Computer Vision · Cross-listed from eess.IV
Visible Singularities Guided Correlation Network for Limited-Angle CT Reconstruction
Yiyang Wen, Liu Shi, Zekun Zhou et al. (+2 more)
Limited-angle computed
tomography (LACT) offers the advantages of reduced radiation dose and shortened scanning time. Traditional reconstruction
algorithms exhibit various inherent limitations in LACT. Currently, most deep learning-based LACT reconstruction methods focus on multi-domain fusion or the introduction of generic priors, failing to fully align with the core imaging characteristics of LACT-such as the directionality of artifacts and directional loss of structural information, which are caused by the absence of projection angles in certain directions. Inspired by the theory of visible and invisible singularities, taking into account the aforementioned core imaging characteristics of LACT, we propose a Visible Singularities Guided Correlation network for LACT reconstruction (VSGC). The design philosophy of VSGC consists of two core steps: First, extract VS edge features from LACT images and focus the model's
attention on these VS. Second, establish correlations between the VS edge features and other regions of the image. Additionally, a multi-scale loss function with anisotropic constraint is employed to constrain the model to converge in multiple aspects. Finally, qualitative and quantitative validations are conducted on both simulated and real
datasets to verify the effectiveness and feasibility of the proposed design. Particularly, in comparison with alternative methods, VSGC delivers more prominent performance in small angular ranges, with the PSNR improvement of 2.45 dB and the SSIM enhancement of 1.5%. The code is publicly available at https://github.com/yqx7150/VSGC.
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All authors Yiyang Wen, Liu Shi, Zekun Zhou, WenZhe Shan, Qiegen Liu
Computer Vision · Cross-listed from cs.CR
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance
Miao Lin, Feng Yu, Rui Ning et al. (+6 more)
Deep neural networks are highly susceptible to
backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify
backdoor threats. This paper presents the first in-depth investigation of how the
dataset imbalance amplifies
backdoor vulnerability, showing that (i) the imbalance induces a majority-class bias that increases susceptibility and (ii) conventional defenses degrade significantly as the imbalance grows. To address this, we propose Randomized Probability Perturbation (RPP), a certified poisoned-sample detection framework that operates in a black-box setting using only model output probabilities. For any inspected sample, RPP determines whether the input has been
backdoor-manipulated, while offering provable within-domain detectability guarantees and a probabilistic upper bound on the false positive rate. Extensive experiments on five
benchmarks (MNIST, SVHN, CIFAR-10, TinyImageNet and ImageNet10) covering 10
backdoor attacks and 12 baseline defenses show that RPP achieves significantly higher detection accuracy than state-of-the-art defenses, particularly under
dataset imbalance. RPP establishes a theoretical and practical foundation for defending against
backdoor attacks in real-world environments with imbalanced data.
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All authors Miao Lin, Feng Yu, Rui Ning, Lusi Li, Jiawei Chen, Qian Lou, Mengxin Zheng, Chunsheng Xin, Hongyi Wu
Computer Vision
CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning
Hang Wu, Yujun Cai, Zehao Li et al. (+4 more)
Understanding camera dynamics is a fundamental pillar of
video spatial intelligence. However, existing
multimodal models predominantly treat this task as a black-box classification, often confusing physically distinct motions by relying on superficial visual patterns rather than
geometric cues. We present CamReasoner, a framework that reformulates camera movement understanding as a structured inference process to bridge the gap between perception and cinematic logic. Our approach centers on the Observation-Thinking-Answer (O-T-A) paradigm, which compels the model to articulate spatio-temporal observations and reason about motion patterns within an explicit
reasoning block. To instill this capability, we construct a Large-scale Inference Trajectory Suite comprising 18k SFT
reasoning chains and 38k RL feedback samples. To the best of our knowledge, we are the first to employ RL for logical
alignment in camera movement understanding, ensuring motion inferences are grounded in structured visual
reasoning rather than contextual guesswork. Built upon Qwen2.5-VL-7B, CamReasoner-7B improves binary classification accuracy from 73.8% to 78.4% and VQA accuracy from 60.9% to 74.5% over its backbone, consistently outperforming both proprietary and open-source baselines across multiple
benchmarks.
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All authors Hang Wu, Yujun Cai, Zehao Li, Haonan Ge, Bowen Sun, Junsong Yuan, Yiwei Wang
Computer Vision
Posterior Continuation with Noise-Conditioned Frequency Exposure for Diffusion Inverse Problems
Feng Tian, Yixuan Li, Weili Zeng et al. (+3 more)
Diffusion posterior sampling solves inverse problems by combining a pretrained
diffusion prior with measurement-consistency guidance. However, full-band guidance can be unreliable at high noise levels, where clean estimates contain score-induced errors and high-frequency measurement directions are weakly identifiable. We argue that posterior guidance should expose measurement frequencies according to the instantaneous
diffusion noise level. Based on this principle, we propose a posterior continuation framework that constructs a family of intermediate posteriors whose likelihood emphasizes currently reliable frequency bands and gradually returns to full-band consistency. We instantiate this framework with a stabilized sampler that combines a
diffusion predictor, frequency-limited likelihood refinement, and a Haar-domain commitment rule that commits reliable coarse corrections while deferring weakly identifiable details. Across super-resolution, inpainting, and deblurring, our method achieves competitive-to-state-of-the-art restoration performance, including up to 5 dB PSNR improvement on motion deblurring over strong baselines in evaluations on FFHQ and ImageNet.
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All authors Feng Tian, Yixuan Li, Weili Zeng, Weitian Zhang, Yichao Yan, Xiaokang Yang
Computer Vision · Cross-listed from cs.LG
The Illusion of Forgetting: Attack Unlearned Diffusion via Initial Latent Variable Optimization
Manyi Li, Yufan Liu, Lai Jiang et al. (+3 more)
Text-to-image
diffusion models (DMs) are frequently abused to produce harmful or copyrighted content, violating public interests. Concept erasure (unlearning) is a promising paradigm to alleviate this issue. However, there exists a peculiar forgetting illusion phenomenon with unclear cause. Based on empirical analysis, we formally explain this cause: most unlearning partially disrupt the mapping between linguistic symbols and the underlying internal knowledge, leaving the knowledge intact as dormant
memories. We further demonstrate that distributional discrepancy in the denoising process serves as a measurable indicator of how much of the mapping is retained, also reflecting unlearning strength. Inspired by this, we propose IVO (Initial Latent Variable Optimization), a novel attack framework designed to assess the robustness of current unlearning methods. IVO optimizes initial latent variables to realign the noise distribution of unlearned models with that of their vanilla counterparts, which reconstructs the fractured mappings and consequently revives dormant
memories. Extensive experiments covering 11 unlearning techniques and 3 concept scenarios show that IVO outperforms state-of-the-art baselines, exposing fundamental flaws in current unlearning mechanisms. Warning: This paper has unsafe images that may offend some readers.
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All authors Manyi Li, Yufan Liu, Lai Jiang, Bing Li, Yuming Li, Weiming Hu
Computer Vision
Intra-Class Subdivision for Pixel Contrastive Learning: Application to Semi-supervised Cardiac Image Segmentation
Jiajun Zhao and Xuan Yang
We propose an intra-class subdivision pixel contrastive learning (SPCL) framework for cardiac image
segmentation to address representation contamination at boundaries. The novel concept “Unconcerned sample” is proposed to distinguish pixel representations at the inner and boundary regions within the same class, facilitating a clearer characterization of intra-class variations. A novel boundary contrastive loss for boundary representations is proposed to enhance representation discrimination across boundaries. The advantages of the unconcerned sample and boundary contrastive loss are analyzed theoretically. Experimental results in public cardiac
datasets demonstrate that SPCL significantly improves
segmentation performance, outperforming existing methods with respect to
segmentation quality and boundary precision. Our code is available at https://github.com/Jrstud203/SPCL.
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Computer Vision
VideoGPA: Distilling Geometry Priors for 3D-Consistent Video Generation
Hongyang Du, Junjie Ye, Xiaoyan Cong et al. (+7 more)
While recent
video diffusion models (VDMs) produce visually impressive results, they fundamentally struggle to maintain 3D structural consistency, often resulting in object deformation or spatial drift. We hypothesize that these failures arise because standard denoising objectives lack explicit incentives for
geometric coherence. To address this, we introduce VideoGPA (
Video Geometric Preference
Alignment), a data-efficient self-supervised framework that leverages a geometry foundation model to automatically derive dense preference signals that guide VDMs via Direct Preference Optimization (DPO). This approach effectively steers the generative distribution toward inherent 3D consistency without requiring human annotations. VideoGPA significantly enhances temporal stability,
geometric plausibility, and motion coherence using minimal preference pairs, consistently outperforming state-of-the-art baselines in extensive experiments.
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All authors Hongyang Du, Junjie Ye, Xiaoyan Cong, Runhao Li, Jingcheng Ni, Aman Agarwal, Zeqi Zhou, Zekun Li, Randall Balestriero, Yue Wang
Computer Vision
User Prompting Strategies and Prompt Enhancement Methods for Open-Set Object Detection in XR Environments
Junfeng Lin, Yanming Xiu and Maria Gorlatova
Open-set object detection (OSOD) localizes objects while identifying and rejecting unknown classes at inference. While recent OSOD models perform well on
benchmarks, their behavior under realistic user prompting remains underexplored. In interactive XR settings, user-generated
prompts are often ambiguous, underspecified, or overly detailed. To study
prompt-conditioned robustness, we evaluate two OSOD models, GroundingDINO and YOLO-E, on real-world XR images and simulate diverse user prompting behaviors using
vision-
language models. We consider four
prompt types: standard, underdetailed, overdetailed, and pragmatically ambiguous, and examine the impact of two enhancement strategies on these
prompts. Results show that both models exhibit stable performance under underdetailed and standard
prompts, while they suffer degradation under ambiguous
prompts. Overdetailed
prompts primarily affect GroundingDINO.
Prompt enhancement substantially improves robustness under ambiguity, yielding gains exceeding 55% mIoU and 41% average confidence. Based on the findings, we propose several prompting strategies and
prompt enhancement methods for OSOD models in XR environments.
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Computer Vision · Cross-listed from astro-ph.IM
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
Shuhong Liu, Xining Ge, Ziying Gu et al. (+7 more)
Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave
stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained by scarce paired training data and the requirement for physically interpretable models in scientific workflows. We propose a physics-based noise synthesis framework tailored to CCD noise formation in the
telescope. The pipeline models
photon shot noise, photo-response non-uniformity, dark-current noise, readout effects, and localized outliers arising from
cosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis, we stack multiple unregistered exposures to produce high-SNR bases. Realistic noisy counterparts synthesized from these bases using our noise model enable the construction of abundant paired
datasets for supervised learning. Extensive experiments on our real-world multi-band
dataset curated from two ground-based
telescopes demonstrate the effectiveness of our framework in both photometric and scientific accuracy.
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All authors Shuhong Liu, Xining Ge, Ziying Gu, Quanfeng Xu, Lin Gu, Ziteng Cui, Xuangeng Chu, Jun Liu, Dong Li, Tatsuya Harada
Computer Vision · Cross-listed from cs.CL
PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu, Rui Meng, Yale Song et al. (+4 more)
Despite rapid advances in autonomous AI scientists powered by
language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic illustrations. Powered by state-of-the-art VLMs and image generation models, PaperBanana orchestrates specialized
agents to retrieve references, plan content and style, render images, and iteratively refine via self-critique. To rigorously evaluate our framework, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications, covering diverse research domains and illustration styles. Comprehensive experiments demonstrate that PaperBanana consistently outperforms leading baselines in faithfulness, conciseness, readability, and aesthetics. We further show that our method effectively extends to the generation of high-quality statistical plots. Collectively, PaperBanana paves the way for the automated generation of publication-ready illustrations.
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All authors Dawei Zhu, Rui Meng, Yale Song, Xiyu Wei, Sujian Li, Tomas Pfister, Jinsung Yoon
Computer Vision
Training-Free Test-Time Adaptation with Brownian Distance Covariance in Vision-Language Models
Yi Zhang, Chun-Wun Cheng, Angelica I. Aviles-Rivero et al. (+2 more)
Vision-
language models suffer performance degradation under domain shift, limiting real-world applicability. Existing test-time adaptation methods are computationally intensive, rely on back-propagation, and often focus on single modalities. To address these issues, we propose Training-free Test-Time Adaptation with Brownian Distance Covariance (TaTa). TaTa leverages Brownian Distance Covariance-a powerful statistical measure that captures both linear and nonlinear dependencies via pairwise distances-to dynamically adapt VLMs to new domains without training or back-propagation. This not only improves efficiency but also enhances stability by avoiding disruptive weight updates. TaTa further integrates attribute-enhanced prompting to improve
vision-
language inference with descriptive visual cues. Combined with dynamic clustering and pseudo-label refinement, it effectively recalibrates the model for novel visual contexts. Experiments across diverse
datasets show that TaTa significantly reduces computational cost while achieving state-of-the-art performance in domain and cross-
dataset generalization.
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All authors Yi Zhang, Chun-Wun Cheng, Angelica I. Aviles-Rivero, Zhihai He, Liang-Jie Zhang
Computer Vision
Structure Over Scale: Learning Visual Reasoning from Pedagogical Video
Bishoy Galoaa, Xiangyu Bai and Sarah Ostadabbas
State-of-the-art
vision-
language models (VLMs) score impressively on
video benchmarks yet stumble on basic visual
reasoning tasks involving spatial relations,
navigation, and object selection that a preschooler solves easily. We hypothesize that the explicit pedagogical structure, specifically the context-question-pause-answer cycles embedded in children's educational
video, provides naturally co-aligned
reasoning traces: temporally synchronized visual cues, questions, and answers that emerge only from deliberate pedagogical authoring and cannot be practically reconstructed through manual annotation at scale. To test this, we introduce SoSVQA (Structure over Scale Visual Question Answering), a unified
benchmark of 10K question-answer pairs automatically extracted from Dora the Explorer (DoraVQA) and Mickey Mouse Clubhouse (ClubHVQA) with precise timestamp
alignment, and fine-tune Qwen2-VL and Qwen3-VL using Group Relative Policy Optimization (GRPO) to leverage the clear correctness signals and structured
reasoning traces inherent in educational content. Despite training on just 10K QA pairs from 78 hours of children's television, orders of magnitude less data than GPT and Gemini, our approach delivers generalizable performance gains for Qwen-based VLMs, yielding consistent improvements on NExT-QA (+19.7),
Video-MME (+10.6), and MotionBench (+4.9), matching the performance of leading proprietary systems and demonstrating that content structure can compensate for content scale.
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Computer Vision
ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search
Tao Yu, Haopeng Jin, Hao Wang et al. (+19 more)
In recent years, large
language models (LLMs) have made rapid progress in information
retrieval, yet existing research has mainly focused on text or static
multimodal settings. Open-domain
video shot
retrieval, which involves richer temporal structure and more complex semantics, still lacks systematic
benchmarks and analysis. To fill this gap, we introduce ShotFinder, a
benchmark that formalizes editing requirements as keyframe-oriented shot descriptions and introduces five types of controllable single-factor constraints: Temporal order, Color, Visual style, Audio, and Resolution. We curate 1,210 high-quality samples from YouTube across 20 thematic categories, using large models for generation with human verification. Based on the
benchmark, we propose ShotFinder, a text-driven three-stage
retrieval and localization pipeline: (1) query expansion via
video imagination, (2) candidate
video retrieval with a search engine, and (3) description-guided shot localization. Experiments on multiple closed-source and open-source models reveal a significant gap to human performance, with clear imbalance across constraints: temporal localization is relatively tractable, while color and visual style remain major challenges. These results reveal that open-domain
video shot
retrieval is still a critical capability that
multimodal large models have yet to overcome.
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All authors Tao Yu, Haopeng Jin, Hao Wang, Shenghua Chai, Yujia Yang, Junhao Gong, Jiaming Guo, Minghui Zhang, Xinlong Chen, Zhenghao Zhang, Yuxuan Zhou, Yufei Xiong, Shanbin Zhang, Jiabing Yang, YiFan Zhang, Hongzhu Yi, Xinming Wang, Cheng Zhong, Xiao Ma, Zhang Zhang, Yan Huang, Liang Wang
Computer Vision
Video-o3: Native Interleaved Clue Seeking for Long Video Multi-Hop Reasoning
Xiangyu Zeng, Zhiqiu Zhang, Yuhan Zhu et al. (+12 more)
Existing
multimodal large
language models for long-
video understanding predominantly rely on uniform sampling and single-turn inference, limiting their ability to identify sparse yet critical evidence amid extensive redundancy. We introduce
Video-o3, a novel framework that supports iterative discovery of salient visual clues, fine-grained inspection of key segments, and adaptive termination once sufficient evidence is acquired. Technically, we address two core challenges in interleaved tool invocation. First, to mitigate
attention dispersion induced by the heterogeneity of
reasoning and tool-calling, we propose Task-Decoupled
Attention Masking, which isolates per-step concentration while preserving shared global context. Second, to control context length growth in multi-turn interactions, we introduce a Verifiable Trajectory-Guided Reward that balances exploration coverage with
reasoning efficiency. To support training at scale, we further develop a data synthesis pipeline and construct Seeker-173K, comprising 173K high-quality tool-interaction trajectories for effective supervised and reinforcement learning. Extensive experiments show that
Video-o3 substantially outperforms state-of-the-art methods, achieving 72.1% accuracy on MLVU and 46.5% on
Video-Holmes. These results demonstrate
Video-o3's strong multi-hop evidence-seeking and
reasoning capabilities, and validate the effectiveness of native tool invocation in long-
video scenarios.
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All authors Xiangyu Zeng, Zhiqiu Zhang, Yuhan Zhu, Xinhao Li, Zikang Wang, Changlian Ma, Qingyu Zhang, Zizheng Huang, Kun Ouyang, Tianxiang Jiang, Ziang Yan, Yi Wang, Hongjie Zhang, Yali Wang, Limin Wang
Computer Vision
Region-Normalized DPO for Medical Image Segmentation under Noisy Judges
Hamza Kalisch, Constantin Seibold, Jens Kleesiek et al. (+2 more)
While dense pixel-wise annotations remain the gold standard for medical image
segmentation, they are costly to obtain and limit scalability. In contrast, many deployed systems already produce inexpensive automatic quality-control (QC) signals like model agreement, uncertainty measures, or learned mask-quality scores which can be used for further model training without additional ground-truth annotation. However, these signals can be noisy and biased, making preference-based fine-tuning susceptible to harmful updates. We study Direct Preference Optimization (DPO) for
segmentation from such noisy judges using proposals generated by a supervised base segmenter trained on a small labeled set. We find that outcomes depend strongly on how preference pairs are mined: selecting the judge's top-ranked proposal can improve peak performance when the judge is reliable, but can amplify harmful errors under weaker judges. We propose Region-Normalized DPO (RN-DPO), a
segmentation-aware objective which normalizes preference updates by the size of the disagreement region between masks, reducing the leverage of harmful comparisons and improving optimization stability. Across two medical
datasets and multiple regimes, RN-DPO improves sustained performance and stabilizes preference-based fine-tuning, outperforming standard DPO and strong baselines without requiring additional pixel annotations.
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All authors Hamza Kalisch, Constantin Seibold, Jens Kleesiek, Ken Herrmann, Frederic Jonske
Computer Vision
Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training
Anglin Liu, Ruichao Chen, Yi Lu et al. (+2 more)
Despite recent
Multimodal Large
Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit:
geometric blindness. This failure to ground outputs in objective
geometric constraints leads to plausible yet factually incorrect
hallucinations, rooted in training paradigms that prioritize linguistic fluency over
geometric fidelity. This paper introduces Med-Scout, a novel framework that "cures" this blindness via Reinforcement Learning (RL) that leverages the intrinsic
geometric logic latent within unlabeled medical images. Instead of relying on costly expert annotations, Med-Scout derives verifiable supervision signals through three strategic proxy tasks inspired by the systematic reading and
reasoning patterns of clinicians: Hierarchical Scale Localization, Topological Jigsaw Reconstruction, and Anomaly Consistency Detection. To rigorously quantify this deficit, we present Med-Scout-Bench, a new
benchmark specifically designed to evaluate
geometric perception. Extensive evaluations show that Med-Scout significantly mitigates
geometric blindness, outperforming leading proprietary and open-source MLLMs by over 40% on our
benchmark. Furthermore, this enhanced
geometric perception generalizes to broader medical understanding, achieving superior results on radiological and comprehensive medical VQA tasks.
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All authors Anglin Liu, Ruichao Chen, Yi Lu, Hongxia Xu, Jintai Chen
Computer Vision · Cross-listed from eess.IV
Scale-Cascaded Diffusion Models for Super-Resolution in Medical Imaging
Darshan Thaker, Mahmoud Mostapha, Radu Miron et al. (+2 more)
Diffusion models have been increasingly used as strong generative priors for solving inverse problems such as super-resolution in medical imaging. However, these approaches typically utilize a
diffusion prior trained at a single scale, ignoring the hierarchical scale structure of image data. In this work, we propose to decompose images into
Laplacian pyramid scales and train separate
diffusion priors for each frequency band. We then develop an
algorithm to perform super-resolution that utilizes these priors to progressively refine reconstructions across different scales. Evaluated on
brain, knee, and prostate MRI data, our approach both improves perceptual quality over baselines and reduces inference time through smaller coarse-scale networks. Our framework unifies multiscale reconstruction and
diffusion priors for medical image super-resolution.
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All authors Darshan Thaker, Mahmoud Mostapha, Radu Miron, Shihan Qiu, Mariappan Nadar
Computer Vision
Hi-Light: A Path to high-fidelity, high-resolution video relighting with a Novel Evaluation Paradigm
Xiangrui Liu, Haoxiang Li and Yezhou Yang
Video relighting offers immense creative potential and commercial value but is hindered by challenges, including the absence of an adequate evaluation metric, severe light flickering, and the degradation of fine-grained details during editing. To overcome these challenges, we introduce Hi-Light, a novel, training-free framework for high-fidelity, high-resolution, robust
video relighting. Our approach introduces three technical innovations: lightness prior anchored guided relighting
diffusion that stabilises intermediate relit
video, a Hybrid Motion-Adaptive Lighting Smoothing Filter that leverages optical flow to ensure temporal stability without introducing motion blur, and a LAB-based Detail Fusion module that preserves high-frequency detail information from the original
video. Furthermore, to address the critical gap in evaluation, we propose the Light Stability Score, the first quantitative metric designed to specifically measure lighting consistency. Extensive experiments demonstrate that Hi-Light significantly outperforms state-of-the-art methods in both qualitative and quantitative comparisons, producing stable, highly detailed relit
videos.
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Computer Vision
Segment Any Events with Language
Seungjun Lee and Gim Hee Lee
Scene understanding with free-form
language has been widely explored within diverse modalities such as images, point clouds, and
LiDAR. However, related studies on event sensors are scarce or narrowly centered on semantic-level understanding. We introduce SEAL, the first Semantic-aware Segment Any Events framework that addresses Open-Vocabulary Event Instance
Segmentation (OV-EIS). Given the visual
prompt, our model presents a unified framework to support both event
segmentation and open-vocabulary mask classification at multiple levels of granularity, including instance-level and part-level. To enable thorough evaluation on OV-EIS, we curate four
benchmarks that cover label granularity from coarse to fine class configurations and semantic granularity from instance-level to part-level understanding. Extensive experiments show that our SEAL largely outperforms proposed baselines in terms of performance and inference speed with a parameter-efficient architecture. In the Appendix, we further present a simple variant of our SEAL achieving generic spatiotemporal OV-EIS that does not require any visual
prompts from users in the inference. Check out our project page in https://0nandon.github.io/SEAL
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Computer Vision
FlowCalib: LiDAR-to-Vehicle Miscalibration Detection using Scene Flows
Ilir Tahiraj, Peter Wittal and Markus Lienkamp
Accurate sensor-to-vehicle calibration is essential for safe autonomous driving. Angular misalignments of
LiDAR sensors can lead to safety-critical issues during autonomous operation. However, current methods primarily focus on correcting sensor-to-sensor errors without considering the miscalibration of individual sensors that cause these errors in the first place. We introduce FlowCalib, the first framework that detects
LiDAR-to-vehicle miscalibration using motion cues from the scene flow of static objects. Our approach leverages the systematic bias induced by rotational misalignment in the flow field generated from sequential 3D point clouds, eliminating the need for additional sensors. The architecture integrates a neural scene flow prior for flow estimation and incorporates a dual-branch detection network that fuses learned global flow features with handcrafted
geometric descriptors. These combined representations allow the system to perform two complementary binary classification tasks: a global binary decision indicating whether misalignment is present and separate, axis-specific binary decisions indicating whether each rotational axis is misaligned. Experiments on the nuScenes
dataset demonstrate FlowCalib's ability to robustly detect miscalibration, establishing a
benchmark for sensor-to-vehicle miscalibration detection.
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Computer Vision · Cross-listed from eess.IV
Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation
Ping Chen, Zicheng Huang, Xiangming Wang et al. (+4 more)
We propose VL-DUN, a principled framework for joint All-in-One Medical Image Restoration and
Segmentation (AiOMIRS) that bridges the gap between low-level signal recovery and high-level semantic understanding. While standard pipelines treat these tasks in isolation, our core insight is that they are fundamentally synergistic: restoration provides clean anatomical structures to improve
segmentation, while semantic priors regularize the restoration process. VL-DUN resolves the sub-optimality of sequential processing through two primary innovations. (1) We formulate AiOMIRS as a unified optimization problem, deriving an interpretable joint unfolding mechanism where restoration and
segmentation are mathematically coupled for mutual refinement. (2) We introduce a frequency-aware Mamba mechanism to capture long-range dependencies for global
segmentation while preserving the high-frequency textures necessary for restoration. This allows for efficient global context modeling with linear complexity, effectively mitigating the spectral bias of standard architectures. As a pioneering work in the AiOMIRS task, VL-DUN establishes a new state-of-the-art across multi-modal
benchmarks, improving PSNR by 0.92 dB and the Dice coefficient by 9.76%. Our results demonstrate that joint collaborative learning offers a superior, more robust solution for complex clinical workflows compared to isolated task processing. The codes are provided in https://github.com/cipi666/VLDUN.
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All authors Ping Chen, Zicheng Huang, Xiangming Wang, Yungeng Liu, Bingyu Liang, Haijin Zeng, Yongyong Chen
Computer Vision
Rethinking Transferable Adversarial Attacks on Point Clouds from a Compact Subspace Perspective
Keke Tang, Xianheng Liu, Weilong Peng et al. (+5 more)
Transferable
adversarial attacks on point clouds remain challenging, as existing methods often rely on model-specific gradients or heuristics that limit generalization to unseen architectures. In this paper, we rethink
adversarial transferability from a compact subspace perspective and propose CoSA, a transferable attack framework that operates within a shared low-dimensional semantic space. Specifically, each point cloud is represented as a compact combination of class-specific prototypes that capture shared semantic structure, while
adversarial perturbations are optimized within a low-rank subspace to induce coherent and architecture-agnostic variations. This design suppresses model-dependent noise and constrains perturbations to semantically meaningful directions, thereby improving cross-model transferability without relying on surrogate-specific artifacts. Extensive experiments on multiple
datasets and network architectures demonstrate that CoSA consistently outperforms state-of-the-art transferable attacks, while maintaining competitive imperceptibility and robustness under common defense strategies. Codes will be made public upon paper acceptance.
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All authors Keke Tang, Xianheng Liu, Weilong Peng, Xiaofei Wang, Daizong Liu, Peican Zhu, Can Lu, Zhihong Tian
Computer Vision · Cross-listed from cs.GR
EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing
Xijie Yang, Mulin Yu, Changjian Jiang et al. (+6 more)
Recent radiance-field-based reconstruction methods, such as NeRF and 3DGS, achieve high visual fidelity for indoor scenes, but often break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, inverse path tracing methods based on mesh representations enforce correct light transport but require highly accurate geometry, making them difficult to apply robustly to real indoor scenes. We present Emission-Aware Gaussians and Path Tracing (EAG-PT), a method for physically based reconstruction and rendering of indoor scenes using a unified 2D Gaussian representation, targeting editable diffuse global illumination. Our approach consists of three key ideas: (1) representing indoor scenes with 2D Gaussians as a transport-friendly
geometric proxy that avoids explicit mesh reconstruction; (2) explicitly separating emissive and non-emissive components during reconstruction to support editing; and (3) decoupling reconstruction from final rendering by using efficient single-bounce optimization and high-quality multi-bounce path tracing, respectively. Experiments on synthetic and real indoor scenes show that EAG-PT produces more natural and physically consistent edited renderings than radiance-field reconstructions, while preserving finer
geometric detail and avoiding mesh-induced artifacts compared with mesh-based inverse path tracing. These results highlight the potential of our approach for applications such as interior design, XR content creation, and
embodied AI.
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All authors Xijie Yang, Mulin Yu, Changjian Jiang, Kerui Ren, Tao Lu, Jiangmiao Pang, Dahua Lin, Bo Dai, Linning Xu
Computer Vision
HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation
Hari Krishna Gadi, Daniel Matos, Hongyi Luo et al. (+4 more)
Visual geolocalization, the task of predicting where an image was taken, remains challenging due to global scale, visual ambiguity, and the inherently hierarchical structure of geography. Existing paradigms rely on either large-scale
retrieval, which requires storing a large number of image
embeddings, grid-based classifiers that ignore geographic continuity, or generative models that diffuse over space but struggle with fine detail. We introduce an entity-centric formulation of geolocation that replaces image-to-image
retrieval with a compact hierarchy of geographic entities embedded in
Hyperbolic space. Images are aligned directly to country, region, subregion, and city entities through Geo-Weighted
Hyperbolic contrastive learning by directly incorporating haversine distance into the contrastive objective. This hierarchical design enables interpretable predictions and efficient inference with 240k entity
embeddings instead of over 5 million image
embeddings on the OSV5M
benchmark, on which our method establishes a new state-of-the-art performance. Compared to the current methods in the literature, it reduces mean geodesic error by 19.5%, while improving the fine-grained subregion accuracy by 43%. These results demonstrate that geometry-aware hierarchical
embeddings provide a scalable and conceptually new alternative for global image geolocation.
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All authors Hari Krishna Gadi, Daniel Matos, Hongyi Luo, Lu Liu, Yongliang Wang, Yanfeng Zhang, Liqiu Meng
Computer Vision
One-shot Optimized Steering Vector for Hallucination Mitigation for VLMs
Youxu Shi, Suorong Yang and Dong Liu
Vision Language Models (VLMs) achieve strong performance on
multimodal tasks but still suffer from
hallucination and safety-related failures that persist even at scale. Steering offers a lightweight technique to improve model performance. However, steering, whether input-dependent or input-independent, achieves a meaningful trade-off between efficiency and effectiveness. In this work, we observe that steering vectors can generalize across inputs when tasks share aligned semantic intent. Based on this insight, we propose OSGA (One-shot Steering with Generative Anchor), an input-independent framework that improves model performance with a single optimization instance. OSGA first selects an informative sample via a variance-based data selection strategy and learns a single steering vector with a contrastive objective with generative anchor regularization. The resulting vector can be universally applied at a certain layer during inference time without modifying model parameters. Experiments across multiple
benchmarks show that a single OSGA-optimized steering vector consistently improves
hallucination mitigation and safety enhancement with negligible overhead, highlighting one-shot steering as a practical and scalable solution for reliable VLMs.
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Computer Vision · Cross-listed from eess.IV
Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imaging
Brayan Monroy and Jorge Bacca
Modulo imaging enables high dynamic range (HDR) acquisition by cyclically wrapping saturated intensities, but accurate reconstruction remains challenging due to ambiguities between natural image edges and artificial wrap discontinuities. This work proposes a learning-based HDR restoration framework that incorporates two key strategies: (i) a scale-equivariant regularization that enforces consistency under exposure variations, and (ii) a feature lifting input design combining the raw modulo image, wrapped finite differences, and a closed-form initialization. Together, these components enhance the network's ability to distinguish true structure from wrapping artifacts, yielding state-of-the-art performance across perceptual and linear HDR quality metrics.
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Computer Vision
Leveraging Multi-Rater Annotations to Calibrate Object Detectors in Microscopy Imaging
Francesco Campi, Lucrezia Tondo, Ekin Karabati et al. (+2 more)
Deep learning-based object detectors have achieved impressive performance in microscopy imaging, yet their confidence estimates often lack calibration, limiting their reliability for biomedical applications. In this work, we introduce a new approach to improve model calibration by leveraging multi-rater annotations. We propose to train separate models on the annotations from single experts and aggregate their predictions to emulate consensus. This improves upon label sampling strategies, where models are trained on mixed annotations, and offers a more principled way to capture inter-rater variability. Experiments on a colorectal organoid
dataset annotated by two experts demonstrate that our rater-specific ensemble strategy improves calibration performance while maintaining comparable detection accuracy. These findings suggest that explicitly modelling rater disagreement can lead to more trustworthy object detectors in biomedical imaging.
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All authors Francesco Campi, Lucrezia Tondo, Ekin Karabati, Johannes Betge, Marie Piraud
Computer Vision
Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI
Yinsong Wang, Thomas Fletcher, Xinzhe Luo et al. (+3 more)
Reconstructing 3D fetal MR volumes from motion-corrupted stacks of 2D slices is a crucial and challenging task. Conventional slice-to-volume reconstruction (SVR) methods are time-consuming and require multiple orthogonal stacks for reconstruction. While learning-based SVR approaches have significantly reduced the time required at the inference stage, they heavily rely on ground truth information for training, which is inaccessible in practice. To address these challenges, we propose GaussianSVR, a self-supervised framework for slice-to-volume reconstruction. GaussianSVR represents the target volume using 3D Gaussian representations to achieve high-fidelity reconstruction. It leverages a simulated forward slice acquisition model to enable self-supervised training, alleviating the need for ground-truth volumes. Furthermore, to enhance both accuracy and efficiency, we introduce a multi-resolution training strategy that jointly optimizes Gaussian parameters and spatial transformations across different resolution levels. Experiments show that GaussianSVR outperforms the baseline methods on fetal MR volumetric reconstruction. Code is available at https://github.com/Yinsong0510/GaussianSVR-Self-Supervised-Slice-to-Volume-Reconstruction-with-Gaussian-Representations.
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All authors Yinsong Wang, Thomas Fletcher, Xinzhe Luo, Aine Travers Dineen, Rhodri Cusack, Chen Qin
Computer Vision
About an Automating Annotation Method for Robot Markers
Wataru Uemura and Takeru Nagashima
Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile
robots for tasks such as material transportation. Markers are commonly used for
robot self-localization and object identification. In the RoboCup Logistics League (RCLL), ArUco markers are employed both for
robot localization and for identifying processing modules. Conventional recognition relies on OpenCV-based image processing, which detects black-and-white marker patterns. However, these methods often fail under noise, motion blur, defocus, or varying illumination conditions. Deep-learning-based recognition offers improved robustness under such conditions, but requires large amounts of annotated data. Annotation must typically be done manually, as the type and position of objects cannot be detected automatically, making
dataset preparation a major bottleneck. In contrast, ArUco markers include built-in recognition modules that provide both ID and positional information, enabling automatic annotation. This paper proposes an automated annotation method for training deep-learning models on ArUco marker images. By leveraging marker detection results obtained from the ArUco module, the proposed approach eliminates the need for manual labeling. A YOLO-based model is trained using the automatically annotated
dataset, and its performance is evaluated under various conditions. Experimental results demonstrate that the proposed method improves recognition performance compared with conventional image-processing techniques, particularly for images affected by blur or defocus. Automatic annotation also reduces human effort and ensures consistent labeling quality. Future work will investigate the relationship between confidence thresholds and recognition performance.
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Computer Vision
Improving Supervised Machine Learning Performance in Optical Quality Control via Generative AI for Dataset Expansion
Dennis Sprute, Hanna Senke and Holger Flatt
Supervised machine learning
algorithms play a crucial role in optical quality control within industrial production. These approaches require representative
datasets for effective model training. However, while non-defective components are frequent, defective parts are rare in production, resulting in highly imbalanced
datasets that adversely impact model performance. Existing strategies to address this challenge, such as specialized loss functions or traditional data augmentation techniques, have limitations, including the need for careful hyperparameter tuning or the alteration of only simple image features. Therefore, this work explores the potential of generative artificial intelligence (GenAI) as an alternative method for expanding limited
datasets and enhancing supervised machine learning performance. Specifically, we investigate Stable
Diffusion and CycleGAN as image generation models, focusing on the
segmentation of combine harvester components in thermal images for subsequent defect detection. Our results demonstrate that
dataset expansion using Stable
Diffusion yields the most significant improvement, enhancing
segmentation performance by 4.6 %, resulting in a Mean Intersection over Union (Mean IoU) of 84.6 %.
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