.

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Incentivizing Versatile Video Reasoning in MLLMs via Data-Efficient Reinforcement Learning

Wang, Xiaodong, Wu, Zhirong, Huang, Langling, Zheng, Yuxi, Peng, Peixi

原文摘要

Multimodal Large Language Models (MLLMs) have made great progress in video understanding tasks. However, when it comes to understanding complex or lengthy videos, MLLMs tend to overlook details or produce hallucinations. To alleviate these issues, recent work has attempted to leverage reinforcement learning (RL) to boost models' deep linguistic reasoning of complex videos. But these methods have two main problems: First, the RL framework they used has unstable training, high training costs, and is difficult to train satisfactory video reasoning models; Second, the linguistic reasoning process is difficult to guarantee the reliability of visual information. To alleviate these problems, we propose to use multimodal elements for reasoning, and we design a novel framework to build and enhance versatile video reasoning capabilities on MLLMs. We carefully design a multi-task cold start and multi-task reinforcement learning to improve the model's visual perception and proficiency in multiple capabilities. In the inference phase, we leverage multimodal reasoning and dynamic sampling to further improve the performance. We verified the efficiency of the framework on a base MLLM (Qwen2-VL-7B-Base). Through cold-start with 3k data and reinforcement learning training with 5k data, combined with inference design, our final model significantly outperforms the base model on seven public video benchmarks, even surpassing and approaching the state-of-the-art Instruct Models such as Qwen2.5-VL-7B-Instruct trained with large-scale data. Our code will be available at https://github.com/Wang-Xiaodong1899/VideoReasoner.

cs.CV · cs.CL

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Fractal Camouflage: A Bio-Inspired Approach for Multi-Scale Adversarial Attacks in the Infrared Domain

Hu, Chengyin, Wang, Xin, Qiu, Rui, Jia, Zhe, Zhao, Yingying, Wang, Kai, Kang, Xu, Wei, Yiwei

原文摘要

Infrared pedestrian detection is crucial in safety-critical systems but remains vulnerable to adversarial attacks. Existing physical attacks often rely on fixed, static patterns. However, they often lack robustness across scales, as their hand-crafted or uniformly generated structures are fundamentally limited by a fixed receptive field and fail to adapt to varying distances and scene contexts. In light of this, we propose AdvFractal, a black-box attack that exploits the innate self-similarity and structural richness of fractal geometry to naturally generate multi-scale, physically realizable adversarial perturbations. By modeling perturbations with H-type fractals and optimizing parameters via Particle Swarm Optimization, AdvFractal seamlessly coordinates attacks across scales, progressively disrupting detector features from local textures to global shapes. Experiments show AdvFractal achieves an attack success rate (ASR) of 97.54% in the physical domain and 99.16% cross-dataset, significantly outperforming state-of-the-art methods. The perturbations are highly effective in the infrared spectrum while remaining stealthy in visible light, offering a novel approach for evaluating and understanding the security of infrared detection systems.

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Planning in 8 Tokens: A Compact Discrete Tokenizer for Latent World Model

Kim, Dongwon, Seo, Gawon, Lee, Jinsung, Cho, Minsu, Kwak, Suha

原文摘要

World models provide a powerful framework for simulating environment dynamics conditioned on actions or instructions, enabling downstream tasks such as action planning or policy learning.Recent approaches leverage world models as learned simulators, but its application to decision-time planning remains computationally prohibitive for real-time control.A key bottleneck lies in latent representations: conventional tokenizers encode each observation into hundreds of tokens, making planning both slow and resource-intensive.To address this, we propose CompACT, a discrete tokenizer that compresses each observation into as few as 8 tokens, drastically reducing computational cost while preserving essential information for planning.An action-conditioned world model that occupies CompACT tokenizer achieves competitive planning performance with orders-of-magnitude faster planning, offering a practical step toward real-world deployment of world models.

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PhysGS: Bayesian-Inferred Gaussian Splatting for Physical Property Estimation

Chopra, Samarth, Liang, Jing, Seneviratne, Gershom, Manocha, Dinesh

原文摘要

Understanding physical properties such as friction, stiffness, hardness, and material composition is essential for enabling robots to interact safely and effectively with their surroundings. However, existing 3D reconstruction methods focus on geometry and appearance and cannot infer these underlying physical properties. We present PhysGS, a Bayesian-inferred extension of 3D Gaussian Splatting that estimates dense, per-point physical properties from visual cues and vision--language priors. We formulate property estimation as Bayesian inference over Gaussian splats, where material and property beliefs are iteratively refined as new observations arrive. PhysGS also models aleatoric and epistemic uncertainties, enabling uncertainty-aware object and scene interpretation. Across object-scale (ABO-500), indoor, and outdoor real-world datasets, PhysGS improves accuracy of the mass estimation by up to 22.8%, reduces Shore hardness error by up to 61.2%, and lowers kinetic friction error by up to 18.1% compared to deterministic baselines. Our results demonstrate that PhysGS unifies 3D reconstruction, uncertainty modeling, and physical reasoning in a single, spatially continuous framework for dense physical property estimation.

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Reflection Separation from a Single Image via Joint Latent Diffusion

Huang, Zheng-Hui, Wang, Zhixiang, Liu, Yu-Lun, Chuang, Yung-Yu

原文摘要

Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to recover both layers in glare or weak-reflection scenarios because of insufficient information. This paper presents a diffusion model explicitly fine-tuned for this task, leveraging generative diffusion priors for robust separation. Our method simultaneously generates transmission and reflection layers through a unified diffusion model, incorporating a novel cross-layer self-attention mechanism for better feature disentanglement. We further introduce a disjoint sampling strategy to iteratively reduce interference between the layers during diffusion and a latent optimization step with a learned composition function for improved results in complex real-world scenarios. Extensive experiments demonstrate that our approach surpasses state-of-the-art methods on multiple real-world benchmarks. Project page: https://brian90709.github.io/diff-reflection-separation/

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EasyOmnimatte: Taming Pretrained Inpainting Diffusion Models for End-to-End Video Layered Decompositio

Hu, Yihan, Chen, Xuelin, Cun, Xiaodong

原文摘要

Existing video omnimatte methods typically rely on slow, multi-stage, or inference-time optimization pipelines that fail to fully exploit powerful generative priors, producing suboptimal decompositions. Our key insight is that, if a video inpainting model can be finetuned to remove the foreground-associated effects, then it must be inherently capable of perceiving these effects, and hence can also be finetuned for the complementary task: foreground layer decomposition with associated effects. However, although naively finetuning the inpainting model with LoRA applied to all blocks can produce high-quality alpha mattes, it fails to capture associated effects. Our systematic analysis reveals this arises because effect-related cues are primarily encoded in specific DiT blocks and become suppressed when LoRA is applied across all blocks. To address this, we introduce EasyOmnimatte, the first unified, end-to-end video omnimatte method. Concretely, we finetune a pretrained video inpainting diffusion model to learn dual complementary experts while keeping its original weights intact: an Effect Expert, where LoRA is applied only to effect-sensitive DiT blocks to capture the coarse structure of the foreground and associated effects, and a fully LoRA-finetuned Quality Expert learns to refine the alpha matte. During sampling, Effect Expert is used for denoising at early, high-noise steps, while Quality Expert takes over at later, low-noise steps. This design eliminates the need for two full diffusion passes, significantly reducing computational cost without compromising output quality. Ablation studies validate the effectiveness of this Dual-Expert strategy. Experiments demonstrate that EasyOmnimatte sets a new state-of-the-art for video omnimatte and enables various downstream tasks, significantly outperforming baselines in both quality and efficiency.

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