Computer Vision
ShellMaker: Language-Guided Exterior Completion under Structural Constraints
Ruiqi Xu and Daniel Aliaga
Despite advances in indoor scene generation, synthesizing coherent building exteriors consistent with generated interiors remains largely unexplored. Existing methods can generate floor plans and wall layouts but typically stop at a structural shell, lacking stylistically consistent facades and roofs. Completing these exteriors is challenging because the footprint, wall geometry, and opening semantics must remain fixed-constraints that unconstrained generative models often violate. We introduce ShellMaker, a
language-guided exterior completion framework that operates under these structural constraints. Given a building scaffold and a text style
prompt, ShellMaker generates a complete exterior mesh with PBR materials by combining parametric roof generation, LLM-based part-aware
prompt refinement, joint wall-roof material
retrieval, and geometry-aware assembly. Operating on a format agnostic scaffold representation, ShellMaker generalizes to indoor generators, CityGML, and CAD inputs, while maintaining structural consistency and improving architectural coherence over
retrieval and unconstrained generative baselines. The project page is available at https://ruiqixu37.github.io/ShellMaker_web/
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Computer Vision · Cross-listed from cs.GR
Practical High-Fidelity Novel-View Synthesis of Mounted Lepidoptera
Kristof Overdulve, Lode Jorissen and Nick Michiels
Mounted butterflies are among the most striking objects in natural history collections. However, their beauty is notoriously hard to digitize in 3D: they are small and fragile, with microscopic hairs and vein structures. Capturing them in sufficient detail, therefore, requires a macro lens, which has a very limited Depth of Field (DoF). Moreover, a camera body cannot be maneuvered beneath a pinned specimen to photograph its ventral surface. We introduce an end-to-end pipeline that resolves these challenges, turning such specimens into photorealistic 3D models viewable from every direction. It combines three ingredients: handheld focus stacking for all-in-focus macro capture without a tripod, a non-contact first-surface mirror system that exposes the ventral surface without touching the specimen, and a
segmentation-free, mirror-aware 3D Gaussian Splatting extension. We validate the reconstructions and design decisions on nine diverse specimens.
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Computer Vision
REDI: Corpus Aware Patch Ranking for DINOv3 Token Reduction
Chanjong Im, Sebastian Diem and Thomas Mandl
Most
token reduction methods for
Vision Transformers seek favorable tradeoffs between accuracy and efficiency by
pruning, merging, or pooling patch
tokens. REDI (Relevance for DINOv3
Token Reduction) studies this question through a controlled supervised reference: how should a fixed
token budget be allocated across patches for image classification? REDI quantizes final block DINOv3 patch representations into a visual vocabulary and derives class conditioned corpus scores using supervised TF-IDF over visual words. For each validation image, the ground truth class selects a row of the TF-IDF table, and four transformed views produce a TF-IDF map aligned to a reference center crop. A separate dense pass on the same crop provides an
attention map. After independent min max normalization, their elementwise product defines the REDI score. A fixed keep, merge, and compress operator then uses score rank to assign patch roles and score magnitude to weight merging and compression. With precomputed REDI scores, a frozen DINOv3 ViT-B/16 backbone, and the same linear classifier used for dense evaluation, the operator reduces the sequence length from 201 to 107
tokens, a 46.8% sequence reduction. The REDI variant based on incoming
attention mass achieves 84.706% Top-1 accuracy on ImageNet-1K, compared with 83.514% for the dense baseline, 82.634% for incoming
attention mass alone, and 81.796% for supervised TF-IDF alone. The same corpus term also improves reduced classification for three alternative
attention formulations relative to their
attention only counterparts. Together, these controlled comparisons indicate that class specific corpus statistics and image specific
attention provide complementary signals for patch ranking in this setting.
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Computer Vision
WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models
Ting-Bing Xu, Jiacheng Sui, Zhe Gao et al. (+12 more)
Despite rapid progress in interactive world models (IWMs), short-horizon performance does not establish sustained action following, visual stability, physical plausibility, or
memory. We introduce WorldRoamBench, an open-world
benchmark for long-horizon stability across four dimensions, each with innovations: (i) Action: per-frame action metric bypassing cross-model semantic scale disparity and exposing failures hidden by trajectory; (ii)
Vision: sliding-window drift metric capturing non-monotonic mid-sequence collapse missed by start-vs-end comparisons; (iii) Physics: evaluation of physical plausibility across mechanics, optics, and 3D consistency, gated by camera-motion and subject-tracking checks; (iv)
Memory: a trajectory-aware protocol reducing confounding from action-following errors, evaluating scene
memory via transition-localized 3D point-cloud reconstruction and subject
memory via tracking-plus-VLM
reasoning. The
benchmark comprises 1000+ test cases across Nature, Urban, and Indoor scenes in first/third-person views with WASD 10-60 s continuous interaction. Evaluating 10+ open/closed-source models reveals none reliably satisfies all dimensions; even the best achieves only moderate scores. Advances on WorldRoamBench are steps toward IWMs that are stable, physically grounded,
memory-faithful, and deployable in real-world applications.
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All authors Ting-Bing Xu, Jiacheng Sui, Zhe Gao, Kewei Shi, Wenjin Yang, Zhicheng Liu, Zhaoxu Sun, Mingchao Sun, Hongyu Pan, Fan Jiang, Mu Xu, Qi Fan, Yang Gao, Yong Li, Baoquan Chen
Computer Vision
SAMBA: A Scatter-Guided Masked Bidirectional Mamba Foundation Model for SAR Target Recognition
Ke Wang, Xiaoyi Pan, Zhaoyu Gu et al. (+4 more)
Synthetic aperture radar automatic target recognition (SAR ATR) is critical for Earth observation and defense, but its practical deployment is constrained by scarce annotated training data. Self-supervised pre-training alleviates this label bottleneck, yet prevailing
Transformer architectures incur prohibitive quadratic computational complexity, and conventional universal masking neglects the unique electromagnetic scattering properties intrinsic to SAR imagery. To address these limitations, we propose SAMBA (Scattering-Guided Bidirectional Mamba), an efficient self-supervised pre-training foundation model for SAR target interpretation. Our framework features three core innovations: (i) a linear-complexity Mamba encoder with a mid-sequence class
token to mitigate computational bottlenecks; (ii) a three-level hierarchical Scattering-Guided Masked Autoencoder (SG-MAE) masking strategy guided by SAR physical priors, aligning the pretext task with SAR's intrinsic imaging mechanism; (iii) a lightweight SpatialMix feature interaction module to enhance cross-region feature fusion. We also design a two-stage cross-domain pre-training pipeline to optimize the overall pre-training process. Extensive evaluations demonstrate that SAMBA consistently delivers superior performance across all pre-training configurations, with substantially fewer parameters than both CNN and
Transformer baselines. Compared with the default masking strategy in standard MAE, the proposed SG-MAE strategy further boosts the model's few-shot transfer capability. Benchmarking on seven downstream
datasets covering classification and detection tasks shows SAMBA achieves state-of-the-art (SOTA) performance on most metrics, fully validating its robust generalizability across diverse SAR interpretation tasks. Source code and pre-trained weights are publicly available at https://github.com/mynswkk/SAMBA.
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All authors Ke Wang, Xiaoyi Pan, Zhaoyu Gu, Xiaofeng Ai, Zhiming Xu, Feng Zhao, Shunping Xiao
Computer Vision
Sparsity-Inducing Divergence Losses for Biometric Verification
Dimitrios Koutsianos, Ladislav Mošner, Yannis Panagakis et al. (+1 more)
Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace. Recently introduced $\alpha$-divergence loss functions offer a compelling alternative, particularly due to their ability to induce sparse solutions (when $\alpha>1$). However, standard
geometric margins are designed for the softmax function and do not naturally extend to this generalized probabilistic framework. In this paper we propose Q-Margin, a novel $\alpha$-divergence loss that introduces a principled probabilistic margin. Unlike conventional methods that apply
geometric penalties to the logits (unnormalized log-likelihoods), Q-Margin encodes the margin penalty directly into the reference measure (prior probabilities). This formulation naturally encourages discriminative
embeddings while preserving the beneficial sparsity properties of the $\alpha$-divergence. We demonstrate that Q-Margin achieves competitive or superior performance on the challenging IJB-B and IJB-C face verification
benchmarks and similarly strong results in speaker verification on VoxCeleb. Crucially, against ArcFace and CosFace baselines trained under an identical recipe, Q-Margin consistently improves at low False Acceptance Rates (FARs), a capability critical for practical high-security applications. Finally, the extreme sparsity of the Q-Margin posteriors enables exact and
memory-efficient training, offering a scalable solution for
datasets with millions of identities.
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All authors Dimitrios Koutsianos, Ladislav Mošner, Yannis Panagakis, Themos Stafylakis
Computer Vision · Cross-listed from cs.RO
DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
Wen Jiang, Hanfang Liang, Li Wang et al. (+9 more)
Recent advances in
multimodal large models have significantly improved
UAV vision-
language navigation (
UAV-VLN) by enhancing high-level perception and
reasoning. However, existing methods mainly focus on predicting discrete actions, local targets, or sparse waypoints, while the continuous transition from
navigation intent to executable
UAV motion remains weakly modeled. This motion-interface gap limits the continuity, stability, and executability of generated
UAV trajectories. To address this gap, we propose DynFly, a dynamic-aware continuous trajectory generation framework that bridges high-level
navigation reasoning and executable
UAV motion. DynFly bridges high-level
navigation intent and continuous
UAV motion through a lightweight trajectory generation layer. Specifically, it represents expert trajectories in B-spline control-point space and employs a Spline-DiT generator to learn conditional trajectory generation via flow matching. Furthermore, we introduce
UAV-oriented dynamic-aware supervision over position, finite-difference velocity, finite-difference acceleration, heading consistency, and local target
alignment, enabling the generated trajectories to better satisfy
UAV motion characteristics. And our trajectory generation framework can also be integrated with an existing
UAV-VLN framework while preserving its original visual-
language reasoning pipeline. Extensive experiments on the OpenUAV
UAV-VLN
benchmark show that DynFly improves both
navigation performance and trajectory quality. On the Test Unseen Full split, DynFly improves the strongest baseline by 4.69 NDTW, 2.40 SDTW, 2.14 SR points and 4.87 OSR points, while reducing NE by 4.51 m.
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All authors Wen Jiang, Hanfang Liang, Li Wang, Kangyao Huang, Wang Xu, Wei Fan, Jinyuan Liu, Shaoyu Liu, Hongwei Duan, Bin Xu, Xiangyang Ji, Huaping Liu
Computer Vision
Technical Report of RoboSpatial Challenge at CVPR 2026: Selective Reasoning Activation and Reference-Frame Disambiguation for Embodied Spatial Reasoning
Yuxiang Xie, Qi Lv, Jianming Xing et al. (+4 more)
Vision-
language models achieve strong general perception but often struggle with the spatial
reasoning required for
embodied tasks. We present RoboSpatialBrain, our submission to the RoboSpatial Challenge at the
Embodied Reasoning in Action Workshop, CVPR 2026, built on RoboBrain2.5-8B-NV. RoboSpatialBrain combines two training-free, inference-time mechanisms: a forced <think> prefix activation strategy paired with a task-specific post-
prompt that elicits deliberate
reasoning on context and compatibility tasks, and an explicit reference-frame redirection pipeline that resolves camera-centric and object-centric ambiguity for context tasks. We additionally explore fine-tuning RoboBrain2.5 on compatibility data and present a detailed analysis of its interaction with prompting. RoboSpatialBrain achieved first place in the RoboSpatial Challenge, with an overall success rate of 80.9% on RoboSpatial-Home. Code is available at https://github.com/YuxiangXie2003/RoboSpatialBrain.
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All authors Yuxiang Xie, Qi Lv, Jianming Xing, Zijian Hong, Xiang Deng, Weili Guan, Liqiang Nie
Computer Vision
LiteMatch: Lightweight Zero-Shot Stereo Matching via Cost Volume Stabilization
Md Raqib Khan, Santosh Kumar Vipparthi and Subrahmanyam Murala
Despite rapid progress in learning-based stereo matching, high accuracy is often achieved at the cost of heavy backbones and computationally intensive 3D cost volume processing, resulting in substantial
memory and runtime overhead. More critically, these methods frequently struggle to generalize across domains, limiting their practical deployment. We present LiteMatch, a lightweight stereo matching framework that achieves strong zero-shot generalization through cost volume stabilization-without expensive 3D convolutions. LiteMatch employs two complementary encoders: a Cross-View Correspondence Encoder (CVCE) to capture global cross-view interactions, and a High-Frequency Encoder (HFE) that enhances fine structural details via FFT-based frequency cues. To stabilize the cost volume, we introduce the Cost Volume Consistency Loss (CVC-Loss), a voxel-wise binary cross-
entropy objective applied to softmax-normalized cost distributions. By encouraging sharp and unimodal disparity probabilities, CVC-Loss promotes stable cost distributions and enables rapid convergence. A lightweight refinement module further produces sharp full-resolution disparities with low-iteration updates, avoiding heavy recurrent refinement. With a flexible design ranging from 3.36M to 9.58M parameters, LiteMatch achieves exceptional zero-shot generalization, delivering competitive EPE and D1 performance across Scene Flow, KITTI, Middlebury, ETH3D, and DrivingStereo. Our results establish that lightweight architectures can indeed generalize across domains without sacrificing accuracy. \href{https://mdraqibkhan.github.io/Litematch}{\textcolor{blue}{Code}}
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Computer Vision
PrISM-IQA: Image Quality Assessment Made Practical for Smartphone Photography
Shuyan Zhai, Jiaqi He, Weixia Zhang et al. (+4 more)
Existing smartphone image quality assessment (IQA) methods commonly reduce perceptual quality to a single score. However, this scalar formulation is poorly aligned with practical image signal processor (ISP) tuning, where engineers must identify specific quality issues, estimate their severities, and determine whether they are acceptable or require intervention. In this work, we introduce a Practical ISP-aware Structured Model for IQA (PrISM-IQA), which reformulates smartphone IQA as a multi-issue ordinal diagnosis problem. Rather than regressing a single quality score, PrISM-IQA predicts an ordered severity level – absent, minor, severe, or critical – for each ISP-relevant issue, covering both global image-level artifacts and local content-dependent defects. To produce logically consistent predictions, PrISM-IQA combines cumulative ordinal encoding with structured inference that captures within-issue monotonicity as well as cross-issue subsumption and exclusion relations. We evaluate PrISM-IQA on a reconstructed SPAQ
benchmark annotated with $53$ ISP-relevant quality issues and on a small-scale expert-annotated real-world
dataset. Experimental results demonstrate the effectiveness of PrISM-IQA for practical issue-level diagnosis, reveal transferable perceptual quality representations through linear probing, and further show how its predictions can support actionable and meaningful ISP tuning.
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All authors Shuyan Zhai, Jiaqi He, Weixia Zhang, Liang Wang, Zhenjie Lee, Zufeng Zhang, Kede Ma
Computer Vision
Robust Autonomous UAV Landing on Maritime Platforms via Multimodal Agentic AI and Active Wave Compensation
Francisco S. Neves, Pedro N. Pereira, Raul D.S.G. Campilho et al. (+1 more)
Autonomous aerial inspection of marine infrastructure is frequently compromised by
stochastic sea states, introducing risks of high-kinetic impacts, post-landing toppling, and sensory occlusion. This paper proposes a decoupled, multi-vehicle landing framework synchronizing an Unmanned Surface Vehicle (USV) equipped with a 3-RPU stabilized platform with a robust Unmanned Aerial Vehicle (
UAV). The architecture utilizes two independent Deep Reinforcement Learning (DRL)
agents: a Soft Actor-Critic (SAC)
agent providing high-frequency wave-motion compensation for the landing deck, and a
multimodal RL
agent for the
UAVs final approach. Evaluated in high-fidelity maritime simulations, the system achieved a 100% landing success rate across 15 trials in wave states varying from calm to rough. Results show a mean stabilization efficacy of 87.8%, maintaining the landing surface within 1 degree of the horizontal plane for 96% of the mission duration in rough conditions, effectively contributing to safer landings.
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All authors Francisco S. Neves, Pedro N. Pereira, Raul D.S.G. Campilho, Andry M. Pinto
Computer Vision
What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States
Chen Liu, Ling Chen, Hanzhang Zhou et al. (+7 more)
Mobile GUI
agents increasingly face long-horizon tasks that require reading, updating, and reusing task-relevant data across pages and applications. Existing methods treat
memory largely as passive storage, where past observations are accumulated and retrieved when needed. Yet retrieving a value does not reveal its current role in the workflow. The
agent must still infer from accumulated records whether the value should be used now, has already been used, or must wait for a later dependency. This implicit reconstruction becomes unreliable in long trajectories with repeated values, distractors, and outdated states, causing repeated or missed operations. To address this, we propose Active Task Driving
Memory (ATMem), which shifts GUI-
agent memory from passive storage to an actively maintained execution state. ATMem maintains task-relevant information as a continually updated execution state that links each value to its role and current status, enabling action selection based on the current workflow state. While supervised fine-tuning enables the
agent to construct ATMem, it does not teach when ATMem is beneficial. We therefore introduce STR-GRPO, an online reinforcement learning method that encourages selective use of ATMem based on its contribution to task completion. STR-GRPO contrasts
memory-on and
memory-off rollouts to estimate when
memory use improves execution, while
memory-cost-aware reward discourages costly
memory usage that does not improve execution. To evaluate whether
agents can complete all in-scope work while avoiding out-of-scope actions, we build a challenging mobile
benchmark. From a list of near identical entries,
agents must act on every entry that satisfies the instruction and reject entries that violate its constraints. We further introduce App-Level Progress and Scope-Aware F1 to measure these two dimensions separately.
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All authors Chen Liu, Ling Chen, Hanzhang Zhou, Xu Zhang, Quyu Kong, Panrong Tong, Wenhao Wang, Xin Yu, Steven Hoi, Yue Wang
Computer Vision
Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation
Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis et al. (+2 more)
Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic
segmentation challenging. Most existing radar
segmentation methods rely on grid-based encodings and pairwise interactions, which struggle to capture the higher-order relational structure formed by multiple radar returns from the same physical object. We introduce a unified higher-order structural
alignment framework for multi-view radar
segmentation. The proposed method refines radar feature representations using learnable hypergraphs to capture higher-order dependencies among spatially related responses. To ensure consistency across heterogeneous radar projections, we further align view-specific features using Unbalanced Optimal Transport (UOT), enabling correspondence-free
alignment under varying measurement densities and partial observations. An adaptive
attention mechanism then fuses complementary radar views while emphasising structurally informative responses under sparsity and noise. The resulting architecture learns structurally consistent representations across Range Angle (RA), Range Doppler (RD), and Angle Doppler (AD) views and is trained using supervised
segmentation together with cross-view consistency regularisation. Experiments on the CARRADA and RADIal
benchmarks demonstrate consistent improvements over strong radar-specific baselines, achieving 63.8% mIoU on CARRADA and 83.4% mIoU on RADIal, improving the previous best methods by +1.7 and +2.3 mIoU, respectively. These results highlight the importance of higher-order relational modelling for robust radar perception.
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All authors Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis, Usman Ali, Abdul Rehman
Computer Vision
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Nikolai Röhrich, Julian Gleißner, Ahmed H. A. Ibrahim et al. (+2 more)
Semantic
segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While synthetic augmentation is an appealing solution, directly generating new labeled data risks misalignment of labels and generated pixels. Existing solutions to this problem often rely on external models, or employ coarse heuristics such as indiscriminately augmenting all foreground objects or entire backgrounds, which wastes capacity on uninformative pixels. To address this, we propose an uncertainty-guided synthetic context augmentation strategy that strictly preserves label validity and efficiently maximizes pixel informativeness per synthetic sample - no external guardrails required. Using a baseline segmenter's predictive
entropy, we identify uncertain semantic regions and inpaint only the complementary visual context. When fine-tuning the segmenter on this synthetic data, we compute the loss only over the original pixels, excluding inpainted regions. This focuses learning on the unmodified, uncertain regions while presenting them in novel contexts. We demonstrate substantial mIoU gains on Cityscapes, UAVID, and BDD100K with the largest gains on rare and difficult classes such as buses, trains, or (from the aerial perspective) cars. Our results demonstrate that uncertainty-guided context augmentation is a highly effective lever to improve
segmentation performance on complex
datasets, with code provided at https://github.com/XITASO/Preserve-the-Hard-Regenerate-the-Rest.
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All authors Nikolai Röhrich, Julian Gleißner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber
Computer Vision
Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning
Kaitao Chen, Weiqian Zhao, Jiamin Wu et al. (+6 more)
Vision-
language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in
multimodal reasoning, yet still struggle with medical images, which typically exhibit extremely sparse visual evidence to inform clinical decision-making. We recognize that
pruning visual
tokens outside the grounding region greatly enhances medical
reasoning. However, a united RL framework for active visual
token pruning (VTP) and medical
multimodal reasoning remains unestablished. Here, we propose a dual-stream RL framework, ViToS, to fulfill
token pruning and question answering. ViToS trains one policy model with two task branches, where one focuses on grounding while the other conducts
token-sparse
reasoning after VTP. Furthermore, we solve the coupled policy learning problem by introducing the cross-feedback sequential optimization, avoiding gradient conflict and facilitating convergence of the shared policy model. Evaluated on seven medical
benchmarks, our method reduces visual
tokens to 77% of the original sequence length while achieving a 108.27% relative performance on Lingshu-7B and 104.16% relative performance on HuatuoGPT-
Vision-7B. Overall, ViToS delivers superior performance and inference speedup, establishing an efficient paradigm for medical
multimodal reasoning.
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All authors Kaitao Chen, Weiqian Zhao, Jiamin Wu, Qihao Zheng, Shangquan Sun, Chunfeng Song, Xiaosong Wang, Mu Zhou, Mianxin Liu
Computer Vision
DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers
Shun Kenney and Teppei Suzuki
The remarkable scalability of
Transformers has expanded their application to 3D computer
vision, where camera-aware positional encoding is crucial for providing spatial cues in multi-view geometry. Recent advancements have established the practice of using camera parameters – such as extrinsics or projection
matrices – as relative positional encoding into the query, key, and value vectors of the
attention mechanism. However, when scaling up the training recipe of novel view synthesis (NVS) models with the camera-based positional encoding, we observe a significant issue: model performance stagnates in the late stages of training. In this paper, we investigate the cause of the performance bottleneck when scaling up and demonstrate that storing rotation and translation given by the positional encoding in the same dimensions of the value vector causes indeterminacy in their independent identification, hindering training scalability. To address this, we propose Decoupled Pose Positional Encoding (DPPE), a novel camera-based positional encoding that explicitly decouples rotation and translation. Extensive evaluations on NVS tasks demonstrate that DPPE enables stable long-term training even in scaled-up training setup. Furthermore, it exhibits superior generalization performance in extrapolation settings, such as handling an increased number of viewpoints and zoom-in scenarios.
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Computer Vision
Localized Conformal Prediction for Image Classification with Vision-Language Models
Clément Fuchs, Tim Bary and Benoît Macq
Conformal predictions have attracted significant
attention in the field of uncertainty quantification, mainly because of their strong marginal coverage guarantees. Full conditional guarantee is not an attainable goal, a well known fact in conformal predictions literature. As a result, several approaches have tried to approximate this behavior by adapting the conformal sets of test-time samples according to their similarity to calibration examples. Although the latter has gained traction and shown impressive performances for regression problems, its application to image classification remains under-explored. We conduct an extensive benchmarking on natural image classification tasks with
vision-
language models (VLMs), using our open source implementation of a recent localized conformal prediction
algorithm. We show that straightforward usage of the cosine similarity between test-time and calibration visual features, an intuitive choice for VLMs, is not sufficient to improve over the non-local baselines. In response, we propose a simple non-linear transformation of the cosine similarities, which conserves marginal coverage guarantees and achieves statistically significant mean set sizes reduction. Code is available at https://github.com/cfuchs2023/lcp-vlm/.
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Computer Vision
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
Zikang Yan, Xiao Wang, Qingquan Yang et al. (+6 more)
Accurate modeling of the divertor temperature field is essential for preventing material melting and damage and for extending the service life of fusion devices. However, conventional numerical methods, such as the Finite Element Method (FEM), are computationally expensive and therefore unsuitable for real-time applications. Therefore, a fast and generalizable method is required for real-time reconstruction of the divertor temperature field and subsequent real-time control. To address the above issue, we propose a Physics-aware Neural Operator
Transformer (PNOT) to characterize the spatiotemporal
evolution of the divertor temperature field. It models boundary heat-flux relations as a structured graph and employs graph
attention to explicitly capture spatial physical dependencies. Inspired by physics-aware
attention, we further develop a physics-aware neural operator module to aggregate query points with similar physical conditions via slicing and model heat
diffusion, while a gradient-constrained Sobolev regularization loss enforces consistency between function values and their derivatives. Experimental results show that these physical constraints improve prediction accuracy while preserving physical consistency. The source code of this paper will be released on https://github.com/Event-AHU/OpenFusion
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All authors Zikang Yan, Xiao Wang, Qingquan Yang, Zhendong Yang, Gaoting Chen, Zehua Chen, Bo Jiang, Jin Tang, Guosheng Xu
Computer Vision
Mitigating Positional Leakage in 3D Masked Autoencoders for Robust Representation Learning
Xu Yan, Huiqun Wang, Chen Wang et al. (+2 more)
Masked autoencoding has emerged as a prominent paradigm for self-supervised learning on 3D point clouds, achieving competitive performance across downstream tasks. Unlike its 2D counterpart, 3D masked autoencoding directly reconstructs spatial coordinates, making it inherently susceptible to positional leakage. In this work, we identify that the decoder in existing 3D MAE frameworks tends to over-rely on positional information, which weakens semantic representation learning and leads to suboptimal feature quality. To address this issue, we propose MPL-MAE, a masked point learning framework that mitigates positional over-reliance while enhancing the utilization of encoder features. Specifically, we introduce a recalibrated positional
embedding module that suppresses metric-dominant coordinate signals while preserving
geometric topology, together with a gated positional interface module that dynamically regulates positional injection during reconstruction. These designs promote a more balanced interaction between spatial priors and semantic features, yielding robust and informative representations. Extensive experiments across downstream tasks demonstrate that MPL-MAE consistently achieves competitive performance, validating its effectiveness. Code is available at https://github.com/yanx57/MPL-MAE.
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All authors Xu Yan, Huiqun Wang, Chen Wang, Lei Ren, Di Huang
Computer Vision
AugSplat: Radiance Field-Informed Gaussian Splatting for Sparse-View Settings
Lorenzo Lazzaroni, Riccardo Bollati, Daniel Barath et al. (+2 more)
Generating high-quality novel views at real-time frame rates remains a central challenge in 3D
vision, particularly in sparse-view scenarios. Neural radiance fields have demonstrated robust reconstruction from limited observations, but their reliance on volumetric rendering leads to high computational cost and slow inference. In contrast, Gaussian Splatting methods achieve real-time rendering through rasterization, but their optimization is highly sensitive to the quality of the initial geometry. This sensitivity becomes especially problematic in sparse-view settings, where limited observations often lead to incomplete or noisy point-cloud reconstructions. In this work, we present AugSplat, a simple framework for improving Gaussian Splatting in sparse-view regimes using radiance-field-based view augmentation. We first train a radiance field on the sparse input views and use it to synthesize additional images from nearby novel viewpoints, increasing the effective view-space coverage available for supervision. These synthetic views are then used as auxiliary supervision during Gaussian Splatting optimization. We study two variants: Staged AugSplat, which uses synthetic views for an initial optimization phase before switching to real images, and Dual AugSplat, which jointly trains on real and synthetic views with a decaying synthetic loss weight. Experiments on sparse-view mip-NeRF 360 scenes show that AugSplat improves reconstruction quality over standard Gaussian Splatting. Staged AugSplat achieves the strongest average performance, while Dual AugSplat provides a closely performing formulation that keeps real-image supervision active throughout training, and both variants preserve real-time rendering at inference.
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All authors Lorenzo Lazzaroni, Riccardo Bollati, Daniel Barath, Michael Niemeyer, Keisuke Tateno
Computer Vision
DataEvolver: Self-Evolving Multi-Agent Data Construction for Text-Rich Image Generation
Siyu Yan, Yizhen Gao, Yilin Wang et al. (+2 more)
Text-rich image generation is one of the most challenging settings in image generation, since models must simultaneously produce visually realistic images and render legible, semantically aligned, and layout-consistent text. Existing data pipelines usually follow a static crawl-filter-freeze paradigm. They collect candidate samples, filter them once, and freeze the accepted data for training. However, rejected samples are usually discarded, although they often contain useful failure signals such as OCR errors and semantic mismatches. As a result, later construction rounds may repeat the same failure modes. To address these limitations, we propose DataEvolver, a self-evolving multi-
agent framework for text-rich image data construction. DataEvolver treats data construction as feedback-driven construction policy
evolution. A Retriever collects candidate samples, a Verifier assigns quality scores and rejection causes, a Critic summarizes round-level feedback into semantic feedback, and a Generator completes under-covered regions through targeted synthesis. The updated feedback
memory then guides the next construction round. Experiments on text-rich image generation
benchmarks show that DataEvolver produces more useful training data than fixed-
dataset baselines under matched data budgets. At the 0.75M scale on PixArt-alpha, DataEvolver improves OCR-F1 over the strongest baseline by 85.3 percent on TextScenesHQ and 35.3 percent on LongTextBench. The improvements are consistent across both evaluated
benchmarks and also transfer to Show-o2, indicating that the benefit of DataEvolver is not tied to a single downstream generator. These results suggest that rejected samples can provide actionable feedback for improving text-rich image data construction.
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All authors Siyu Yan, Yizhen Gao, Yilin Wang, Dongxing Mao, Alex Jinpeng Wang
Computer Vision
MV-GEL: Language-Driven Multi-View Geometric Entity Localization on Meshes
Kartik Bali and Roland Aydin
Identifying and grounding precise
geometric entities, such as edges, planar regions, and curved surfaces within 3D objects, is foundational to computer-aided design (CAD), robotic manipulation, and scientific simulation. Although modern
Vision Language Models (VLMs) have advanced referring
segmentation (RIS) in the image domain, extending such
language-driven localization to structured 3D geometry is substantially harder. The 3D object appearance is highly sensitive to viewpoints; a single perspective may render a target entity clearly observable, while another may suffer from severe occlusion or foreshortening. In this work, we attempt to solve these challenges with MV-GEL (Multi-View
Geometric Entity Localization), a framework for localizing fine-grained
geometric entities on polygon meshes from natural
language queries. Our key insight is that reliable CAD entity (i.e., faces, edges or solids) localization depends on selecting views that make the queried entity maximally interpretable. We introduce GELviews, a
prompt-conditioned ranking module that prioritizes viewpoints based on
language prompted observability of
geometric CAD entities. Selected views are processed by a VLM-based
reasoning segmentation backbone, and predicted masks are lifted to the corresponding meshes via geometry-aware ray casting. Our framework is completely CAD agnostic and relies only on 3D meshes. Experiments show up to a 1.7X improvement in face-level IoU and over 4.5X gains in edge-level F1 compared to vanilla baselines, substantially outperforming CLIP-based and random view sampling, particularly for thin and view-sensitive structures. The
dataset, code and trained checkpoints are available at https://github.com/kbali1297/MV-GEL.
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Computer Vision · Cross-listed from eess.IV
Distortion-Corrected Diffusion MRI Using Rotated-View EPI and Joint Field-Map/Image Estimation with Gaussian Primitives
Wenqi Huang, Zhitao Li, Nan Wang et al. (+8 more)
Echo Planar Imaging (EPI) is the standard acquisition technique for
diffusion and functional neuroimaging, enabling rapid imaging but suffering from
geometric distortions caused by B0 field inhomogeneities. Existing correction methods first reconstruct distorted images using parallel imaging, then estimate the B0 field and correct the distortion in the image domain. In this sequential process, reconstruction artifacts at high acceleration factors and low SNR at high
diffusion b-values degrade B0 estimation and limit the overall correction quality. We propose a physics-informed framework that jointly estimates the B0 field and distortion-free image directly from k-space data, without depending on an intermediate parallel-imaging reconstruction for the correction. The image and the B0 field are each represented as a superposition of Gaussian primitives embedded within an MRI physics forward model. The explicit, continuous parameterization captures both smooth regions and tissue boundaries and supports rotated-view EPI acquisitions without interpolation. The
diffusion-weighted image is modeled as real and non-negative, with the image phase absorbed into a per-shot phase factor. Rotated views distribute distortions across multiple phase-encoding orientations, improving point spread function isotropy and providing stronger constraints for B0 estimation. On in vivo
brain diffusion EPI, the proposed method attains the closest
brain-boundary agreement with a distortion-free structural reference, with the largest improvement over sequential methods at high b-value and high acceleration. Extensive visual comparisons further show improved detail fidelity and noise suppression.
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All authors Wenqi Huang, Zhitao Li, Nan Wang, Yimeng Lin, Mengze Gao, Yurui Qian, Sevgi Gokce Kafali, Xiaozhi Cao, Kawin Setsompop, Daniel Rueckert, Congyu Liao
Computer Vision · Cross-listed from cs.IR
Unsupervised Data-Efficient Cross-Modal Retrieval with Global-Neighborhood Alignment Hashing
Runhao Li, Xiaoxu Ma, Zhenyu Weng et al. (+5 more)
Compared to supervised cross-modal hashing (CMH), unsupervised CMH reduces the reliance on manual labeling by learning binary codes from unlabeled image-text pairs. However, existing unsupervised CMH methods often rely on large-scale image-text pairs, which are costly to collect. To address this limitation, we propose Global-Neighborhood
Alignment Hashing (GNAH), a novel approach that preserves the semantic structure of
vision-
language foundation models within a compact binary Hamming space using only a limited number of image-text pairs. Specifically, GNAH captures global structural information from the continuous latent space and transfers it into the binary Hamming space through a Prototype-Anchored Global
Alignment module. In addition, GNAH extends conventional pairwise contrastive learning by modeling
stochastic neighborhood relationships via a Contrastive
Stochastic Neighborhood
Alignment module, thereby alleviating overfitting to sparse pairwise correlations. Extensive experiments demonstrate that GNAH consistently outperforms existing unsupervised cross-modal
retrieval methods under data-constrained settings, offering a practical solution for real-world CMH applications.
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All authors Runhao Li, Xiaoxu Ma, Zhenyu Weng, Yue Zhang, Guibo Luo, Huiping Zhuang, Zhiping Lin, Yap-Peng Tan
Computer Vision
PRISM: Latent Composition Consistency for Single-Image Reflection Removal
Junseong Shin and Tae Hyun Kim
Single-image reflection removal (SIRR) seeks to recover the transmission layer from a mixture corrupted by reflections – a severely ill-posed problem. Existing methods operate in pixel space, where the nonlinear sRGB formation model entangles the two layers and limits generalization. We observe that pretrained VAE latent spaces exhibit substantially lower coherence between image layers compared to pixel space, providing a more favorable working space for decomposition. Building on this finding, we propose PRISM (Pretrained-latent Reflection Image Separation Model), which reinterprets SIRR as a latent linear separation problem. Under an approximate additive formulation in latent space, PRISM learns a flow matching velocity field on a pretrained FLUX backbone that recovers both transmission and reflection in a single forward pass. To enforce robust disentanglement, we introduce a Latent Composition Consistency (LCC) strategy that constructs synthetic mixtures by swapping reflection latents across samples and enforces consistent decomposition via a cycle loss. We further propose a Layer Contrastive Separation (LCS) loss that promotes semantic separation between layers through patch-level contrastive learning, without requiring explicit reflection targets. Experiments on six
benchmarks demonstrate that PRISM consistently outperforms state-of-the-art methods by significant margins, with strong generalization to in-the-wild images.
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Computer Vision
SimpleSearch-VL: A Simple Recipe for Multimodal Agentic Deep Search
Ming Dai, Zhihong Lu, Jinjie Gu et al. (+5 more)
We present SimpleSearch-VL, an efficient, reliable, and practical framework for
multimodal agentic search. Its core idea is to improve the
agent's own search-and-verification process rather than scaling data, tools, or auxiliary model components. For efficiency, Factorized Adaptive Rollout (FAR) improves sampling efficiency by forming more informative training groups while using redundant samples to mitigate long-tail latency and expose hard samples. For reliability, SimpleSearch-VL performs evidence-verified
reasoning, explicitly using chain-of-thought verification to assess the relevance of retrieved visual and textual cues to the original context. For practicality, SimpleSearch-VL keeps a lightweight tool interface and performs webpage self-summary within the
agent, requiring no additional external dependencies. With only 5K supervised tool-interleaved trajectories and 2K RL data, SimpleSearch-VL improves Qwen3-VL agentic baselines by 15.8 and 16.0 average points for the 8B and 30B-A3B variants, respectively. The SimpleSearch-VL-30B-A3B model further achieves performance competitive with agentic Gemini-3-Pro.
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All authors Ming Dai, Zhihong Lu, Jinjie Gu, Jiedong Zhuang, Yefeng Liu, Wankou Yang, Jian Wang, Chunhua Shen
Computer Vision
Fully Automated High-Precision Segmentation of Retinal Atrophy and Ellipsoid Zone Thickness in OCT: A Reliable Tool for Real-World GA Monitoring
Wolf-Dieter Vogl, Hlynur Skulason, Oliver Leingang et al. (+3 more)
Geographic atrophy (GA) secondary to age-related macular degeneration (AMD) requires precise monitoring of relevant structural biomarkers to assess disease stage, progression, and treatment response. This paper presents a fully automated, deep learning-based framework for the high-precision, pixel-wise
segmentation of key biomarkers in optical coherence
tomography (OCT) imaging: retinal pigment epithelium (RPE) loss, ellipsoid zone (EZ) loss, and EZ thinning. The proposed pipeline uses three specialized semantic
segmentation models to delineate RPE loss, EZ boundaries (including interruptions), and Bruch's membrane. To ensure robustness and generalizability, the models were developed on a diverse
dataset of 298 SD-OCT volumes representing the full phenotypic spectrum of AMD (GA:222, intermediate AMD: 40, neovascular AMD: 17, healthy: 19) and validated on an independent external
dataset (n=43). The comprehensive evaluation was further strengthened using additional
datasets to assess repeatability, inter-reader reliability, the impact of B-scan density on measurement accuracy, and subgroup performance stratified by lesion size. Results demonstrated high
segmentation accuracy (Dice RPE loss: 0.88, Dice EZ loss: 0.87, Pearson's r > 0.99). Total EZ thickness measurements exhibited a sub-pixel average deviation of 2.15 $\mu m$, and
segmentation reliability was confirmed by a strong reproducibility score (ICC > 0.98). By accurately and consistently quantifying outer photoreceptor degeneration and RPE loss, this fully automated framework provides a highly reliable tool for GA assessment in both clinical trials and routine real-world ophthalmic care.
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All authors Wolf-Dieter Vogl, Hlynur Skulason, Oliver Leingang, Ursula Schmidt-Erfurth, Amir Sadeghipour, Ariadne Whitby
Computer Vision
HVPNet: A Bio-Inspired Network for General Salient and Camouflaged Object Detection
Jiawei Xu, Qiangqiang Zhou, Zhouping Li et al. (+3 more)
In recent years, most research on
multimodal salient object detection (SOD) and camouflaged object detection (COD) typically aims to improve performance through complex cross-modal feature fusion and decoding structures. However, this approach leads to an excessively large model parameter scale and often fails to deliver satisfactory detection performance due to structural redundancy. In contrast, the human visual process is able to efficiently perform salient and camouflaged object identification without such complex structures. This contrast raises an important question: Can we draw conceptual inspiration from the human visual process to achieve a simpler modeling strategy, and still realize accurate and efficient object detection? To answer this question, we propose HVPNet, a simple yet general bio-inspired computational architecture. Drawing on the multi-layered information integration of the retina as a conceptual metaphor, we designed a Retinal Integration Module (RIM), which effectively integrates
multimodal features through a level-specific multi-stage integration strategy. To fully exploit these features, we further design a cortical decoder (CD) that breaks down the decoding process into low- and high-level visual stages, abstracting the hierarchical processing in the human visual cortex. Benefiting from these designs, HVPNet can readily extend to seven tasks across four modalities. Without bells and whistles, it establishes an excellent accuracy-efficiency trade-off across 22
datasets spanning these seven tasks. Our code is available at https://github.com/jiaweiXu1029/HVPNet.
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All authors Jiawei Xu, Qiangqiang Zhou, Zhouping Li, Yanjiao Shi, Yugen Yi, Jiacong Yu
Computer Vision
DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation
Chi Huang, Wenhao Zhang, Hang Yin et al. (+5 more)
Dense depth estimation for autonomous driving faces a geometry-scale conflict: depth foundation models deliver pixel-aligned dense visual geometry without reliable metric scale, while projected
LiDAR provides metric anchors that are sparse, noisy, and misaligned with image structures. Existing sparse-prompted methods incorporate
LiDAR by regenerating depth from scratch, overriding the foundation model's coherent geometry and producing structural artifacts on visually continuous surfaces. Our key insight is that foundation models already capture geometrically coherent relative depth; no additional surface structure learning is required-only a per-pixel scale factor mapping relative geometry to metric coordinates. Based on this, we propose DrivingDepth, which treats sparse
LiDAR as
geometric prompts that locally calibrate a frozen foundation prior through residual pixel-wise scale correction, preserving dense visual geometry by construction. On nuScenes with 4-frame surround-view input, DrivingDepth achieves an AbsRel of 11.19 and an EdgeCR of 5.741, outperforming MapAnything (11.99/1.914) by simultaneously delivering SOTA metric accuracy and
geometric consistency.
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All authors Chi Huang, Wenhao Zhang, Hang Yin, YuAn Wang, Hao Li, Bosheng Wang, Xun Sun, Liang Wang
Computer Vision · Cross-listed from cs.AI
One Reflection Is Not Enough: Self-Correcting Autonomous Research via Multi-Hypothesis Failure Attribution
Jie Ma, Binfei Chu, Jie Gao et al. (+6 more)
Autonomous research
agents can now draft hypotheses, write code, run experiments, and produce papers, but they remain brittle when experiments fail. Under the prevailing paradigm, failure recovery is usually delegated to a single free-form reflection: a rich trajectory of metrics, logs, and design choices is compressed into one verbal critique, which often leads either to localized trial-and-error or to hard pivots that discard useful context. We propose SAGE, a Self-correcting, Autonomous, Grounded Experimenter, to tackle this failure-recovery bottleneck. Its core mechanism, Multi-Hypothesis Failure Attribution (MHFA), treats recovery as a structured causal diagnosis. By analyzing dynamic trajectory features, MHFA systematically generates multiple evidence-grounded explanations for a failure, independently evaluates their severity, and deterministically routes the verified root cause to the correct intervention level (hypothesis, experimental design, or implementation). To guarantee scientific honesty, SAGE further employs a grounded reporting mechanism that explicitly constrains drafted results to actual measured values, redacting hallucinated numbers. On a 12-topic, 5-domain
benchmark, SAGE increases metrics-bearing outputs from 42% to 92% over a reflection baseline, improves artifact quality from 5.00 to 6.75/10, and blindly outscores AI-Scientist-v2 (52.0 vs. 48.2), with gains concentrated in code development and execution. While fully autonomous scientific writing and generating conference-ready papers remain notoriously difficult open problems for the entire field, SAGE successfully produces significantly more reliable and higher-quality scientific artifacts. Ultimately, by coupling structured recovery with explicit grounding constraints, SAGE significantly outperforms monolithic reflection paradigms, establishing a highly trustworthy foundation for future autonomous research.
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All authors Jie Ma, Binfei Chu, Jie Gao, Jinlu Zhang, Yiwei Ma, Yi Tan, Jiayi Ji, Xiaoshuai Sun, Rongrong Ji