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新论文 · v1arXiv:2611.04409

LCM-UNet: Scale-Selective Mamba with Reparameterizable Multi-Basis Local Modeling for Lightweight Skin Lesion Segmentation

Md Shihabul Islam Shovo, Nishi Kanta Paul, Dip Kumar Saha, Kishor Morol

原文摘要

Accurate lesion segmentation underpins automated dermoscopic analysis: masks enable measurements of asymmetry, border and size. Deployment in primary care or on mobile devices requires a compact network. Existing ultra-light Mamba U-Nets offer global context, but global-modeling cost depends on each Mamba block's resolution, while their local branches rely on a single square kernel. We introduce LCM-UNet, a 33,474-parameter U-shaped network that fuses every skip at its native resolution, restricts bidirectional Mamba to stages at one-sixteenth of the input resolution or coarser, and uses local convolutions at finer scales. A multi-basis reparameterizable adapter augments each local branch during training with dilated, horizontal and vertical depthwise kernels, then folds them into the host kernel before inference. Each skip connection also learns a stage-specific gain. Nine networks were retrained on the official ISIC 2017 and ISIC 2018 partitions under a harmonized protocol, with PH$^2$ reserved for external validation and ablations run on a development split. LCM-UNet achieved Dice scores of 88.62%, 89.73% and 90.46% on ISIC 2017, ISIC 2018 and PH$^2$, respectively, ranking first on both ISIC test sets and second on PH$^2$ among the compared models. In the native-resolution host, removing Mamba from the one-eighth-resolution decoder stage eliminated 63% of Mamba operations, corresponding to 5.3% of all operations, with a Dice change of $-0.03$ percentage points. The adapter improved Dice by 0.48 percentage points over a single host kernel without adding deployed parameters or operations. With latency so far measured only on a GPU, LCM-UNet is a candidate for resource-constrained deployment.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04405

DR-LiteNet: A Lightweight Explainable Hybrid CNN Framework for Imbalanced Diabetic Retinopathy Grading via Adaptive SMOTE Fusion

Nishi Kanta Paul, Md Shihabul Islam Shovo, Audrija Chowdhury, Israt Jahan Esha

原文摘要

Automated diabetic retinopathy (DR) grading from colour fundus photographs is held back by two compounding challenges: severe class imbalance across five severity grades and the heavy computational cost of large scale architectures in resource constrained clinical settings. We introduce DR-LiteNet, a dual branch hybrid convolutional neural network that tackles both problems at once. The CNN branch extracts 1,280 dimensional deep features through EfficientNetB0, while the handcrafted (HC) branch produces a compact 51 dimensional lesion descriptor capturing vessel density, exudate intensity, texture entropy, and HSV colour histograms. Concatenating both branches yields a 1,331-d hybrid embedding, on which Adaptive SMOTE (ADASYN) is applied during training to synthesise minority class feature vectors, correcting distributional skew without touching the image space. A two phase training protocol first warms up the classification head on balanced features, then fine tunes the full network end to end. Grad CAM saliency maps deliver post-hoc clinical explainability. Tested on APTOS 2019 under five fold stratified cross validation, DR-LiteNet reaches 88.77% +/- 0.61 accuracy, a quadratic weighted kappa (QWK) of 0.884 +/- 0.010, and a macro F1 of 0.792 +/- 0.008 with only 4.69 M parameters beating ResNet50 (23.5 M) by DeltaQWK = 0.022 while staying practical on a single Google Colab T4 GPU. The source code of this project is available at: https://github.com/Nishi-Kanta-Paul/DR-LiteNet.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.10080

BEVTraj: Map-Free Multimodal Trajectory Prediction with Sensor-Derived BEV Context and Sparse Goal Reasoning

Minsang Kong, Myeongjun Kim, Sang Gu Kang, Hejiu Lu, Yupeng Zhong, Sang Hun Lee

原文摘要

Trajectory prediction is a key task in autonomous driving, where a model must forecast plausible future motions of surrounding agents from dynamic histories and scene context. Many recent methods use high-definition (HD) maps to provide explicit road topology and semantic constraints, but such maps are costly to maintain, geographically limited, and difficult to update in changing environments. This paper presents Bird's-Eye View Trajectory Prediction (BEVTraj), a map-free trajectory prediction framework that directly uses BEV features constructed from raw multi-sensor data. Since sensor-derived BEV features are dense and only implicitly structured, BEVTraj employs deformable attention to selectively aggregate agent-relevant spatial context from sparse locations rather than processing the entire BEV grid uniformly. In addition, a Sparse Goal Candidate Proposal module combines overlapping mode-wise anchor clusters with attention-based goal prediction to generate compact, scene-adaptive goal hypotheses. These goals are decoded and iteratively refined into multimodal trajectories, while trajectory-aware mode scoring and temporal consistency modeling improve confidence estimation and suppress abrupt, physically implausible refinement updates. Experiments on nuScenes and Argoverse 2 Sensor show that BEVTraj achieves competitive results under a map-free setting without using pre-built HD maps, while ablation studies and qualitative analyses illustrate the roles of BEV context aggregation and sparse goal reasoning. The source code is available at https://github.com/Kongminsang/bevtraj.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04397

Auditing Targetless Camera-LiDAR Extrinsic Correction before Recursive Use

Bo Ma, Weiqi Yan

原文摘要

Camera-LiDAR fusion repeatedly uses an extrinsic transform in projection, association, and recursive state estimation. We study whether a targetless correction should be admitted to a downstream consumer only after separate audits of correction quality, confidence ranking, and recursive risk. Our log-disjoint protocol evaluates these stages under synthetic extrinsic drift on Argoverse 2 and treats cross-dataset and odometry experiments as bounded diagnostics. On 27 test-split logs, single-frame Canny alignment is harmful in the mild tier. Multi-frame alignment improves severe-tier rotation by 2.52 degrees but worsens translation by 0.177 m; its declared joint error-magnitude improvement is also negative (-0.158, 95% CI [-0.223, -0.098]), so the rotation gain is not a six-degree-of-freedom correction success. A zero-injection check moves away from the true extrinsic, and solver-disagreement AUROC on selected splits does not yield a validated nonzero-coverage risk-coverage operating point. In a 15-log final system role, the locked conservative policy admits no candidate and equals Raw, whereas Always-correct increases trajectory error. These results support an auditable separation of correction, confidence, and recursive contamination; they do not establish a deployable calibration gate, positive odometry or SLAM improvement, map consistency, or closed-loop safety.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04389

Spin Spherical Fourier Neural Operators: Efficient Equivariant Learning for Vector and Spin Fields on the Sphere

Kevin Mulder, Matthew A. Price, Eirini Leftaki, Jason D. McEwen

原文摘要

Many fields of physical interest on the sphere, such as wind, the polarisation of the cosmic microwave background and the shear of weak gravitational lensing, are spin fields, whose values change phase when the local frame rotates. We extend spherical Fourier neural operators (SFNOs) to fields of any spin, through lifted spherical convolutions that are exactly rotationally equivariant. Lifting spin fields to Mackey functions on the rotation group provides the conceptual framework, while computation is performed exactly and efficiently through their harmonic representation and by appealing to sampling theorems on the sphere and rotation group. We construct spin SFNOs (S$^2$FNOs) from these building blocks. We demonstrate empirically that rotational equivariance holds to machine precision for these layers. We achieve state-of-the-art performance on existing benchmarks, and introduce new benchmarks on which spin-aware models outperform their scalar counterparts.

cs.CV · astro-ph.IM · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04386

StepX-OPD: Rethinking Multi-Teacher On-Policy Distillation in Diffusion Models

Chenru Wang, Xuanyang Zhang, Yucheng Han, Wenbo Nie, Wei Cheng, Xianfang Zeng, Gang YU, Chi Zhang

原文摘要

A deployable text-to-image diffusion model must hold several capabilities at once, such as instruction following, text rendering and human preference, and must sample in a few steps. Capabilities and few-step sampling are built in separate stages: a full-step model first consolidates capabilities through multi-teacher on-policy distillation, then serves as a fixed teacher for step distillation. Every full-step model we evaluate scores lower once compressed, and the second stage cannot begin until the first finishes. We introduce StepX-OPD, which merges the two stages: one student acquires every capability while learning to sample in a few steps, so frozen specialists supervise it on the states its own rollout visits rather than on a longer trajectory it never runs. Because a decomposition of the rollout's KL weights velocity mismatch most heavily near the clean end, we also regress the student's velocity on the low-noise part of the rollout. Separately, we find that capability integration makes models write prompt words into images on prompts that never asked for text, and we give a variant that lowers it. We evaluate on SD3.5-Medium and FLUX.1-dev across instruction following, text rendering, image quality and human preference. On both architectures, StepX-OPD attains the highest normalized average score among the four-step methods compared and exceeds the forty-step Flow-OPD model, and on SD3.5-Medium it trains with about 37% fewer GPU-hours than sequential DiffusionOPD followed by step distillation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.00643

Evaluation of Visual Processing Should Be Human-Centered, Not Metric-Centered

Fanghua Yu, Jinfan Hu, Zhiyuan You, Xiang Yin, Hongyu An, Xinqi Lin, Chao Dong, Jinjin Gu

原文摘要

This position paper argues that the evaluation of modern visual processing systems should no longer be driven primarily by single-metric image quality assessment benchmarks, particularly in the era of generative and perception-oriented methods. Image restoration exemplifies this divergence: while objective IQA metrics enable reproducible, scalable evaluation, they have increasingly drifted apart from human perception and user preferences. We contend that this mismatch risks constraining innovation and misguiding research progress across visual processing tasks. Rather than rejecting metrics altogether, this paper calls for a rebalancing of evaluation paradigms, advocating a more human-centered, context-aware, and fine-grained approach to assessing the visual models' outcomes.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.25489

Agentic AI in medicine: architectures, applications, evaluation, and challenges for clinical translation

Wanshu Fan, Zheng Tong, Yang Liu, Jing Qin, Zhongbin Han, Haifan Gong, Congyu Liao, Xiaofeng Liu 等 9 位作者

原文摘要

Large language models and multimodal foundation models are enabling medical artificial intelligence (AI) systems to move beyond isolated prediction and undertake multistep clinical tasks that require planning, tool use, memory, iterative correction, and coordination among specialized agents. However, the scope of agentic AI in medicine remains unsettled, and current evaluation practices are not yet aligned with the requirements of clinical use. We conducted a scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration. The included studies describe single agents that use external tools, workflows supported by retrieval and external knowledge, multimodal agents, and multi-agent systems applied to medical question answering, image interpretation, electronic health record analysis, drug safety, and clinical trial prediction. The evidence base remains dominated by public benchmarks, simulated settings, retrospective datasets, and small-scale expert evaluation. Process reliability, evidence traceability, uncertainty, safety, workflow impact, and external validity are evaluated less consistently. Clinical translation will depend on clearer definitions, reproducible evaluation, auditable oversight, interoperable system design, and prospective validation in real-world clinical workflows.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04379

Understanding Backdoor Attacks in Scene Graph Generation via Global Context Pathway

Xiaoguang Chang, Tuo Chen, Chengze Jiang, Teng Wang, Changyin Sun

原文摘要

Scene Graph Generation (SGG) produces structured scene representations, yet its training-time backdoor risk remains unexplored. Unlike classification, SGG exposes a pair-level attack surface that enables selective corruption of relations involving a designated object category. However, because SGG largely relies on localized evidence, globally placed triggers may face difficulty in manipulating such predictions. Recent SGG architectures increasingly incorporate global context to improve scene modeling, which may in turn facilitate such attacks. In this paper, we systematically study this backdoor risk and investigate the role of the global context pathway in backdoor behavior. Concretely, we first instantiate this attack with an effective method, and propose a three-axis evaluation protocol to characterize backdoor behavior. Experiments on Visual Genome with representative open-vocabulary and closed-set SGG models demonstrate the effectiveness of the attack. Mechanistic analyses reveal that the explicit global context pathway serves as a major carrier of the backdoor effect across the evaluated SGG models. In particular, replacing the poisoned global context representation with its clean counterpart during inference sharply suppresses the attack, yielding 100% and 97% reductions in attack success for the OvSGTR family and RelationFormer, respectively. Moreover, training-time interventions in the OvSGTR family show that suppressing trigger signals in this pathway either reduces attack success or substantially degrades utility. Together, these findings suggest that explicit global context designs may pose a backdoor risk in SGG architectures.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04374

IGSW: Multi-Weather Urban Scene Reconstruction with Intrinsic-Decoupled Gaussian Splatting

Zhongquan Wang, Long Cao, Peijia Xie, Senlei Ma, Jie Li, Guofa Li

原文摘要

High-fidelity weather editing of 4D scenes can support end-to-end training and closed-loop simulation for autonomous driving. Combining generative image editing with dynamic Gaussian representations enables multi-weather scene reconstruction, but weather-edited images often contain structural distortions. Directly fitting their appearance may propagate errors from teacher images into the reconstructed 4D scene, compromising the original scene structure and temporal continuity. To address these challenges, we propose IGSW, a two-stage framework that combines intrinsic-decoupled 4D Gaussian scene modeling with structure-preserving weather refinement. In Stage I, background Gaussian appearance is decomposed into weather-shared albedo and normals and weather-specific irradiance and residuals. Weather teacher images generated by a diffusion-based image editing model and intrinsic maps estimated by a diffusion-based inverse renderer provide feature-level soft supervision for stable structure and illumination changes. In Stage II, a structure-preservation gate combines semantic conflicts with cross-weather intrinsic discrepancies to suppress unreliable teacher regions. Per-Gaussian weather transport variables and target-weather branches then strengthen the desired weather appearance over the intrinsic-decoupled scene. Experiments on nuScenes and Waymo show that IGSW achieves the highest content similarity and semantic consistency and the lowest temporal error among the compared methods, while retaining pronounced weather-dependent illumination and atmosphere. The project page is available at https://akaxi6.github.io/IGSW/ .

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04368

RAFT: Reasoning-Aligned Fine-Tuning Improves Robustness of VLA Post-Training

Shresth Grover, Andrey Kolobov, Dhruv Shah

原文摘要

Adapting a generalist vision-language-action (VLA) policy to a target task with limited demonstrations can improve nominal task success while reducing robustness to variations in appearance, objects, and instructions. We introduce reasoning-aligned fine-tuning (RAFT), a two-stage post-training procedure: RAFT first trains low-rank adapters on task-relevant reasoning annotations, then freezes them while fine-tuning the policy on action demonstrations. On a physical DROID robot, RAFT achieves $75.0\%$ out-of-distribution success, compared with $27.9\%$ for plain fine-tuning; on a multi-stage bimanual YAM task, it reaches $70.0\%$ mean success, compared with $36.0\%$ for the strongest baseline. RAFT also improves success under visual, object, and instruction shifts in simulated pick-and-place and open-drawer tasks. Compared with plain fine-tuning, RAFT policies place more attention on annotated task regions and yield higher linear-probe accuracy for object identity in later layers. A broader reasoning-data mixture further improves downstream success across demonstration budgets.

cs.RO · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.25242

Medical world models in healthcare: capabilities, evidence, and trustworthy clinical translation

Wanshu Fan, Zhongxiu Cong, Zhaoyan Chen, Zhuanfeng Jin, Qi Ai, Haifan Gong, Congyu Liao, Xiaofeng Liu 等 10 位作者

原文摘要

Medical world models offer a framework for extending medical artificial intelligence beyond static prediction by representing evolving patient states and modelling how they change over time and in response to clinical interventions. This Review uses structured narrative synthesis and reproducible evidence mapping to define conceptual boundaries, technical foundations, application domains, and clinical evidence requirements. We screened 1,455 unique records and assembled a corpus of 300 sources, including 14 studies that met a strict empirical definition of a medical world model. The field is organized around four capabilities: patient state representation, temporal dynamics modelling, intervention-conditioned simulation, and clinician-supervised planning. Evidence spans medical imaging, longitudinal electronic health records, treatment response modelling, physiological and multimodal state modelling, ultrasound and surgical interaction, and population and health-system simulation; clinical digital twins are treated as a cross-cutting integration framework. Studies provide early evidence of technical feasibility for trajectory forecasting and comparison of candidate interventions, but most remain retrospective, task-specific, or preclinical. We distinguish functional capability from clinical evidence maturity and specify evidence needed to support progressively stronger clinical claims, from temporal forecasting to intervention comparison and planning-oriented decision support. These requirements include precise intervention representations, robust causal and mechanistic grounding, calibrated trajectory-level uncertainty, safety-constrained planning, external validation, and prospective multicentre evaluation against clinically meaningful endpoints. This framework aims to distinguish technically plausible simulation from clinically supportable prediction and decision support.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21364

CoDeFont: One-Shot Font Generation via Multi-Scale Factor-Specific Representation Learning

Rejoy Chakraborty, Prasun Roy, Saumik Bhattacharya, Umapada Pal

原文摘要

One-shot Font Generation (OFG) aims to synthesize a target glyph by transferring the stylistic characteristics of a single reference glyph while preserving the structure of the target character. However, accurately capturing font style from a single reference image remains challenging due to the limited stylistic information available in a single glyph, and this challenge is further amplified for complex scripts. We propose CoDeFont, a script-agnostic OFG framework that addresses this challenge through multi-level style-content factorization and progressive style-content reconciliation. CoDeFont independently aggregates complementary style and structural information across multiple spatial levels to obtain factor-specific representations, while a contrastive objective explicitly encourages separation between style and content embeddings. During reconstruction, the disentangled representations are progressively injected through multi-level style conditioning, enabling stylistic information to be incorporated at different decoding resolutions while preserving the underlying glyph structure. We evaluate CoDeFont on structurally distinct Bengali and Latin scripts across seen and unseen font styles. Extensive quantitative and qualitative analyses demonstrate consistent improvements over existing state-of-the-art methods, highlighting the effectiveness of the proposed framework for OFG across diverse writing systems.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04358

EndoS3:Endoscopic Semantic Splatting SLAM

Mona Sheikh Zeinoddin, Benjamin Wright, Zafer Tandogdu, Greg Shaw, Evangelos Mazomenos, Danail Stoyanov

原文摘要

Purpose: Despite advances in 3D surgical scene understanding, semantic simultaneous localisation and mapping (SLAM) remains under-explored for two key reasons. First, it requires solving three challenging tasks in parallel: camera pose tracking, high-fidelity deformable reconstruction, and semantic mapping, making progress particularly demanding. Second, the scarcity of datasets providing the necessary ground truth to evaluate all three tasks comprehensively under conditions that closely reflect real surgical procedures environments. Method: We propose EndoS3, a Gaussian Splatting-based semantic SLAM framework that uses instance masks to guide all three tasks. An instance-level motion decomposition module separates static and dynamic instances by evaluating residual flow after compensating for camera motion, accounting for flow noise and comparing motion across neighbouring instances. The camera is tracked against the static Gaussians, while an instance-aware warp field gives each dynamic instance its own control nodes and deformation network. A semantic node allocation module redistributes a fixed node budget across instances according to their size and non-rigidity. We also introduce CRCD++, an ex-vivo Cholecystectomy dataset with RGB-D frames, camera pose and instance annotations for full semantic SLAM evaluation. Results: EndoS3 surpasses all state-of-the-art general-purpose and surgical semantic SLAM methods in tracking, reconstruction and semantic mapping on the CRCD++ and SemSup datasets. Conclusion: Semantic-aware motion and deformation modelling is vital to surgical semantic SLAM. EndoS3 and CRCD++ offer a method and benchmark for improved geometric and semantic understanding in endoscopy.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04352

ReSMap: Recasting Satellite Priors for Robust and Accurate Online HD Map Construction

Kyungmin Kim, Sumin Lee, Sungoh Jeong, DoHyun Lim, Seonghyun Park, Soonmin Hwang

原文摘要

Despite recent advances in camera-based online HD map construction, these systems remain susceptible to sensor failures due to their reliance on onboard cameras. In contrast, satellite images remain available independently of real-time sensing conditions as they are cached offline. Existing methods utilizing satellite images, however, primarily leverage them under nominal conditions, without explicitly addressing them as redundancy when a sensor degrades. To address this gap, we propose ReSMap, a redundancy-aware camera-satellite fusion framework for robust online HD map construction. Our framework explicitly leverages satellite images as a complementary source to improve robustness against camera failures. A Predict-then-Fuse (PTF) module fuses camera and satellite BEV features by using each branch's own prediction confidence as a per-pixel reliability weight, with no learned router. An Object-Centric Cooperative Multi-modal Query (OCMQ) decoder then extends this per-modality separation to the query level: each modality cross-attends only to its own BEV, and per-modality updates are merged through a learned per-token gate. Our model achieves state-of-the-art performance on nuScenes across all splits and range settings. More importantly, it also sets the new state-of-the-art under camera-failure scenarios, demonstrating that satellite images serve not only as a strong prior but as practical redundancy for robust online HD mapping.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04337

Mitigating Spatial Blindness in Multiple Instance Learning for Whole-Slide Image Analysis

Xiangyu Li, Jincheng Li, Xing Xie, Bin Guan, Shaohua Kevin Zhou, Rui Yan, Ran Su

原文摘要

Whole-slide multiple instance learning can predict diagnostic and prognostic labels from tissue composition while making limited use of spatial organization. Providing coordinates or a graph does not establish that a trained predictor uses tissue neighborhoods. We examine this gap as spatial blindness and hypothesize that early composition learning can weaken supervision of a spatial pathway. We introduce ResTopoMIL, which freezes a learned composition predictor before training a graph residual with a coordinate-reassignment separation loss. Permutation and information analyses distinguish architectural symmetry, available spatial information and the proposed optimization mechanism. On a controlled spatial task, staged training achieved an AUC of 0.987, compared with 0.684 for the same jointly trained architecture. Performance remained high on a composition-only task. Across four WSI classification and five survival benchmarks, ResTopoMIL improved mean primary performance on eight tasks; four improvements remained significant after Holm-adjusted patient-cluster tests. Coordinate-free residual controls recovered some of the gains, while neighborhood destruction reduced performance close to the composition anchor. Neighborhood-preserving transformations and anatomy-stratified perturbations further characterized this dependence. These results support fixing a composition reference as a practical intervention for learning spatial corrections, while retaining additional appearance capacity as a contributing explanation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02044

Unified Loss-Aware Density Control for 3D Gaussian Splatting

Tae-Young Kim, Jiwoo Chung, Jae-Pil Heo

原文摘要

The efficiency and fidelity of 3D Gaussian Splatting critically depend on adaptive density control, which grows and suppresses Gaussian primitives throughout optimization. However, the standard pipeline still relies on heuristic view-space gradients, scale thresholds, and global opacity resets, rather than explicitly assessing the loss-reducing effect of each split, clone, or opacity update. This mismatch can allocate primitives to already-sufficient regions, miss under-reconstructed structures, and destabilize training through blanket opacity changes. In this work, we recast adaptive density control as a collection of local loss-reduction decisions. We derive efficient Taylor-based per-Gaussian surrogates that predict the objective change caused by three primitive-level operations: splitting, cloning, and opacity decay. This yields a unified loss-aware framework with three components. First, we extend second-order splitting to a normalized joint position-scale space, enabling children to adapt not only their locations but also their anisotropic extents. Second, we formulate duplicate cloning as a surrogate minimization problem, selecting only Gaussians whose duplicated contribution is predicted to improve reconstruction and assigning each child a principled opacity. Third, we replace global opacity reset with selective soft opacity decay that weakens harmful primitives while preserving useful ones. Across standard 3DGS benchmarks, our method produces more targeted primitive allocation and achieves improved rendering quality with comparable or smaller Gaussian budgets compared to baselines.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04317

SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views

Jovan Kamchev, Zoran Ivanovski

原文摘要

Steel scrap composition estimation is an important part of yield planning and fair supplier compensation. Measuring the composition of a multi-ton pile is an inherently laborious process, so the assessment is often left to trained human operators. Recent works tackle composition-level tasks, but require structured acquisition and multi-stage preprocessing. We study a simpler approach to steel scrap load assessment by aggregating features from sparse, handheld views of the pile. On 600 out-of-fold piles, frozen DINOv2 tokens aggregated uniformly across views reach 13.4% total variation (TV) distance against operator estimates. Most remaining error lies in allocation among four thickness-defined heavy melting scrap (HMS) classes, yet image-dependent allocation reduces within-HMS TV from 25.1% under a training-set split prior to 11.2%. Due to the lack of public datasets and benchmarks for predicting composition, external evaluation relies on the public DOES dataset intended for classification. A classifier using the same frozen features reaches 93.97% accuracy, exceeding the published results we compare against. The composition model also aligns with related DOES categories without adaptation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04305

Weather Adaptive Autonomous Driving

Nicholas Taylor, Alexander Picon, Sudip Dhakal

原文摘要

Camera object detectors lose recall in rain, fog, snow and sand, and the confidence threshold at which a detector is deployed decides how many objects it misses and how many false alarms it raises. We study how a frozen detector should make this decision in adverse weather. We first reproduce SeqCRC, a conformal object detector whose first step calibrates the confidence threshold by conformal risk control (CRC), and use that step, calibrated separately in each weather condition, as our baseline. With YOLOv8x it keeps the held-out miss fraction within a 20% budget in every DAWN weather bin, where a single global threshold fails in fog, but the false alarms it needs are an output rather than a choice: from 1.6 to 117 per image across DAWN and BDD100K conditions. We then propose a calibrated trade-off dial. One setting, $λ$, the number of false alarms accepted to avoid one missed object, selects the expected-cost-optimal decision rule: either one score threshold, or a per-detection rule that keeps a box when its calibrated probability of being a real object, estimated by gradient-boosted trees from the detector's own per-box outputs, is at least $1/(1+λ)$. F-score preferences map onto the same dial, and CRC applied to $λ$ itself is planned to restore a per-condition miss guarantee. The dial is fitted once on BDD100K train and graded only on BDD100K val and DAWN.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04303

FlowTranslator: Training-Free In-Image Machine Translation with Flow-Based Editing

Zhixian Li, Xingsong Ye, Yongkun Du, Yunyan Xiao, Yanglin Huang, Pinxuan Chen, Jiyu Zhuang, Cong Xu 等 9 位作者

原文摘要

In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context. Existing task-specific models achieve convincing performance but require massive paired training images before and after translation. As advanced text-to-image (T2I) models have reached a practically usable level of multilingual text generation, we argue that this capability can be transferred to IIMT and present FlowTranslator. It is a training-free IIMT framework that first employs an OCR model and an LLM to generate the translation prompt, which is injected into the T2I-based diffusion model. To optimize the accurate and natural generation of translated text, we introduce two trajectory controls: Region Coupling Release (RCR) partially decorrelates target-branch noise inside the text region early in the trajectory, while Coupling Expansion (CE) amplifies the emerging edit displacement mid-trajectory. Under the EasyOCR read-back protocol, FlowTranslator achieves average PRIM BLEU and COMET scores of 20.6 and 57.2, respectively, substantially outperforming the strong training-based baseline UniTranslator. On the more challenging and realistic whole-image ScenePair photographs translated into Chinese, it reaches 40.1\% exact-match accuracy and surpasses existing editing systems, some of which only address the text-rendering stage rather than the complete IIMT task. These results establish flow-based editing as a strong, fully training-free baseline for IIMT.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04300

CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection

Richard Beuck, John Nguyen, Sudip Dhakal

原文摘要

Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measurements far beyond both. We present CLOCs-R, an extension of the CLOCs candidate-level fusion framework that adds radar evidence to camera-LiDAR rescoring. Radar returns are accumulated over several sweeps, compensated for ego-motion, and associated with each pre-NMS 3D candidate through a range-dependent gate. Compact features summarizing support, Doppler speed and spread, range residual, and offset are then appended to every camera-LiDAR pair before a lightweight fusion network rescores the candidates. We first verify that our fusion implementation reproduces CLOCs on KITTI. We then evaluate on the daytime clear-weather subset of the aiMotive dataset, reporting distance-binned metrics out to 200 m with frozen SECOND and Cascade R-CNN detectors. Camera-LiDAR CLOCs improves on SECOND up to 50 m but degrades beyond it. A residual scoring variant, introduced post hoc, corrects SECOND's score instead of replacing it and recovers most of this loss. Radar consistently improves BEV AP at 30-50 m (0.857 to 0.870), but beyond 50 m its gain is small (+0.003 center-distance AP), within seed variation, and CLOCs-R with residual scoring is statistically tied with LiDAR-only SECOND. Our analysis attributes the limited long-range benefit to weak separation of true and false detections by radar features, scarce far-range training positives, poor camera evidence at distance, and missing LiDAR proposals that rescoring cannot recover. We report all as-designed and post hoc results side by side.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04286

GroundSeek-CXR: Retrospective Evaluation-Guided Active Observation Framework for Multi-Label Chest Radiography Diagnosis

Yihang Fu, Xinyu Liu, Qing Xu, Xiaofeng Yang, Zhen Chen

原文摘要

Chest radiography (CXR) diagnosis is critical for thoracic disease assessment, yet challenged by the need to identify subtle lesions and co-occurring abnormalities across regions. While recent vision-language models can actively observe local regions, existing preference-based methods supervise each observation by its immediate effect on the current diagnosis, failing to capture how each observation and stopping decision contributes to the final diagnosis. To address this issue, we propose GroundSeek-CXR, an active CXR diagnosis framework that learns observation and stopping decisions by retrospectively evaluating them with the final diagnosis. Specifically, we first devise a Retrospective Diagnostic Evaluation (RDE) that scores observation and stop candidates from offline Monte Carlo tree search through independent continuations. Moreover, we design a Retrospective Gap-weighted Preference Optimization (RGPO) that learns from retrospective-score preferences weighted by their score gaps, prioritizing actions that improve diagnostic accuracy. Extensive experiments demonstrate that our GroundSeek-CXR framework outperforms state-of-the-art methods, and improves abnormal-image sample-F1 by 4.48% over immediate-score preference learning.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2603.18373

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

Rui Hong, Shuxue Quan

原文摘要

When Vision-Language Models (VLMs) answer correctly, do they rely on the image? We introduce a Tri-Layer Diagnostic Framework that asks three questions of every (image, question) pair when the image is blanked: does the model register that the image is missing (Latent Anomaly Detection, LAD), does its output distribution depend on the image (Visual Necessity Score, VNS), and does it nonetheless commit to a content answer? The cascade assigns each pair of any existing benchmark to one of four modes: Perceptual Blindness, Language Shortcut, Visual Sycophancy (the blank image registers and matters, yet the model commits to a fabricated answer), or Robust Refusal. Across nine open-weight VLMs and 9,000 model-sample pairs, 28.1% are Visual Sycophancy, 18.6% Language Shortcut, 5.6% Perceptual Blindness, and 47.8% Robust Refusal. The modes are strongly model-specific: the Qwen3-VL models refuse on blank input in 93-98% of pairs, whereas Pixtral-12B commits to an answer in 72%. Within-generation scaling increases Visual Sycophancy in both Qwen2.5-VL (7B->72B: 25.5->40.1%) and Llama-3.2-Vision (11B->90B: 34.3->48.0%), while the newer Qwen3-VL generation nearly eliminates it. A blinded human study confirms that LAD and VNS track what annotators observe, and that 80% of Visual-Sycophancy responses carry no textual sign that the image was blank.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00833

Fewer Tokens, More Answers: Verifiable-Reward Tuning of an Ornith-Style VLM for Multi-View Driving QA

T N Rickesh, Pranav Vinodh, Sowmya Kamath S

原文摘要

Reasoning-enabled model derivatives must turn intermediate deliberation into a usable answer within a deployment budget. We study that transition in an Ornith checkpoint adapted to multi-camera driving question answering with verifiable rewards. Paired archived outputs show that, on a selected 400-question development screen with a 256-token cap, GRPO increases answer extraction from 78.25% to 90.25% and accuracy from 55.50% to 63.75%. Of 39 wrong-to-correct transitions, 33 begin with an unextractable base response. The base's 87 unextractable responses all reach the token cap. Checkpoint-verified reconstruction on those same questions gives a much smaller first-pass accuracy gain at 512 tokens: +0.75 percentage points, versus +8.25 at 256. A later answer-cued continuation is analyzed separately; it helps the base more and narrows the adapter difference. On the full 2,511-question development set, this recovery-enabled pipeline reduces mean generated tokens from 108.6 to 47.1, while the paired accuracy interval includes zero gain. We frame these findings as adaptation of answer delivery under finite budgets, with explicit selection and provenance limits. Ornith's self-scaffolding lineage and newer reasoning-enabled derivatives such as Bonsai motivate the problem, but our measured results concern Ornith alone and do not establish a shared architectural mechanism or cross-model transfer.

cs.CV · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04268

Weight-Space Programming for Improving Image-Restoration Diffusion Models: Technical Conjectures and a Recursive Improvement Loop

Zhuoran Zheng, Doubao

原文摘要

Can an LLM directly edit the weights of a diffusion-based image-restoration model to \emph{improve} restoration quality---without gradients, paired data, or fine-tuning? Recent experiments show frontier LLMs can fix structural bugs by modifying as little as \(0.016\%\) of parameters, but cannot instill new capabilities. We ask whether weight-space programming (WSP) can move from \emph{debugging} to \emph{improvement} in the precise-regression setting of image restoration (deblurring, super-resolution, denoising, inpainting). Restoration diffusion UNets present a middle ground: low-level signal-processing layers are concentrated enough for targeted weight-direction edits, while high-level semantic layers remain distributed and resistant to sparse modification. We propose three technical conjectures: (C1) an LLM can localize the layer behind a quality gap (over-smoothing, color shift, hallucinated texture) within a 200 forward-pass budget; (C2) editing a small set of identified weight directions (e.g., scaling high-frequency filter norms, correcting output-bias channels) can measurably improve PSNR and perceptual quality; (C3) a recursive loop---diagnose \(\rightarrow\) edit \(\rightarrow\) evaluate \(\rightarrow\) re-diagnose---can compound gains across rounds, forming a minimal recursive self-improvement (RSI) loop for vision. We analyze the required AI capability (arguing targeted edits need not superintelligence, while general capability generation does) and detail the challenges of precise pixel-level regression. We provide a PyTorch-style deployment pipeline and outline a benchmark. This manuscript is conjectural, intended to open a new subtopic for image restoration.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04262

Sketch-Omni: Instance-Level Sketch-Based Visual Prompting for Perception MLLMs

Dongyu Wang, Dar-Yen Chen, Zhiming Hong, Arkaprabha Basu, Yi-Zhe Song

原文摘要

Promptable perception, from GroundingDINO to Rex-Omni, puts one model behind one prompt interface and returns coordinates for any localisation task. Every model in that line was built for photographs; none has been given a sketch. We present Sketch-Omni, to our knowledge the first perception MLLM built to work on sketches and from them. On a sketch, the sketch is the image, and text prompts detect, point, resolve object and spatial referring expressions, and ground affordances in it. From a sketch, the sketch is the prompt, and a drawing of one object localises that object in a photograph, even among several of the same category, a query that neither a category name nor a photo exemplar can pose. Two obstacles stand in the way. A photo-trained model's attention is tuned to colour and texture, which a sketch does not have, and no dataset pairs a drawing with the one instance it depicts among others of its kind. We build the training data instead of collecting it. An edge detector turns every labelled photograph into a sketch-like drawing that keeps the photograph's labels, and two constructions produce sketch-to-photo instance pairs from existing data. On top of this, photo-guided relation alignment passes each photograph and its edge sketch through the same model and trains the sketch pass to reproduce how the photograph pass relates phrases to patches and patches to each other. Not one training label is drawn by hand. Sketch-Omni reaches 91.4 F1 on sketch-prompted localisation against 81.8 for the strongest general-purpose MLLM, with a lead that doubles when the photograph holds several instances of the queried category, and it is best in 39 of 40 evaluation cells across six tasks. Code, model, and a hand-annotated benchmark of 2754 sketch-to-photo links will be released.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00828

Answer Correction with Verifiable Rewards: A Gemma-Family Study of Multi-View Driving QA

T N Rickesh, Pranav Vinodh, Sowmya Kamath S

原文摘要

Multi-camera driving question answering provides a discrete test of vision-language models, but post-training gains can reflect changed selected answers or improved answer extraction. We investigate this distinction within the Gemma model family using archived, task-linked WaymoQA evaluations. A zero-shot diagnostic on 5,519 test questions spans effective-small, mixture-of-experts, unified, and dense checkpoints, with serving differences disclosed rather than interpreted as architecture effects. The strongest controlled evidence is a matched-harness Gemma-4-12B base/GRPO pair: accuracy rises from 73.78% to 75.43%, a gain of 1.65 percentage points with a scene-cluster bootstrap interval of [+0.84, +2.41]. There are 215 improvements and 124 regressions; 213 improvements start from an extractable but incorrect base answer, and only two recover previously unextractable responses. Crossing correctness with extraction state shows why correct noncompliant outputs cannot be counted as prediction errors. Category counts reveal a critical safety asymmetry: routine perception gains (+5.93 pp) mask a regression in safety-critical hazard prediction (-1.82 pp), demonstrating that unweighted outcome rewards can trade off rare edge cases for aggregate accuracy. Reward saturation describes sampled training dynamics rather than an architectural ceiling. A separate archived Gemma-31B thinking comparison yields no detected average improvement, with uncertainty that does not establish equivalence. These results support a modest answer-selection gain for the recorded tuning configuration and a reproducible diagnostic accounting, without attributing corrected labels to specific grounding mechanisms or claiming superiority over matched supervised tuning.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04257

ProGeo-RC: Prototype-guided Semantic Reliability and Geometric Error Calibration for Semi-supervised Cardiac Image Segmentation

Yunfei Zhu, Wenfeng Zhang, Pan Zeng, Xin Huang, Tao Jia, Weiting Shao, Wei Hu, Jianming Hu

原文摘要

Semi-supervised medical image segmentation aims to train reliable segmentation models using limited labeled data and abundant unlabeled data. However, existing methods typically rely on prediction confidence or model consistency to estimate pseudo-label reliability, but these criteria are insufficient to characterize both semantic reliability and geometric plausibility. To address this issue, we propose ProGeo-RC, a prototype-guided semantic reliability and geometric error calibration framework for semi-supervised medical image segmentation. ProGeo-RC consists of two key modules: Prototype-guided Semantic Reliability Estimation (PSRE) and Structured Semantic-Geometric Error Calibration (S-SGEC). PSRE uses probabilistic class prototypes to estimate the semantic reliability of unlabeled predictions and calibrate pixel-wise teacher pseudo labels, while S-SGEC introduces signed-distance-field-based geometric constraints to refine ambiguous boundaries and suppress spurious foreground responses outside target structures. Experiments on the ACDC and MVAA-CT datasets show that ProGeo-RC achieves better performance compared with representative semi-supervised segmentation methods. Under the 10\% labeled setting on ACDC, ProGeo-RC improves DSC and Jaccard by 0.37\% and 0.49\% over the corresponding second-best results, while reducing 95HD and ASD by 0.61 and 0.21, respectively. These results indicate that joint semantic reliability estimation and geometric error calibration can provide more reliable pseudo supervision for semi-supervised medical image segmentation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04254

P-SER: Physical Consistency-Constrained Scattering Expandable Representation for Class-Incremental HRRP Target Recognition

Fangyi Wang, Peng Zhao, Haipeng Wang

原文摘要

High-resolution range profiles (HRRPs) provide compact radar target representations but exhibit strong aspect sensitivity, complicating class-incremental recognition when historical data are unavailable. Changes in viewing aspect shift scattering-peak locations and amplitudes, while reflector groups may produce responses resembling ships. Existing dynamic expansion methods can also incur substantial parameter and memory overhead. To address these challenges, we propose P-SER, a Physical Consistency-Constrained Scattering Expandable Representation framework for exemplar-free class-incremental HRRP recognition. P-SER reconstructs dominant scattering points from complex-valued sum- and azimuth-difference-channel HRRPs and observation geometry. The reconstructed spatial structure, target extent, and energy distribution are used to derive class-level scattering prototypes and sample-level saliency priors, providing physically grounded constraints for feature learning and old-class knowledge retention. A primitive scatterer dictionary further characterizes local strong-scattering mechanisms and calibrates uncertain ship--reflector predictions at inference. Meanwhile, lightweight residual adapters are incrementally introduced into a shared HRRP backbone to learn new classes with limited model growth. Experiments on simulated and measured HRRP datasets show that P-SER delivers competitive recognition performance without retaining old-class exemplars, while maintaining physical consistency, interference robustness, knowledge retention, and model scalability.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.20157

HumanScore: Benchmarking Human Motions in Generated Videos

Tiange Xiang, Yusu Fang, Tian Tan, Narayan Schuetz, Scott Delp, Li Fei-Fei, Ehsan Adeli

原文摘要

Recent advances in model architectures, compute, and data scale have driven rapid progress in video generation, producing increasingly realistic content. Yet, no prior method systematically measures how faithfully these systems render human bodies and motion dynamics. In this paper, we present HumanScore, a systematic framework to evaluate the quality of human motions in AI-generated videos. HumanScore defines six interpretable metrics spanning kinematic plausibility, temporal stability, and biomechanical consistency, enabling fine-grained diagnosis beyond visual realism alone. Through carefully designed prompts, we elicit a diverse set of movements at varying intensities and evaluate videos generated by thirteen state-of-the-art models. Our analysis reveals consistent gaps between perceptual plausibility and motion biomechanical fidelity, identifies recurrent failure modes (e.g., temporal jitter, anatomically implausible poses, and motion drift), and produces robust model rankings from quantitative and physically meaningful criteria.

cs.CV

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