Computer Vision
End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction
Zeyu Jia and Xin Ming
Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic assessment. Conventional voxel segmentation models often sever delicate bifurcations, while heuristic Euclidean Minimum Spanning Trees introduce non-anatomical shortcuts. Moreover, linear Poiseuille flow neglects quadratic kinetic dissipation across arterial narrowings, underestimating ischemia. We formulate an end-to-end framework decoupling continuous
geometric arborescence generation from non-linear hemodynamics. First, an autonomous 3D Ostium Landmark Localization Head with dual-sinus query channels and spherical-gated refinement eliminates centerline seeding dependency, achieving cohort mean localization error of 7.63 mm (7.43 mm LCA, 7.83 mm RCA; 71.4% <= 8.0 mm) from raw contrast context. Second, a Spatially-Grounded Deformable Step Flow Architecture queries continuous 3D feature pyramids via trilinear sampling, sequentially generating trajectories with anchor boundary enforcement (X(0) = P_start). Third, a Top-Down Recursive Arborescence State Machine detects bifurcation peaks via Tree-NMS and parameterizes predecessor parent pointers (p_k < k), guaranteeing single connected acyclic tree
topology (beta_0 = 1, beta_1 = 0) with differentiable step termination. Fourth, an iterative Picard non-linear Kirchhoff solver with Young-Tsai / Gould quadratic dissipation enforces machine-precision mass conservation (residual 5.82e-11 mL/s). Across 14 development patients under standardized in-silico stenosis stress testing (Q_0 = 4.0 mL/s), linear Poiseuille flow misclassifies 75% diameter lesions as non-ischemic (FFR > 0.80) in 14/14 cases, whereas our non-linear solver captures functional ischemia (FFR = 0.5864, lesion disparity 32.89 mmHg, p = 6.10e-5) with 3.66x collateral shunting. Test set firewall isolation was maintained.
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Computer Vision
LoDEOT: Low-Dimensional and Efficient Offset Tokens for Building Footprint Extraction from Off-Nadir Imagery
Kai Li, Zigan Zhou, Zhenyang Li et al. (+7 more)
Instance-level roof-to-footprint offset (RFO) prediction is central to extracting building footprints from off-nadir imagery. Query-based pipelines commonly use high-dimensional instance
tokens to predict signed two-dimensional RFOs. We investigate whether RFO prediction can instead use a compact offset
token. Under local pinhole projection and vertical-extrusion assumptions, the idealized RFO map admits a five-parameter sufficient descriptor comprising intrinsic shape, composite amplitude, and relative geometry. This factorization provides a structural prior for a five-dimensional offset
token, whose channels learn task-relevant latent representations through end-to-end training. Based on this design, we propose LoDEOT, which retains high-dimensional instance
tokens for detection and segmentation but maps instance-
token, concentration-gated roof, and box-mask evidence to a five-dimensional offset
token followed by an independent two-dimensional readout. Known denoising-query target indices further align each supervised decoder-layer estimate with the same clean instance RFO, organizing successive predictions as target-aligned recovery under perturbed query conditions. Experiments on five real-world building
datasets demonstrate the effectiveness of LoDEOT for building footprint extraction. Experiments on real-world building
datasets demonstrate that a five-dimensional offset
token can support accurate RFO prediction. On BONAI, LoDEOT achieves the best roof-detection bAP and bAP50 and leads all five offset-corrected footprint metrics among the evaluated end-to-end methods, with FAP50 of 54.58 and mEPE of 5.23 pixels. Its FAP50 exceeds those of the evaluated end-to-end baselines by 7.56-16.85 percentage points.
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All authors Kai Li, Zigan Zhou, Zhenyang Li, Hui Shan, Zhe Chen, Yupeng Deng, Zhihao Xi, Yu Meng, Yifan Peng, Xiangyu Zhao
Computer Vision
Fitting Vision Adapters at Frontier Scales
Jaehoon Lee, Harry Partridge, Mudith Jayasekara et al. (+3 more)
Training a small projector between a frozen
vision encoder and
language model is an established approach to
multimodal learning. As the parameter count of
language models scales dramatically, we revisit which
vision capabilities this approach can add while keeping their pretrained weights fixed. Here we train a 50M parameter projector from the
vision encoder of Kimi K2.6 to GLM 5.2 and 5.3, both models without native
vision capabilities, and further present a reproducible recipe for training these adapters at scale. We study the following: (a) how
vision capabilities of
multimodal models scale as purely the
language model side scales, and (b) what specific
vision capabilities are able to be imbued into a pure
language model at scale, and which ones remain limited. We evaluate on MMMU-Pro and BLINK, examining both overall performance and results on individual visual tasks.
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All authors Jaehoon Lee, Harry Partridge, Mudith Jayasekara, Charles O'Neill, Max Kirkby, Michael Psenka
Computer Vision
TasteRoute: Personalized Routing for Video Generation
Zhi Rui Tam, Chao-Chung Wu, Sin-Han Yang et al. (+8 more)
Rapid progress in
video generation has led to a plethora of models that differ substantially in capability and generation cost. This raises a natural question: can each request be efficiently routed to an appropriate model? We find that even when the consensus of the other annotators is used as an oracle, it agrees with each annotator's own favorite only 34-55% of the time. Motivated by this observation, we introduce TasteRoute, a personalized
video-generation router that selects a generator jointly based on the input request, user preferences, and available generation budget. Across text-to-
video and image-to-
video settings, TasteRoute is competitive with strong simple baselines on preference routing while reducing average generation cost. The cost saving increases under higher budget caps. Finally, we release TasteRoute-3k, a human-annotated
dataset containing multi-model
video comparisons, quality judgments, preference rankings, and user-profile signals to facilitate future research on personalized and cost-aware
video routing.
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All authors Zhi Rui Tam, Chao-Chung Wu, Sin-Han Yang, Peyton Ku, Brendan Kuang, Tzu-Ting Hsieh, Min-Fang Hsu, Fang-Ling Tsai, Yun-Nung Chen, Wei-Chiu Ma, Chieh-Yen Lin
Computer Vision
Spatial Supervision Without Attribution Optimization: Improving Post-Hoc Class Activation Maps via Box-Guided Evidence Routing
Wenhao Liang, Liangwei Nathan Zheng, Lin Yue et al. (+3 more)
Post-hoc class activation maps (CAMs) are a standard tool for inspecting the evidence behind an image classifier's predictions, yet nothing in ordinary training encourages these maps to be spatially appropriate. We study whether inexpensive spatial supervision can improve a classifier's own predicted-class Grad-CAM without ever optimizing an attribution map. Box-Guided Evidence Routing (BGER) trains a lightweight gate on the final feature map under box or mask supervision and routes classification through the gated features, while Grad-CAM is computed separately at the pre-gate representation, so the evaluated map never enters the training objective. With a BCE routing loss, BGER raises MaxBoxAccV2 from $0.584$ to $0.715$ on CUB-200-2011 and from $0.757$ to $0.832$ on Stanford Dogs at comparable accuracy. Matched controls attribute most of the ResNet-50 gain to the spatial supervision reshaping the backbone rather than to routing itself: when classification bypasses the gate, most of the improvement remains, and detaching gradients through the gate leaves the ResNet-50 result nearly unchanged. The same detachment preserves most of the gain in two DenseNet-121 chest X-ray settings but removes the apparent gain on Swin-T, and directly supervising the CAM reaches stronger localization at a larger accuracy cost. Overall, spatial supervision can improve separately evaluated post-hoc CAMs, but both the mechanism and the size of the benefit depend on the architecture and the evaluation setting.
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All authors Wenhao Liang, Liangwei Nathan Zheng, Lin Yue, Wei Emma Zhang, Mingyu Guo, Weitong Chen
Computer Vision
fMRI-TAMCL: Text-Anchored Supervised Multimodal Contrastive Learning for fMRI-Based Brain Disorder Classification
Juliana Mantebea Danso, Enoch Opanin Gyamfi and Mylene C.Q. Farias
Resting-state fMRI is important in the classification of
brain disorders, but highly
multimodal and exhibits strong multisite heterogeneity. Existing methods fuse images, BOLD-based functional connectivity, and phenotypic data modalities. Unlike other medical imaging
datasets, rs-fMRI
datasets rarely include a text modality, so they are generated from phenotypic data or BOLD activations. These text generation methods rely on fixed assumptions for subjects, sites, devices, and protocols, leading to poor generalization across
datasets. We propose fMRI-TAMCL, a text-anchored
multimodal contrastive learning framework that integrates fMRI images, sparse FC, and generated subject-specific text. Its Subject-Adaptive Threshold Derivation module generates BOLD activation text, while Feature-Value Serialization module generates phenotypic text. All three modalities are encoded as clustered graphs, projected onto a shared unit hypersphere space, aligned using pairwise, text-anchored supervised contrastive learning, and fused with
attention. fMRI-TAMCL proves its generalization capability across five
datasets outperforming 29 baselines with 78.6%-86.4% accuracy in downstream classification.
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Computer Vision
Certification of Real Images through Calibrated Content Authentication
Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam et al. (+2 more)
Generative models can synthesize high-quality inauthentic multimedia content that is already being misused at scale. We evaluate twenty deepfake detectors against ten generators released in the last four years and find accuracy decreasing over time, from near-perfect 99.5% to 76%.
Adversarial perturbations further reduce every baseline detector to below 2% accuracy, effectively inverting the detector's assigned label. We argue that this unreliability reflects a fundamental ambiguity: generators can reproduce authentic content exactly (e.g., through memorization), so content alone cannot reveal the true provenance label. For this reason, content produced by a generator must admit a faithful reconstruction by that same generator, and finding such a reconstruction makes synthetic provenance plausible and authenticity plausibly deniable. We therefore propose and evaluate a detection paradigm that outputs a calibrated prediction of whether authenticity is plausibly deniable: a faithful reconstruction by any known generator establishes plausible deniability, while calibration bounds how often content from known generators fails to be reproduced. Our evaluation shows that (i) our detector can be calibrated so that at most 1% of generated content is wrongly certified, an operating point at which most baseline detectors reach near-zero recall, including the strongest with 93% accuracy; (ii) calibrating a stricter security threshold on attacked samples preserves this bound against adaptive adversaries within the evaluated bounded-perturbation attack space, whose perturbations break every baseline, but does not cover arbitrary
adversarial transformations; and (iii) post-hoc verifiability is eroding, as 1,116 of 3,000 Reddit images resist reproduction by a 2022 generator, but only 55 to 79 resist reproduction by 2024 generators.
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All authors Sarim Hashmi, Abdelrahman Elsayed, Mohammed Talha Alam, Samuele Poppi, Nils Lukas
Computer Vision
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection
Dohun Kim and Jinmyung Jung
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, and the way they are combined determines whether cross-scale structure is preserved. Summation collapses the two scales before the scan, so the scan has no cross-scale structure to exploit, while concatenation keeps both scales but at far higher cost. To address this, we propose Weave Mamba Fusion (WMF), which interleaves two adjacent pyramid scales column by column so that each step of a horizontal bidirectional SS2D scan moves from one scale to the other. With partial-channel processing and parameter-free de-weaving, WMF enables efficient cross-scale interaction while preserving feature structure. Integrating WMF into every fusion node yields WeaveBiFPN, the neck of our WeaveFace detector. On WIDER FACE, WeaveFace achieves 91.41% mean AP with only 0.34M parameters and 1.16 GFLOPs, outperforming prior detectors under 0.5M parameters. Its largest gains are on the Hard subset, where it reaches 87.14% AP. The code is publicly available at \url{https://github.com/dohun-mat/WeaveMambaFusion}.
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Computer Vision
Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training
Yongxin Ning, Runliang Niu, Qianli Xing et al. (+4 more)
Graphical User Interface (GUI)
agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However, training highly capable and generalizable
agents fundamentally relies on massive, high-fidelity visual-action trajectories, which are notoriously difficult to acquire. While human demonstrations are unscalable, existing GUI world models rely on text descriptions or HTML rendering, discarding crucial pixel-level visual details like icons and layout styles. To address this issue, we introduce Infinite-Dreamer, a simulation-free data synthesis method powered by a pixel-level Image Editing World Model. By conceptualizing GUI transitions as image editing tasks, we leverage
Vision-
Language Models (VLMs) to describe action-induced UI changes as structured delta-text. We then fine-tune an image editing backbone to controllably synthesize realistic screenshot transitions. We utilize this model to generate both single-frame visual robustness data and multi-step imaginary trajectories. To validate the effectiveness of our approach, we fine-tune the Qwen3-VL baseline solely on the synthesized data to obtain Infinite-Actor, and evaluate it on AndroidWorld, MobileWorld, and AndroidControl-Curated
benchmarks. Infinite-Actor consistently outperforms the Qwen3-VL baselines across scales: Infinite-Actor-8B improves AndroidWorld Pass@1 by +4.45 and nearly doubles the MobileWorld Pass@3 success rate, while Infinite-Actor-2B improves Pass@1 by +9.05. Code is available at https://github.com/swaydy-n/Infinite-Dreamer.
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All authors Yongxin Ning, Runliang Niu, Qianli Xing, Zhiyi Duan, Qingzu He, Pan Wang, Qi Wang
Computer Vision · Cross-listed from cs.RO
Dual-Rate Force-Image Control with Model-Based Orientation Limits for Robotic Ultrasound
Tyler Foster, Qiang Zhang, A B M Tahidul Haque et al. (+1 more)
Robotic ultrasound couples a high-rate contact-force loop with slower, delayed image feedback, so image-guided ultrasound probe rotation can perturb contact force before the resulting image response is observed. We derive a closed-form orientation-rate limit that bounds the modeled rotation-induced estimated-force excursion over a finite horizon while accounting for disturbance rejection by the fast force loop. The limit depends on local contact stiffness, force-loop gains, a conservative rotation-to-force gain bound, the excursion budget, and the prediction horizon. We implement this model in a dual-rate controller with timestamp-based delay reconstruction and joint-torque-based force estimation, and evaluate it on a curved gelatin phantom using paired controller comparisons and component ablations. Relative to unconstrained image guidance, the proposed rate-limited controller reduced first-second root-mean-square (RMS) estimated-force error by 0.40 N while increasing cue-convergence time by 0.94 s. A fixed rate cap near the analytically predicted ceiling produced no resolvable difference in force error and converged 0.32 s faster, indicating that the principal practical value of the model is the rate-design rule rather than online prediction. Delay reconstruction had no resolvable effect at the tested latency. A single-subject popliteal scan demonstrated feasibility, although the image cue was noise-limited on heterogeneous tissue.
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All authors Tyler Foster, Qiang Zhang, A B M Tahidul Haque, Anh Thu Nguyen
Computer Vision
Kiyohiro Nakayama, Brian Chao, Jan Ackermann et al. (+5 more)
Image and
video diffusion models allocate equal computation to every region, even when the intended scene calls for varying levels of detail. The spatial distribution of detail can often be anticipated before generation, indicating where computation can be reduced. We introduce Level-of-
Token (LoT)
Diffusion, a framework that turns this knowledge into an explicit multiresolution
token layout (Level-of-
Token layout) for adaptive and efficient generation.
Tokens represent rectangular patches of varying sizes and shapes, allocating finer
tokens where detail is needed and coarser
tokens elsewhere. We adapt pretrained
diffusion transformers to LoT layouts through a patch-wise asymmetric flow parametrization and
embeddings for multiresolution
tokens, preserving full-resolution flow prediction at every denoising step while processing only a reduced
token sequence. LoT
Diffusion enables layout-adaptive generation while preserving pretrained generative priors. We demonstrate LoT with layouts derived from semantic masks, bounding boxes, texture variance, and depth-of-field cues, as well as agentic plans. Across image and
video generation, LoT offers favorable quality-efficiency tradeoffs, with significant speedups determined by the layout's
token budget. Our project website is at https://georgenakayama.github.io/lotdiffusion/.
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All authors Kiyohiro Nakayama, Brian Chao, Jan Ackermann, Hansheng Chen, Federico Tombari, Leonidas Guibas, Lior Yariv, Gordon Wetzstein
Computer Vision
Gauss-Map Variation for Image Denoising: Geometric Analysis and an Anderson–Accelerated Majorization–Minimization Method
Haibin Su
We propose a Gauss-map variation (GMV) model for image denoising that measures the spatial variation of the tangent-plane projectors of the scaled image graph. We establish an equivalent representation of the regularizer in terms of the corresponding Gauss map and, using differential
geometric tools including tubular coordinates and the Frenet frame, analyze its behavior across general $C^2$ and piecewise $C^2$ boundaries. The resulting estimates provide edge- and corner-contrast preservation properties. To solve the proposed model, we introduce a bilinear decomposition involving a unit normal field and a scalar magnitude field and develop an Anderson-accelerated majorization–minimization
algorithm. The normal field subproblem admits an explicit pointwise majorization–minimization update, which is combined with an Anderson acceleration. For both $L^1$ and $L^2$ data fidelity terms, we establish sufficient decrease and boundedness of the iterates and prove that the generated sequence converges to a critical point of the penalized model. Numerical experiments on synthetic and natural images demonstrate the boundary preserving capability of the proposed model and its competitive performance in removing Gaussian and impulsive noise.
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Computer Vision
FairRSFM: A Biome-Aware Benchmark and Debiasing Framework for Remote Sensing Foundation Models
Md Aminur Hossain, Omkumar Vaghasiya, Rajeev Ranjan Dwivedi et al. (+2 more)
Remote sensing foundation models (RSFMs) are commonly evaluated using aggregate metrics, which can hide systematic performance disparities across ecological regions. We introduce FairRSFM, a biome-aware
benchmark for evaluating ecological group robustness in RSFMs. FairRSFM maps georeferenced samples from 14 terrestrial biome classes into six ecologically meaningful macro-groups and evaluates models under a unified frozen-backbone evaluation protocol. The
benchmark covers four downstream
datasets: m-EuroSAT, m-BigEarthNet, m-SA-Crop-Type, and MMEarth20K with Dynamic World label maps. Using Prithvi-EO-2.0, SatMAE, and DOFA across three random seeds, we show that aggregate performance consistently masks biome-dependent disparities across architectures and tasks. For example, Prithvi-EO-2.0 reaches 90.98% overall macro-F1 on m-EuroSAT but a mean worst-group score of only 83.72%, while m-SA-Crop-Type drops from 27.30% overall mIoU to 18.47% in the Xeric and Mineralogical group. We further evaluate Biome-Orthogonal Linear Probing (BOLP), Dynamic Biome Reweighting (DBR), and GroupDRO as complementary mitigation baselines. Their effectiveness is model- and task-dependent; for example, BOLP improves Prithvi-EO-2.0 worst-group F1@opt on m-BigEarthNet from 46.12% to 50.27% without updating the RSFM backbone. FairRSFM provides a reusable protocol for diagnosing and mitigating ecological robustness gaps in remote sensing foundation models. Code and
datasets are available at: https://github.com/aminurhossain/FairRSFM.
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All authors Md Aminur Hossain, Omkumar Vaghasiya, Rajeev Ranjan Dwivedi, Vinod Kurmi, Biplab Banerjee
Computer Vision
A Spatiotemporal Semantic Importance-Guided Unified Compression and Editing Framework for AI-Generated Videos
Xihua Sheng, Dong Liu and Chang Wen Chen
AI-generated
videos are rapidly increasing in volume, duration, and resolution, creating growing demands for efficient storage and transmission. Unlike natural
videos captured from the physical world, AI-generated
videos are samples from a learned generative distribution, where semantic structures are critical to content consistency, while many local textures and
stochastic details can be plausibly regenerated. This distinction suggests that compression should preserve semantically important spatiotemporal information rather than reconstruct every pixel of a particular generative sample. Beyond reconstruction, AI-generated
videos also create a practical need for
prompt-based editing, where users expect to modify generated content while preserving its original spatiotemporal semantics. Motivated by these observations, we propose a unified compression and editing framework for AI-generated
videos that incorporates a frozen
video generator as a reusable generative prior. Within this framework, we design three spatiotemporal semantic importance-guided techniques that respectively address what to transmit, how much to transmit, and how to use the transmitted side information. First, an innovation selection method projects the latent discrepancy using spatiotemporal semantic importance, so that the selected innovations prioritize semantic invariants over replaceable generative variations. Second, a frame-adaptive bit allocation method estimates the nonuniform semantic demands of latent frames and allocates more innovations to frames requiring stronger semantic preservation. Third, a unified reconstruction and editing method continuously adjusts the influence of the transmitted side information, enabling the same compressed representation to provide strong guidance for faithful reconstruction or serve as a flexible semantic anchor for structure-preserving
prompt-driven editing.
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Computer Vision
InteractionBench: A Real-Time Interaction Benchmark for Streaming Video Systems
Enxin Song, Suhao Yu, Yifei Xu et al. (+7 more)
A
video assistant must speak when its instruction warrants a response and stay silent otherwise. We introduce a
benchmark that evaluates this decision for the complete system of model,
memory, and response controller. InteractionBench covers query responses, event triggers, and ongoing updates in 1,060 interactions over 812
videos, with 69 negative streams and 53 suites that pair counted events with look-alike near misses. It scores content accuracy, timing accuracy, and silence compliance on the
video clock. Timely
speech costs silence across systems. Polled Qwen3-VL-8B reaches 77.8 timing accuracy but 10.9 silence compliance. A native real-time interaction system reaches 29.2 silence compliance at 66.8 timing accuracy, yet emits on 89.9% of negative streams. No open-weight system clears a third of the near-miss suites. Fewer replies help only when chosen, as random deletion merely trades timing for silence. Offline scores miss these failures and mispredict online behavior. Adding restraint is costly, as the native system's controller adds little by itself and agentic systems add it only at about 30 s per poll.Project page: https://www.enxinsong.com/projects/interactionbench/ Code: https://github.com/Espere-1119-Song/InteractionBench Data: https://huggingface.co/
datasets/InteractionBench/InteractionBench
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All authors Enxin Song, Suhao Yu, Yifei Xu, Barbara Su, Weili Xu, Wenhao Chai, Yao Tang, Jie Deng, Haiyang Xu, Jiatao Gu
Computer Vision
Controllable Road Marking Generation
Zhiyu Cai, Yufan Zhang, Ruichen Tan et al. (+2 more)
Lane and road markings provide critical guidance for vehicle navigation and multi-
agent coordination, yet authoring them at scale remains a manual workflow that limits quantitative analysis and scenario testing. We introduce Controllable Road Marking Generation, which synthesizes a missing center-region marking layout from a drivable-area mask, optional outer-ring markings, and a textual description. Our
benchmark uses deterministic, metadata-derived
prompts and three output channels: lane dividers, road dividers, and pedestrian crossings. We develop a conditional bird's-eye-view (BEV) pipeline that combines (i) a text-conditioned latent rectified-flow DiT trained with a
topology-aware auxiliary loss, (ii) Gaussian-blurred training targets that stabilize learning of thin, sparse markings, and (iii) Structured Gaussian Render (SGR), a training-free post-process that recovers crisp divider geometry by extracting polylines, fitting cubic Bézier curves, and re-rendering them as anisotropic super-Gaussian primitives. On 4,597 Argoverse~2 test tiles, our system achieves Buffered F1 of 80.8 and clDice of 50.2, compared with 38.8 and 24.6 for an adapted state-of-the-art mask-refinement baseline. On Waymo
dataset, it yields 88.0 Buffered F1 and 66.2 clDice. Component ablations show complementary connectivity gains from
topology-aware supervision and SGR. Text-editing experiments reveal that stronger guidance improves edit success but also increases changes to non-target structures. We see this framework as a step toward simulation-ready road-marking variation, automated map completion, and early-stage infrastructure design exploration.
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All authors Zhiyu Cai, Yufan Zhang, Ruichen Tan, Zengxiang Lei, Satish Ukkusuri
Computer Vision
A Three-Dimensional Reverse-Projection Method for Sparse Point Cloud Completion and Its Application to High-Speed Train Nose Reconstruction
Xiaozhen Ma, Zhao Tang, Hanbin Lai et al. (+2 more)
Holes in
LiDAR scans of environments with glass windows remain an unresolved problem in three-dimensional reconstruction. This study presents a pipeline for point cloud acquisition, filtering, completion, and surface reconstruction to address sparse sampling and missing window regions in scans of a high-speed train nose. FAST-LIVO2 provides the initial point cloud through multisensor odometry and mapping, and moving least squares (MLS) smooths the observations. We then introduce three-axis projection-based subdivision and interpolation with reverse hole boundary identification, referred to as three-axis reverse completion. The method interpolates missing regions from observations around each hole. Greedy projection triangulation, Poisson surface reconstruction, and a Marching Cubes-based pipeline generate meshes from the completed point cloud. Experiments on a proportionally scaled display model of a high-speed train nose show that the proposed method fills missing point cloud regions around the glass windows. Under the evaluation setting used in this study, greedy projection triangulation yields lower
geometric distance errors than the other two reconstruction pipelines. The pipeline supports non-contact digital modeling of train nose geometry and provides a practical approach to reconstructing objects with glass windows.
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All authors Xiaozhen Ma, Zhao Tang, Hanbin Lai, Ruiqi Chen, Jin Jin
Computer Vision
Vision-enabled detection of safety helmet compliance in construction zones
Tri Nhut Do* and Ba Loc Pham
In the rapidly evolving field of construction management, worker safety remains a top priority. This paper introduces an innovative
vision-based system for real-time detection of helmet compliance, specifically designed for construction sites, utilizing advanced computer
vision techniques and machine learning
algorithms within the YOLO (you only look once) framework. Our system leverages high-resolution
video feeds from strategically positioned cameras to monitor adherence to safety regulations regarding helmet usage. By employing deep learning methodologies, the system effectively identifies individuals not wearing helmets, thereby significantly mitigating the risk of head injuries among workers. Our training and validation results revealed an impressive precision exceeding 97% at mAP@0.5 for both helmeted and non-helmeted individuals. Furthermore, our experiments demonstrate exceptional detection accuracy, demonstrating the system's resilience under varying lighting conditions and diverse worker movements. The consistent decrease in loss and improvement in metrics throughout training validates the effectiveness of the YOLOv8 model in enhancing recognition performance. The implications of this research extend beyond mere regulatory compliance, opening avenues for innovative applications in occupational safety management. This study highlights the critical role of technology in protecting lives and lays the groundwork for future advancements in smart construction environments.
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Computer Vision
A new design of a fall detection system integrating landmark identification and deep learning techniques
Tri Nhut Do and Thi Thuy Le
This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliability. By utilizing advanced computer
vision techniques, such as Media Pipe for spatial recognition, the system effectively differentiates between routine movements and actual falls. The integration of landmarks with a deep learning prediction
algorithm minimizes false alarms, ensuring timely responses to genuine falls. Comprehensive experimentation underscores the system's versatility across various scenarios, emphasizing its potential to improve safety and independence for older adults. The training process demonstrates a steady increase in accuracy, stabilizing by the 40th cycle, while error rates decline significantly during the initial cycles. Real-time experiments, involving both male and female participants aged 8 to 50, recorded a remarkable 95% detection rate of falls, demonstrating the system's effectiveness and promising future applications in elder care and smart health monitoring environments.
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Computer Vision
Robust Local Optimization Done Right
James Pritts and Kevin Köser
RANSAC scoring and local optimization (LO) impose different robustness requirements, motivating the separation of hypothesis selection from refinement. We systematically isolate the effects of robust-loss shape, incorrectly specified inlier scales, and optimization strategy on essential
matrix, fundamental
matrix, and homography estimation. A profile-marginal score marginalizes the nuisance inlier scale and selects an inlier partition, from which we estimate the scale that sets the LO loss width; this makes LO robust to an inlier scale specified too large, whereas one specified too small degrades selection itself. Refinement needs gradient from correspondences the seed currently rejects: optimizers that reweight from current residuals stay pinned to their seed, whereas methods with broad basins recover strongly perturbed seeds yet degrade accurate score-selected hypotheses, so basin size alone is insufficient to assess RANSAC LO. Joint half-quadratic optimization balances the two and is the most consistent strategy across model classes. An optimizer matched to the profile-marginal score, which never decreases it, does not reach the best accuracy, challenging the prescription that scoring and refinement objectives should match. Composed from these findings, our RANSAC reduces the median essential-
matrix pose error of a state-of-the-art RANSAC on PhotoTourism from 2.23 degrees to 1.58 degrees with a correctly specified inlier scale and from 38 degrees to 6.2 degrees when it is grossly misspecified (128x too large).
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Computer Vision
HLA-WM: Hybrid Linear Attention for Long-Horizon Video World Models
Zhuokun Chen, Feng Chen, Xi Lin et al. (+4 more)
Long-horizon
video world models require persistent
memory to preserve scene consistency over extended rollouts. Softmax
attention retains the full generation history through a growing KV cache, whereas recurrent linear
attention compresses history into fixed-size states with substantially lower
memory cost. However, we identify severe long-range forgetting in Gated DeltaNet (GDN), where information from distant but relevant scenes is progressively attenuated by subsequent state updates. To address this limitation, we propose HLA-WM, a training-free hybrid linear-
attention framework that combines coarse-grained geometry-guided
retrieval with fine-grained recurrent linear-state computation. HLA-WM exploits the affine structure of GDN to cache compact chunk-wise transition summaries, retrieve scene-relevant historical chunks using camera geometry, and recompose them into query-specific recurrent states. On the $60$-second SANA-WM-Bench, HLA-WM improves all six aggregate revisit-consistency and camera-control metrics of the base autoregressive generator without additional training, including a $0.74$ dB PSNR gain and a $28.5\%$ reduction in rotation error. The improvements persist after downstream refinement and generalize to MBench-A, where HLA-WM consistently improves all three revisit-consistency metrics across all four subsets and all evaluated inference modes over $547$ samples. At a $60$-second context, HLA-WM reduces historical-state
memory by $12\times$ relative to full KV caching while incurring at most a $1.6\%$ reduction in inference throughput. These results demonstrate that selectively addressable recurrent
memory can improve long-range scene recall while preserving the efficiency advantages of GDN. Project page: https://caesarhhh.github.io/hla-wm/
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All authors Zhuokun Chen, Feng Chen, Xi Lin, Xiyu Wu, Jiahao He, Jianfei Cai, Bohan Zhuang
Computer Vision
T-JEPA: A Temporal Joint-Embedding Predictive Architecture for Learning Better Remote Sensing Representations
Bowen Peng, Li Liu, Yongxiang Liu et al. (+3 more)
Earth observation (EO) data provide rich temporal supervision, yet existing remote sensing foundation models mainly exploit sequential observations through imposing predefined pairwise relations or aggregating holistic reconstruction context. We seek to further exploit the sparse and nonuniform temporal sampling inherent in EO sequences as supervisory signals. To this end, we propose T-JEPA, a temporal joint-
embedding predictive architecture that learns time-gap-conditioned latent transitions. A shared single-frame encoder processes each observation, while a temporal predictor estimates the complete target latent field from a masked source latent representation and the actual elapsed time. Across multiple temporal intervals, these predictive constraints organize observed states into structured latent trajectories. Asymmetric metadata injection mitigates shortcut learning, and direct supervision across multiple temporal scales proves more effective than recursively rolling out intermediate states. In parallel, masked pixel reconstruction provides complementary supervision for preserving spatial details. Under matched pre-training data and throughput, T-JEPA achieves leading transfer performance on both static and temporal tasks. Analyses further reveal that T-JEPA learns representations with time-gap-dependent transition predictability and coherent latent dynamics, while maintaining strong cross-period consistency, representation diversity, and semantic discriminability.
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All authors Bowen Peng, Li Liu, Yongxiang Liu, Weijie Li, Jie Zhou, Zhen Liu
Computer Vision
Rotated, but How Far? Diagnosing and Improving Object-Rotation Reasoning in VLMs
Zhaochen Wang, Yujun Cai, Huangbo Zou et al. (+4 more)
Vision-
language models (VLMs) can detect that an object has rotated across views, but cannot reliably tell by how much. We introduce OR-Bench, a fine-grained
benchmark for object-rotation
reasoning with eight tasks covering rotation detection, rotation magnitude estimation, and multi-view rotation
reasoning. Across 12 VLMs, the gap is stark: the strongest models approach 100% accuracy on detection, yet even coarse magnitude estimation is near chance. When asked for exact angles, models place 91.8–100% of their predictions on just $0^\circ$, $90^\circ$, and $180^\circ$, a failure we term canonical-angle collapse. This collapse persists even without visual input. Representation probing shows that missing information is only part of the explanation. Although rotation information becomes less recoverable at finer granularity, substantial coarse-grained information remains, and a simple linear probe outperforms the models' generated answers. This suggests that VLMs underuse rotation information they already encode. We therefore propose RotationCue, a lightweight decoder that recovers coarse rotation information from the VLM's own frozen representations and feeds it back to the model as intermediate textual context. Across three VLMs, RotationCue improves every model–task combination on OR-Bench, raising macro-average accuracy by 7.9–12.6 points while preserving general capabilities.
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All authors Zhaochen Wang, Yujun Cai, Huangbo Zou, Hower Yang, Naipeng Dong, Miao Xu, Haibin Ling
Computer Vision · Cross-listed from cs.LG
From Pixels, Without Pre-training: Joint Generative and Self-Supervised Representation Learning in One Model
Vicente Balmaseda, Ching-Long Lin and Tianbao Yang
Strong image generation models are conditioned on class labels, aligned to frozen pretrained encoders, or built on separately trained autoencoders. While effective, generation then depends on supervision or pretraining: labels must be annotated, and encoders or autoencoders pretrained for the target domain. We study joint generative and self-supervised representation learning in a single model, enabling self-conditioned generation without labels or pretrained models. This is challenging because the objectives are mismatched: contrastive learning consumes clean augmented views and favors coarse, invariant semantics, while flow matching consumes noisy images and must preserve the fine detail and spatial layout that contrastive learning discards. We propose SCION (Self-conditioned Generation on Self-supervised representation), whose core is a single pixel-space encoder conditioned on the flow timestep and an
embedding. For representation learning, this conditioning
embedding is a learned global vector shared across images, with the encoder's [CLS]
token yielding the semantic representation trained by the contrastive loss. For generative training, the conditioning
embedding is the image's own [CLS] representation, while patch
tokens pass through a decoder to predict the image. To sample without a reference image at inference, we jointly learn a prior over the
embedding. Gradient-norm balancing and stop-gradient mechanisms enable joint optimization in one run. SCION is self-supervised and self-contained, with no labels or pretrained models. On ImageNet 256x256, with the JiT-B recipe and no representation guidance, SCION reaches 8.92 FID, surpassing class-unconditional iREPA, which aligns to pretrained DINOv2 (46.44), and RCG, which conditions on it (14.27). With JiT-L, SCION achieves 5.89 FID without guidance and 3.47 with representation guidance, outperforming RCG with the ADM recipe (6.24).
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Computer Vision
Difference Feature Map Distillation: Transferring Inter-Sample Relational Knowledge Towards Efficient Transformer-Based Tracking
Zhicheng Ding, Xinyu Chu and Qing Tian
In autonomous driving perception, visual object tracking systems must satisfy stringent latency and power constraints while remaining robust in complex and dynamic environments. Although
transformer-based trackers achieve state-of-the-art accuracy, their substantial computational and
memory overheads hinder deployment on real-time, resource-constrained platforms. To move toward this goal, we propose Difference Feature Map Knowledge
Distillation (DFM-KD), a novel relational
distillation framework tailored for
transformer-based visual object tracking. Unlike conventional feature
distillation methods that minimize point-wise discrepancies (e.g., mean squared error) between teacher and student feature representations, DFM-KD transfers knowledge through inter-sample feature differences, explicitly aligning the relational structure of the feature space. By distilling how the teacher models appearance variation and consistency across samples, rather than enforcing similarity in absolute activations, DFM-KD enables the student to better capture the structural dynamics of visual changes within a batch. As a result, the distilled model exhibits enhanced feature robustness and improved tracking performance. Extensive experiments demonstrate that DFM-KD consistently outperforms conventional feature-level
distillation methods in both tracking precision and success rates.
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Computer Vision · Cross-listed from cs.RO
Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing
Ashik E Rasul and Hyung-Jin Yoon
In GPS-denied or cluttered urban environments,
vision-based landing is essential for reliable
UAV missions. Real-world landing sites are often unstructured and highly variable, requiring strong generalization by the perception system. Deep Neural Networks (DNNs) trained with synthetic data augmentation offer a scalable solution for learning landing-site features across diverse vehicle and environmental states. However, computationally expensive DNN retraining, along with challenging performance validation via test flights, limits exhaustive model fine-tuning and necessitates an optimized retraining pipeline. In this work, we deploy a
Bayesian data augmentation framework integrated with a photorealistic simulator featuring high-fidelity vehicle dynamics to iteratively retrain the helipad detector DNN, maximizing landing performance as the objective function. We validate our framework with experiments in a photorealistic simulator under different environmental conditions and vehicle states, demonstrating improved landing performance and tighter confidence intervals on predicted landing outcomes.
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Computer Vision
StageVLN: Spatial and Trajectory Auxiliary Guidance for Efficient Vision-Language Navigation
Anh Dao, Quan-Dung Pham, Le Danh Vinh et al. (+6 more)
Vision-and-
Language Navigation (VLN) policies increasingly benefit from strong semantic priors provided by large
vision-
language models (VLMs). However, standard action supervision does not explicitly encourage intermediate representations to preserve scene geometry, relative orientation, or global episode progress. Incorporating depth
estimators, explicit maps, point clouds, or geometry foundation models at inference can provide such structure but introduces additional computation,
memory overhead, and architectural dependence during deployment. We introduce StageVLN, a training framework that shapes navigation representations through privileged spatial and trajectory guidance while preserving the original inference pathway. A frozen geometry foundation model provides multi-level spatial guidance to hierarchical navigator states, while relative-heading and expert-route progress objectives provide complementary trajectory-state supervision. All auxiliary components are used only during training and removed at deployment. On R2R-CE validation-unseen, StageVLN achieves 56.3% SR and 51.4% SPL with a 4B-parameter backbone, without an additional geometry encoder at inference. On RxR-CE, it achieves 54.3% SR without additional navigation training data or a geometry encoder at inference.
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All authors Anh Dao, Quan-Dung Pham, Le Danh Vinh, The Anh Nguyen, Nguyen Viet Tri Pham, Yiyu Chen, Pham Tuyen Le, Van-Truong Nguyen, Quan Nguyen
Computer Vision · Cross-listed from cs.AI
Visual Grounding Safety in Vision-Language Models
Erfan Shayegani, Kundan Krishna, Yue Dong et al. (+3 more)
Vision-
language models (VLMs) are increasingly trained to generate structured outputs like points and bounding boxes that downstream interfaces,
agents, and
robots can act on, yet safety
alignment of this output channel has not been systematically analyzed. We study visual grounding safety by repurposing three safety
benchmarks spanning direct harm (VLSU), social bias (BBQ-V), and situational safety (Asimov-2.0) into 15,401 matched pairs of harmful requests that differ only in the requested output: a free-text answer (VQA) or a grounding (point or bounding box). Across five VLMs, models that refuse a harmful request posed as a question often comply when the same request asks for a grounding: averaged over models, grounding refusal trails VQA refusal by 31-59 percentage points, depending on the domain, and safety system
prompts do not close this gap. We propose a fine-tuning approach that combines grounding-form refusals with capability grounding data and self-distilled benign data to counter over-refusal. For Qwen3-VL-8B and VisionReasoner-7B, it improves grounding refusal by 77-95 percentage points on VLSU and BBQ-V and by 64-85 points on the held-out Asimov-2.0 domain, while also improving VQA refusal, preserving grounding capability, and keeping over-refusal limited. Representation analysis shows that fine-tuning moves harmful requests toward each model's refusal direction, most strongly for grounding, while leaving benign requests near the harmless reference.
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All authors Erfan Shayegani, Kundan Krishna, Yue Dong, Nael Abu-Ghazaleh, Leon Gatys, Shruti Palaskar