arXiv_CV
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When Reconstruction Helps Event-Camera Person Triggers
Abstract
Event cameras record asynchronous changes in brightness. To use a general visual model as a recording trigger, a system must represent these measurements in a form the model can interpret. We compare direct grayscale accumulation, two temporal encodings, and two intensity reconstructors on paired real event data. On 192 windows from four held-out DSEC recordings, thresholds calibrated only on development data yielded 40 of 52 person detections and 12 of 140 background positives with E2VID. The strongest direct temporal encoding at an admissible fixed cutoff yielded 14 detections and seven background positives. A retrospective comparison at a common 10% background-positive cap yielded 40 detections for E2VID and at most 19 for any direct encoding. FireNet+ had lower preparation cost and produced 21 detections with zero observed background positives at its frozen cutoff. On the same 192 windows, Qwen3-VL-4B yielded 33/52, 2/140 with E2VID and 9/52, 9/140 with the highest-recall direct rendering, the three-bin image (person detections, background positives). A 96-second exploratory replay illustrates the sensitivity of episode capture and recording starts to cutoff selection. The experiments identify a detection benefit from reconstruction under controlled input history and distinguish it from the cost and false starts of continuous triggering.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13488
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Hand-Object Contact Transition Detection in Egocentric Video
2026-11-10 02:52:29Huy Anh Nguyen, Chang Dong, Feras Dayoub, Minh HoaiAbstract
This paper tackles the task of hand-object contact transition detection in egocentric video, which aims to localize the two temporal boundaries of a contact episode: the moment a hand first establishes contact with an object (touch) and the moment contact is released (release). We propose a unified framework that jointly models the interacting hand and object to accurately localize both transition types. The proposed framework augments hand-centric spatiotemporal representations with object-aware features learned directly from the video and introduces a transition-aware event spotting formulation tailored to contact transitions, without relying on external object or hand-object interaction detectors during inference. To support this task, we present ContactTransition, a dataset comprising 4,021 egocentric videos with 8,498 touch and 8,077 release annotations, together with object-of-interest supervision. Experiments demonstrate that the proposed method consistently outperforms strong two-stage and end-to-end event-spotting baselines on both transition types. Finally, we show that detected contact transitions provide effective temporal anchors for downstream egocentric hand-object reasoning.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13486
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Camera3R: Feed-Forward 3D Reconstruction from Heterogeneous Camera Models
2026-11-10 01:56:04Ruxiao Duan, Yunwen Zhou, Erin Hong, Dongxu Zhao, Eric Turner, Federico Tombari, Alex WongAbstract
Feed-forward 3D reconstruction (3R) from unconstrained multi-view images has recently emerged as a prominent research direction in 3D computer vision. Various foundational reconstruction models have been proposed with different architectures and reconstruction paradigms. Despite being trained on large-scale perspective datasets with substantial computational resources, these models generally struggle to generalize to non-perspective images. This limitation hinders their deployment in applications involving arbitrary, unknown, and potentially heterogeneous camera models. To build on the capabilities of existing reconstruction models with minimal retraining, we propose Camera3R, a low-rank adaptation framework that efficiently extends perspective-pretrained 3R networks to any other camera models. Extensive experiments demonstrate that, with only 0.5% additional parameters and less than a day of training, Camera3R adapts a foundational 3D reconstruction model to non-perspective cameras while fully preserving its original performance on perspective inputs. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13453
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Prompt2Echo : Text-Prompted Multi-View Echocardiography Segmentation with One Model
2026-11-10 01:08:01Moein Heidari, Alvin Kimbowa, Mahdi Tabatabaei, Afshin Bozorgpour, Sina Ghorbani Kolahi, River Jiang, Wenjin Chen, David J. Foran, Jasmine Grewal, Dorit Merhof, Ilker HacihalilogluAbstract
Automated segmentation in multi-view echocardiography is essential for comprehensive cardiac assessment, yet remains challenging due to substantial anatomical and appearance variability across standard acoustic windows. In this paper, we present P2Echo, a unified framework capable of segmenting diverse cardiac structures across multiple standard echocardiographic views using a single model controlled by structured view-aware text prompts. Unlike recent foundation model adaptations that rely on implicit latent priors, P2Echo explicitly conditions the segmentation process via a novel Dynamic Image-Text Alignment (DyITA) module. DyITA integrates a differentiable routing mechanism and token differential operators to dynamically modulate decoder features according to the requested view and target structure. This allows the network to adaptively switch its processing pathways for different views without architectural changes. Extensive experiments on a large-scale internal dataset (seen during training) and held-out evaluation on three external datasets demonstrate that P2Echo matches or exceeds both view-specific specialists and state-of-the-art universal models at a fraction of their computational cost, offering an efficient, controllable solution for multi-plane cardiac analysis. Code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13425
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Uni-Loc: Weakly Supervised 3D CT Pathology Localization via Spatially Grounded Vision-Language Classification
2026-11-09 22:58:36Hao-Chih Lee, Zahi A. Fayad, Xueyan MeiAbstract
Accurate localization of abnormalities in three-dimensional (3D) CT scans is essential for linking automated predictions with clinically relevant pathological findings. We propose Uni-Loc, a vision-language framework that unifies pathology classification and 3D localization using scan-level supervision. Uni-Loc derives both predictions and localization maps from spatial similarities between pathology-conditioned queries and image features. A token-to-voxel upsampling module enables high-resolution localization, while gradient-guided thresholding identifies pathology regions without additional learned parameters. Evaluated on five localization tasks comprising 355 annotated CT scans, Uni-Loc achieves macro-averaged voxel-level AUROC/AUPR of 96.1\%/34.5\%, outperforming relevance propagation (80.7%/13.9%) and gradient saliency (88.7%/8.1%). Uni-Loc also achieves classification AUROC of 88.39% on CT-RATE and 78.03% on RAD-ChestCT, improving over a matched baseline. These results demonstrate effective 3D pathology localization without requiring pathology-specific voxel-level supervision.
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URL
https://arxiv.org/abs/2611.13330
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Robust Flow Matching under Target Corruption and Label Noise
2026-11-09 22:47:42Mert Can Kurucu, Erik Englesson, Hossein AzizpourAbstract
Flow Matching (FM) is a strong framework for generative modeling due to its stable training and efficient sampling. However, FM assumes clean target data and labels, an assumption often violated in practice. We formulate FM training as a time-dependent regression problem over conditional vector fields and analyze how the two corruption types affect the objective: i) target corruption adds a time-dependent residual term that grows with time, ii) label noise causes updates to the wrong class-conditional vector field. Based on this analysis, we propose to improve FM robustness by mapping corruption-specific residual scores to sample weights. For target corruption, we propose the Temporal Residual Score (TRS), which emphasizes residual magnitudes at intermediate-to-late FM times, where clean and corrupted samples are more separable. For label noise, we propose the Comparative Conditional Score (CCS), which compares the residual of the observed class-conditional vector field against alternative class-conditional vector fields for the same trajectory and velocity target. Experiments across controlled corruption settings and real-world noisy labels show that the proposed methods improve robustness over standard FM and robust-training baselines, supporting corruption-specific residual structure as a practical basis for robust FM.
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URL
https://arxiv.org/abs/2611.13324
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A probabilistic model of visual segmentation explains early visual cortical dynamics
2026-11-09 22:35:26Tridib K. Biswas, Ruben Coen-CagliAbstract
Neural dynamics are a central feature of biological neural systems. In the visual cortex, the functional role of neural dynamics is poorly understood, particularly for static stimuli. The statistics of still natural images have long been used by models that successfully predict firing in early visual cortex; those models assume that neurons represent inferences about latent image features, but often ignore the dynamics of said inference. Here, we test if inferential dynamics in images explain visual-cortical dynamics. To do so, we present a biologically plausible model of cortical computation which assumes that the inference of features is coupled with inference about how those features are organized into meaningful parts, a function also known as segmentation. Given an input image, our model iterates between inferring the features and the segments, through recurrence and feedback, thus inducing dynamics in the neural representation of features. For these neural dynamics, we derive theoretical and exact predictions. Our model predicts both gain and variability in evoked responses to natural images, and we show that it captures classical single-neuron experimental findings such as gain dynamics and variability decay during stimulus presentation. The model also predicts that those dynamics are organized around inferred segment structure and more heterogeneous than previously thought, reflecting the complexity of inference for natural scenes. Finally, our analytical formulation provides a clear interpretation of our results. Insights from our normative model could generalize to segmentation problems in other experimental modalities, and help address the cost of inference in artificial networks with iterative computation.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13316
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ABRR: Transforming Temporal Sentence Grounding with Wasserstein-Adaptive Refinement and Precision Ranking
2026-11-09 22:23:50Xiang Fang, Wanlong Fang, Junhao Dong, Daizong LiuAbstract
Temporal Sentence Grounding (TSG) aims to localize temporal segments in untrimmed videos that correspond to natural language queries, a critical task for video understanding with applications in video retrieval, summarization, and human-computer interaction. Despite significant progress driven by transformer-based models, existing TSG methods suffer from two key limitations: quantization errors due to temporal downsampling, which misalign segment boundaries, and suboptimal ranking of predictions based on semantic matching rather than localization precision. To address these challenges, we propose the Adaptive Boundary Refinement and Ranking (ABRR) framework, a novel, model-agnostic post-processing approach that enhances the performance of any TSG model without retraining. ABRR integrates three innovative components: (1) Temporal Distribution Smoothing, which mitigates noise in predicted score distributions using Gaussian kernel convolution; (2) Boundary Refinement via Optimal Transport, which leverages the 2-Wasserstein distance and Sinkhorn iterations to align coarse segment boundaries with true temporal moments, grounded in Monge-Kantorovich optimal transport theory; and (3) Quality-Driven Re-Ranking, which reorders predictions using a composite score balancing localization precision and semantic relevance. By correcting quantization errors and prioritizing accurately localized segments, ABRR achieves substantial improvements over state-of-the-art TSG models. Extensive experiments on benchmark datasets, including Charades-STA, ActivityNet Captions, and QVHighlights, demonstrate consistent gains at stringent IoU thresholds (e.g., IoU=0.7), alongside enhanced ranking performance. The framework's computational efficiency and compatibility with existing models make it a versatile and impactful solution for real-world TSG applications.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13310
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Domain-Adversarial Adaptation from MURA to In-House Radiographs for Improved Adult Elbow Fracture Classification
2026-11-09 22:22:04M. Bolhassani, W. Furtado, D. Duvall, MJ Negahdar, J. Joshi, S. Contractor, A. AminiAbstract
We studied domain shift in adult elbow fracture classification from radiographs when training on a large public dataset and deploying on a small, institution-specific dataset (UofL). We implemented a domain-adversarial neural network (DANN) to align feature distributions across source (MURA) and target (UofL) domains while optimizing fracture classification. A pretrained convolutional backbone was adapted to grayscale inputs; the task head predicts fracture presence, and a gradient-reversal-based domain head encourages domain-invariant features via adversarial learning. We compared four settings: (1) Source-only, (2) Target-only (limited labels), (3) Naive merge (pooled training), and (4) DANN (MURA to UofL). Evaluation on UofL test data uses AUC-ROC, and accuracy. DANN outperformed Source-only and Naive merge baselines by reducing domain bias without overfitting to the small target set. Ablations showed that progressive adversarial weight, dataset-size-aware domain loss, and domain-balanced sampling improved stability and target performance. Results demonstrated that domain alignment is a practical path to robust clinical generalization from public to local data.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13308
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DUO: Dual Readouts of Diffusion Models for OOD Detection
2026-11-09 22:13:09Alireza Javid, Shirin Shoushtari, Nuria Gonz\'alez PrelcicAbstract
Diffusion models are powerful generative methods for learning and sampling from complex data distributions. Safely deploying these models requires detecting inputs that fall outside their training distribution. Because diffusion models recover clean samples from noisy inputs, reconstruction error is often used for out-of-distribution (OOD) detection. This approach assumes that in-distribution (ID) inputs are reconstructed more accurately than OOD inputs. However, OOD inputs may be reconstructed equally well or even better, making this score unreliable. We therefore propose the leading sensitivity spectrum of intermediate denoising features as a complementary OOD signal and demonstrate that it remains informative when reconstruction error is misleading. We introduce DUal-readout of diffusion models for OOD detection (DUO), a fully unsupervised, training-free OOD detector that combines reconstruction error and feature sensitivity. DUO improves average AUROC by approximately 6% points over the strongest baselines while substantially reducing inference cost, and achieves SOTA on near-OOD and high-resolution benchmarks.
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URL
https://arxiv.org/abs/2611.13304
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Domain adaptation of a video Foundation model for Heart Failure Diagnosis from Echocardiography
2026-11-09 22:04:03Mahyar Bolhassani, Tijana Geroski, Narayana S. Singam, Amir AminiAbstract
The rapid advancement of foundation models has transformed computer vision, yet their adaptation to medical imaging, particularly purpose built foundation models for spatio temporal echocardiography videos, remains limited. In this work, we explore the feasibility of fine tuning a pretrained foundation model for video (TimeSformer) for automated diagnosis of reduced ejection fraction heart failure (HFrEF) from apical four chamber videos. The model is finetuned on the publicly available RVENet A4C dataset with a phase aware clip generation strategy that captures complementary systolic and diastolic dynamics. We employ divided space time attention, layer wise learning rate decay, and multi clip inference to enhance robustness under limited data. Experimental results show that full fine tuning of the proposed model outperforms classifier only tuning and a 3D CNN baseline. These findings highlight the potential of leveraging foundation video models for echocardiography based heart failure diagnosis and motivates further domain adaptation studies in medical imaging.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13297
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RetinexGS: Retinex-Inspired Gaussian Splatting for Low-Light RAW Reconstruction
2026-11-09 21:44:31Yulang Fei, Huaiyuan Weng, Chul Min YeumAbstract
Standard 3D Gaussian Splatting (3DGS) pipelines struggle with severely underexposed images, producing unstable colors, floating artifacts, and incomplete reconstructions in dark regions. To better account for noise in these low-light observations, RAW images offer a natural basis for noise-aware reconstruction by preserving linear sensor measurements and providing access to noise metadata. We present RetinexGS, a RAW-domain 3DGS framework that combines Retinex-inspired appearance modeling with sensor-informed optimization for low-light novel-view synthesis. We factorize each Gaussian's color into a view-independent base color and a shared, position- and view-dependent modulation field, regularizing the two components separately to constrain appearance learning from noisy observations. We use DNG noise metadata to weight the reconstruction loss for signal-dependent noise, while distortion-aware rendering avoids spatially resampling RAW targets. To address sparse geometry in dark regions, we introduce depth-guided initialization. We evaluate RetinexGS on three public RawNeRF scenes and two custom-captured low-light RAW scenes. With all methods trained on the same RAW inputs for each scene, RetinexGS outperforms LE3D and RawNeRF in mean sRGB PSNR by 2.18 dB and 2.71 dB respectively, across the five scenes, while supporting real-time novel-view rendering.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13280
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RaMOD: Moving-Object Detection from Raw mmWave Radar Cubes under Ego Motion
2026-11-09 21:36:59Yuan Fang, Long Zhuang, Lingzi Xing, Xijia Wei, Jingqi Jiang, Fangzhan Shi, Simon Julier, Kevin ChettyAbstract
Millimeter-wave (mmWave) radar provides weather-robust sensing and direct Doppler measurements, yet automotive perception pipelines often reduce its output to sparse point clouds or two-dimensional projections, leaving raw range-azimuth-Doppler (RAD) cubes underutilized. Ego motion induces Doppler responses from world-static structures, while Doppler wrapping aliases radial velocities, complicating moving-object detection and velocity estimation. We present RaMOD (Radar-based Moving-Object Detection), a real-time framework for moving-object detection and velocity estimation from raw RAD cubes acquired by a vehicle-mounted single-chip radar. RaMOD explicitly estimates frame-wise ego motion and uses the estimated radar translational velocity to predict the Doppler region of world-static structures and suppress returns within it. It temporally encodes the suppressed cubes into multi-scale features to predict RAD-space boxes with class labels. For each detected object, it predicts radial and forward velocities under raw-Doppler and Doppler-unwrapped hypotheses, together with Doppler-wrapping probabilities. On our BRIDGE-Liv dataset, RaMOD outperforms all compared raw-radar methods across RAD-cube, RA, and RD detection settings, achieving 80.8% cube mAP at IoU 0.30, 15.7 percentage points above the strongest RAD-cube baseline. Velocity MAE is 1.128 m/s, with 99.87% Doppler-wrapping accuracy.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13271
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Rethinking Evaluation for Video Capsule Endoscopy: Validation Protocol, Source-Video Diversity, and Cross-Dataset Shift
2026-11-09 21:20:54Smriti Regmi, Debesh JhaAbstract
Automated video capsule endoscopy (VCE) classification remains challenging due to severe class imbalance, limited source-video diversity, and variations in image appearance across datasets. Although deep learning models have demonstrated promising performance on established benchmarks, their reliability under different validation protocols and external dataset shift remains insufficiently understood. In this work, we introduce DMSNet, a deformable adaptive multi-scale edge-guided attention network integrating adaptive multi-scale feature fusion, deformable lesion refinement, edge-guided attention, and sparse feature selection. We benchmark DMSNet against 18 representative convolutional, transformer-based, and pretrained vision architectures on Kvasir-Capsule under random frame-level and source-video-disjoint validation protocols. We further identify source-video overlap between the original training and test splits and evaluate model performance on both the complete test set and a source-video-disjoint subset. Under random frame-level validation, DMSNet achieves an MCC of 0.442 $\pm$ 0.020 on complete test set and 0.456 $\pm$ 0.037 on the source-video-disjoint subset. On the external CV2024 dataset, ConvNeXt-Tiny achieves the highest MCC of 0.207, while performance degrades substantially across architectures relative to in-domain evaluation. We further investigate source-video diversity using a per-class cap of 200 training frames. With the total training-set size held fixed, increasing the maximum source-video coverage from four to eight improves MCC for 17 of 19 evaluated architectures. Overall, these findings demonstrate that model rankings and classification performance depend substantially on evaluation protocol, training-source diversity, and cross-dataset shift, emphasizing the need for source-aware validation and rigorous external evaluation of VCE classification systems.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13245
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Image Quality and the Reliability of Fabric-Density Measurement: Counting Threads from Smartphone Photos Through a Pick Glass
Abstract
Thread density, the threads per inch along and across a woven fabric, is a basic specification that buyers check whenever fabric is sold. Without laboratory instruments, inspectors count it slowly, by eye, through a pick glass, a folding 10$\times$ magnifier with a one-inch window. A smartphone photo through that glass could make the check faster and leave a record. We judge such a photo by what it is used for, as in task-based image quality: it is good if the thread count from it is within $\pm$5% of the true count, a common tolerance in fabric specifications. We introduce PickGlass-TPI: 76 factory scenes of 44 fabric patches, each counted in an open annotation tool by a textile-trained annotator, whose counts are the reference. On it we benchmark frontier and open vision-language models, CNNs, counting specialists and a training-free spectral counter (PG-Lattice). No method agrees with the reference within the tolerance. The best methods are off by about a quarter: a small open model that we tuned only on synthetic images (Molmo2-4B, 25%) is level with the best frontier model, GPT-6.1-sol (27% when asked directly, 25% given four magnified close-ups of the window). Both err less than a non-expert annotator (37%), but only the tuned model also beats, on both error measures, a baseline that guesses each scene's density as the average of the others. PG-Lattice most often repeats its count within 5% across two photos of the same patch, but its misses, like GPT-6.1-sol's errors, are often a factor of two. Even the reference repeats a count across such re-shoots only to a median 9%, so one photo cannot yet be shown to meet $\pm$5%; the image quality that matters is reliability across re-shoots. Generic no-reference quality scores chosen before labeling do not rank PG-Lattice's error, and generative enhancement raises perceptual scores but does not reliably improve the count.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13200
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Comparative Evaluation of Deep Learning Models for Geographic Atrophy Segmentation in En Face OCT Images
2026-11-09 20:12:13Utkarsh Doshi, Adarsh Gadari, Sharat Chandra Vupparaboina, Joseph DeCicco, Giulia Gregori, Nicola Valsecchi, Nasiq Hasan, Elham Sadeghi, Jos\`e-Alain Sahel, Jay Chhablani, Kiran Kumar Vupparaboina, Sandeep Chandra BollepalliAbstract
Background: Geographic atrophy (GA) is an advanced form of age-related macular degeneration (AMD). It causes irreversible vision loss due to progressive degeneration of the retinal pigment epithelium (RPE) and photoreceptors. Accurate segmentation of GA in en-face image is crucial for early diagnosis, monitoring disease progression, and developing personalized treatment strategies. However, existing deep learning approaches are typically evaluated using single architectures and single-device datasets, limiting insight into relative performance and cross-device robustness. Methods: We trained and systematically compared five deep learning architectures for GA segmentation: Nested U-Net, ResU-Net, nnUnet, Swin-Unet and MedSegDiff model, together with a thresholding based classical baseline. The models were trained on a dataset of en-face OCT images derived from 269 OCT volumes from two different devices, following a three-step preprocessing technique involving normalization and inpainting to enhance the quality of the en-face images. Results: On the fixed held-out validation set comprising OCT scans from both Cirrus (R) (Carl Zeiss Meditec, Inc., USA) and Spectralis (R) (Heidelberg Engineering, Germany) devices, nnUnet achieved the highest mean Dice similarity coefficient (DSC) of 0.8882 +/- 0.0033. The other models yielded the following Dice scores: Nested U-Net (0.7932 +/- 0.0423), ResU-Net (0.8453 +/- 0.0074), Swin-Unet (0.6886 +/- 0.0294), and Diffusion model (0.7959 +/- 0.0242). Conclusions: nnUnet framework, demonstrated most reliable performance for GA segmentation in en-face OCT under limited data conditions. It offers the best balance of accuracy, stability, and efficiency for GA quantification.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13191
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X Marks the Spot: Diffusion for Image Geolocalization with Location Representations
2026-11-09 20:03:12Brian Wei, Chih-Chuan Hsu, Tianyi Gao, Daniel Cher, Eric Xing, Danielle Beaulieu, Nathan JacobsAbstract
Image geolocalization predicts the location at which a photo was taken. Accurate geolocalization requires discriminating between both physically and semantically adjacent areas on Earth. However, prior diffusion-based approaches predict locations by generating coordinates, which are semantically unstable over space. We avoid this limitation by learning geolocalization behavior in the pretrained representation spaces of location encoders, which provide both positional and semantic understanding. To make this space usable for coordinate prediction, we introduce a Location Autoencoder (LAE), which pairs a frozen location encoder with a learned decoder mapping embeddings back to coordinates with kilometer-level accuracy worldwide. In the LAE space, we train DIG, a latent diffusion model that transports noise to location embeddings conditioned on image features. DIG consistently outperforms coordinate-based generative models and sets a new state of the art for parametric geolocalization on three benchmarks: OSV-5M, Im2GPS3k, and YFCC4k. Notably, DIG is the first generative model to outperform retrieval-based methods, and further improves retrieve-and-rerank systems when used as a prior. Code and models will be released.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13180
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Multimodal Judges Flag, Humans Decide: A Review-and-Edit Framework for AI-Generated Product Video Ads
2026-11-09 20:00:23Anoubhav Agarwaal, Sukesh Perla, Karthikeyan Mohanraj, Susmita GhoseAbstract
Generative pipelines can now turn every product page of an e-commerce catalog into a short video ad, but deciding whether such an ad is usable is not one good-or-bad call. We describe how we operationalized the review and editing of these ads so that both can keep pace with generation: reviewers go through each ad scene by scene and answer six questions, on the product, scene quality, pronunciation, script, sound and captions; judges built from vision-language, speech and language models file each finding under the matching question, and reviewers accept or reject each finding and add what the judges missed. Each confirmed issue then opens an edit tool matched to it. The questions and the routing from a finding to its fix do not depend on the models that generate the ad. Across 120 ads from our pipeline, reviewed with judge findings on screen, reviewers marked the product wrong on 60% of scenes, and script, caption or sound problems on under 10% each. On 60 of these ads, they marked the product wrong on 58% of scenes, far more than the 26% marked by two reviewers who later saw the ads with the judges hidden; one reviewer who saw the ads both ways marked 57% with judge findings and 19% without. On 120 blind-labeled scenes, a product judge that watches the clip with the catalog photos reached AUC 0.84, while video-quality and photo-similarity scores were close to chance. We tune the judges for recall: the product judge catches nearly all defects but also flags many clean scenes, so reviewers decide; together, the judges widen what reviewers mark and reviewers add what the judges miss. Without judge findings, one reviewer's marking faded over a session. We release a review-portal template and our review data (120 ads and 562 scenes with judge findings and human marks, plus 120 blind-labeled scenes with baseline scores) at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13178
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GroundCascade: Selective Second Opinions for GUI Grounding
Abstract
Graphical user interface (GUI) grounding maps a screenshot and a natural-language instruction to the screen location where an agent should act. Zoomed views can reveal small targets but may lose the context needed to identify them. We introduce GroundCascade, a training-free controller that uses agreement between a grounder's full-screen and zoomed predictions to decide when to consult a complementary model. It selects between zoomed predictions while preserving corroborated full-screen locations. An optional Finalizer predicts from an unmarked joint crop when disagreement remains (Full); omitting it yields the two-stage Selective variant. GroundCascade reaches 85.33% on ScreenSpot-Pro with Indeed-UI-32B/8B/32B and 88.43% on the visible-target OSWorld-G-Refined subset with Qwen3.6-27B/3.8-27B/3.8-27B, gains of 2.66 and 2.35 percentage points over the stronger standalone two-pass grounder. On UI-Vision, GroundCascade reaches 72.20% category-balanced accuracy, with Selective slightly higher at 72.28%. Across these three evaluations, GroundCascade in Full mode uses 2.42-2.73 calls per request, 31-39% fewer than unconditionally acquiring both models' two-pass predictions. At matched second-model call budgets, selective acquisition gains 1.52-2.56 points over standalone Two-pass, compared with 0.22-0.69 points from stratified random acquisition. GroundCascade leads the official ScreenSpot-Pro leaderboard and exceeds reported GUI-specialist comparisons on OSWorld-G-Refined and UI-Vision, subject to differences in external evaluation protocols.
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URL
https://arxiv.org/abs/2611.13177
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Robust Reconstruction of Power Lines from Incomplete Mobile and Aerial LiDAR Data
2026-11-09 19:58:33Mohamed Gaha, Damos Ayobo Abongo, Philippe Massicotte, Eya Ben Kacem, Wael Jaafar, Rami Langar, Christian ButeauAbstract
Accurate vegetation-encroachment assessment and risk mitigation require electric utilities to accurately locate and characterize distribution-network assets, including poles and power lines, and distinguish among medium-voltage, low-voltage, neutral, and telecommunication lines. Mobile and aerial Light Detection and Ranging (LiDAR) point clouds provide detailed 3D representations of these assets and their surroundings. However, power-line detection and reconstruction remain challenging because conductor returns are often sparse, incomplete, noisy, or occluded by vegetation. These challenges are especially significant in distribution networks, where lower pole heights and complex line configurations place conductors close to trees. In addition, mobile and aerial LiDAR datasets differ substantially in point density, viewing geometry, spatial coverage, and occlusion patterns. We propose a robust and automated reconstruction method combining scaled density-based clustering, robust polynomial fitting, and iterative fragment merging to recover individual conductors under severe data incompleteness and occlusion. Following semantic classification, the same reconstruction pipeline processes both mobile and aerial LiDAR data without modality-specific code or parameter tuning. The method is evaluated through degradation experiments, benchmarking, parameter sensitivity analysis, and large-scale validation. On the aerial dataset, it achieves 96.87% precision, 96.66% recall, a 96.76% F$_1$-score, and a geometric RMSE of 0.127 m. On the mobile dataset, where conductor-level reference annotations were unavailable, it achieves a point-to-model RMSE of 0.045 m and a reconstruction coverage of 95.27%. Source code, documentation, and evaluation data are publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2611.05197
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