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新论文 · v1arXiv:2610.36874

AdaGround: Token-Normalized Adaptive Zooming with Verifier-in-the-Loop Refinement for GUI Grounding

Bingchao Wang, Yongcheng Jing, Chengyu Wang, Jun Huang, Jie Yang, Wei Liu, Dacheng Tao

原文摘要

GUI grounding on high-resolution screens fails mostly on small targets, and zooming in is a common training-free remedy. However, most existing zooming pipelines do not match the zoom to the target's size, accept the zoomed answer unconditionally and never revisit a wrong answer. To address these issues, we propose AdaGround, a training-free framework with three components: (1) Token-Normalized Adaptive Zooming, which sets the magnification, field of view and zoom depth of each sample from the model's own estimate of the target size; (2) Verifier-in-the-Loop Refinement, which uses one marked-click Yes/No verifier to keep or reject each zoom, choose between zoom chains, trigger re-grounding and, with an existence query, decide when to abstain; and (3) Compositional Structure Grounding, which composes spreadsheet cells from their headers. On Qwen3-VL models, AdaGround surpasses state-of-the-art training-free zooming methods and many trained GUI models, improving accuracy over the backbone by 7.4 to 20.6 points on OSWorld-G and ScreenSpot-Pro, and its gains carry over to held-out UI-Vision. Code is available at https://github.com/bcwang-sjtu/AdaGround

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36873

RangeRectQuant: Compressing Scientific Fields for Every Future Rectangular Sum

Jiahao Zhang, Xinling Wen, Yu Chen

原文摘要

Scientific rasters are often stored before the regions to be measured are known. Independent pixel-error bounds can force exact integer reconstruction when one absolute error budget covers every future rectangle. Coordinated changes allow every pixel to change while every rectangular sum remains accurate. A prefix sum accumulates values from the top-left corner. Four such sums determine any rectangular total. RangeRectQuant preserves the pixel range and designated exact samples while controlling errors in prefix sums. At each row position, we characterize exactly which errors let the remaining pixels meet these constraints. These errors form a single integer interval, even when many completions exist. A backward pass finds its endpoints; a forward pass uses them to choose quantized pixels. The resulting field meets the prescribed error budget for every axis-aligned rectangle, with linear work and workspace proportional to one row. Experiments on geographically held-out built-surface and water-occurrence maps show substantial storage savings over alternatives meeting the same requirements. Data producers encode once; later users measure arbitrary rectangles without the source, re-encoding, or query-specific correction.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36870

Tail Cues, Principal Corrections: Plug-and-Play Rectification for Open-Set Test-Time Adaptation

Yingkai Yang, Chaoqi Chen, Hui Huang

原文摘要

Open-Set Test-Time Adaptation (OSTTA) operates on non-stationary target streams where covariate and semantic shifts coexist. Existing TTA methods often obtain adaptation signals by updating model parameters or maintaining mutable memories, which can be costly and vulnerable to unknown samples. We present TALOR, a lightweight, backward-free rectification module that uses the source linear head as a geometric anchor. The head-induced basis decomposes each normalized feature into high-gain principal coordinates and low-gain tail coordinates. TALOR estimates a soft-routed affine correction in the principal subspace, combining a regime-level principal bias with a tail-conditioned slope that captures sample-level tail-to-principal coupling. Using weighted ridge regression, it subtracts this drift estimate from the principal coordinates, reconstructs the feature and feeds it into the source head. On the ImageNet-C benchmark with six csOOD datasets, TALOR outperforms the next-best UniEnt by 2.2% H-score while running 2.5x faster and using only 12% of its GPU memory; as a post-correction plug-in, it further boosts COME to 68.4% H-score with only 2% additional memory overhead.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2503.15056

Single-Step Bidirectional Unpaired Image Translation Using Implicit Bridge Consistency Distillation

Suhyeon Lee, Kwanyoung Kim, Jong Chul Ye

原文摘要

Unpaired image-to-image translation has seen significant progress since the introduction of CycleGAN. However, methods based on diffusion models or Schrödinger bridges have yet to be widely adopted in real-world applications due to their iterative sampling nature. To address this challenge, we propose Implicit Bridge Consistency Distillation (IBCD), which enables single-step bidirectional unpaired translation without using adversarial loss. IBCD reformulates consistency distillation by using a diffusion implicit bridge model that connects PF-ODE trajectories between distributions, parametrized on a signed time axis whose sign selects the target domain, so that one network translates in both directions in a single step. Additionally, we introduce two key improvements: 1) distribution matching for consistency distillation and 2) adaptive weighting method based on distillation difficulty. Experimental results demonstrate that IBCD achieves state-of-the-art performance on benchmark datasets in a single generation step, and that it extends to brain MRI contrast translation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36859

ActiveAlign: Aligning Egocentric Manipulation via Active Perception

Ri-Zhao Qiu, Bowen Wen, Yifeng Jiang, Chen Tessler, Eric Heiden, Xue Bin Peng, Xiaolong Wang, Stan Birchfield 等 9 位作者

原文摘要

Real-to-Sim-to-Real (Real2Sim2Real) enables structured world representations for planning, interpretability, and safe execution. Most existing approaches rely on passive perception, which creates real2sim gaps due to imperfect observation. Inspired by how humans perceive actively through actions, we propose ActiveAlign, a framework that leverages active perception to align the Real2Sim2Real gap from interactions. ActiveAlign introduces two complementary modules: (i) ActiveAlign-Waving, which performs hand-eye calibration by exploiting active hand motions and differentiable rendering, and (ii) ActiveAlign-Touching, which refines object alignment through tactile feedback to achieve millimeter-level precision. Together, these capabilities enable strong real2sim alignment. We demonstrate that ActiveAlign-Waving can be used to curate simulation environments for open-sourced uncalibrated robot manipulation datasets, and demonstrate on a real Unitree G1 robot that ActiveAlign enables fine manipulation of millimeter-scale objects such as pressing buttons and moving sliders.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36858

Forget the Data, Keep the Capability: Switchable Unlearning for Retrieval

Jingong Chen, Kyle Gao, Guangyuan Li, Chulwoo Pack, Sijia Liu, Kwanghee Won

原文摘要

Privacy compliance requires re-identification models to selectively forget individuals while preserving their ability to recognize others. For this, we present MoME, a switchable framework that can adapt pre-trained backbones for privacy compliance, allowing the same model to switch between Full mode for standard inference and Core mode for unlearned inference. MoME splits one backbone into core parameters that preserve retrieval and zero-initialized parasitic parameters that carry the full-data knowledge, and training them under gradient isolation so the core never receives a forget-set gradient. Disabling the branch yields Core, which satisfies a request to delete a person's training data, and enabling it yields Full, which retains what the deleted data taught, so honoring the request is a flag flip rather than a retraining run. We prove Core independent of the forget set up to an audited set of coupling channels and introduce the rank-based Forgetting-Rank statistic to audit that independence in a trained model. On Market-1501, VeRi-776 and RegDB, Core reads at a retrained reference under three membership attacks while retrieving above it, and the retrieval advantage carries to ResNet and CLIP backbones. Code and weights are available at https://github.com/Jingong-Chen/MoME.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.31217

TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation

Abid Ali, Nidhal Eddine Chenni, Arunkumar Rathinam, Djamila Aouada

原文摘要

Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequence acquired during spacecraft manoeuvres. Few temporal approaches require full backbone fine-tuning or auxiliary optical flow networks, risking catastrophic forgetting or increasing computational cost, respectively. We propose TALON (Token-Aligned Lightweight adapters for Orbital Navigation): spatiotemporal 3D adapters injected before the self-attention layers of a frozen ViT vision transformer, combined with a patch-token alignment loss that geometrically grounds the adapted features to keypoint structure through a prototype-conditioned KL-divergence objective. Pre-attention placement allows the frozen attention to reason over temporally enriched tokens, achieving stronger performance with a single adapter per block than post-attention alternatives. The alignment loss shapes the intermediate representations so that each keypoint induces a spatially precise activation in the token field, while the framework adds less than 5% parameters to the frozen backbone. On SPADES dataset, TALON reduces the pose error by 50% over the prior state-of-the-art, and on SwissCube dataset it surpasses the prior best by 21.8% in ADD-0.1d accuracy. Zero-shot cross-domain evaluation from sim-to-real on SPARK real data reduces pose error by 4.7x, and ablations characterise the role of adapter depth across in-domain and cross-domain settings.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36853

LingGS: Decoupling Feed-Forward Geometry Prediction from Camera Tracking for High-Fidelity Gaussian SLAM

Da Li, Yihan Zang, Ivan Viola, Zeyu Wang, Dominik Engel

原文摘要

Achieving high-quality reconstruction and responsive camera tracking remains challenging for monocular RGB Gaussian splatting SLAM under a limited optimization budget. We present an online system that addresses this challenge by combining learned geometric priors with explicit geometric camera tracking, which can substantially outperform learned camera prediction in terms of tracking accuracy. A frozen streaming 3D foundation model serves as a virtual RGB-D sensor, providing depth estimation from monocular video. An explicit geometric solver then estimates camera motion through feature reprojection, and a streaming Gaussian mapper integrates the resulting observations into a dense scene representation. We further show that geometric tracking improves camera pose accuracy across representative streaming geometry backbones, demonstrating the broader applicability of our approach. Extensive experiments on indoor and outdoor datasets show that our method significantly improves reconstruction quality over existing approaches, achieving about 7 FPS on a single RTX 4090 and delivering 2-3 times the throughput of the evaluated prior SLAM systems.

cs.CV · cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33078

Evaluating Visual Language Models on Visual Grounding for Visually Impaired People

Fairooz Azim, Lydia Achour, Claudia Borg, Lina Rojas-Barahona

原文摘要

Visual grounding (VG) in visual question answering (VQA) is a fundamental capability of Vision-Language Models (VLMs), enabling the detection of the most relevant region in an image to answer the question. This reflects the model's capability of multimodal comprehension, which is crucial when interacting with visually impaired people (VIP). Indeed, visual grounding, when applied to assistive scenarios, is not merely a technical feature, but rather a trust and safety requirement. In this work, we study visual grounding in VLMs for visually impaired people. To this end, we assess open-source VLMs on visual grounding using the VizWiz-VQA-Grounding dataset and challenge, which contains real-life images taken by VIP. We propose VG-SHAP, which extends an existing explainability framework to provide visual grounding explanations, allowing the evaluation of black-box VLMs. Our results on the VizWiz-VQA-Grounding challenge show that despite generating correct answers, VLMs struggle to ground in the proper region in the image. However, these results are constrained by the quality of the segmentation model of our explainability framework and further research is necessary.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36850

uTHCD-C: A Corruption-Robustness Benchmark for Tamil Handwritten Character Recognition

Preethi S, Meeradevi T, Arun Prakash A

原文摘要

Evaluation of Tamil handwritten character recognition (HCR) has largely focused on clean test images, and a standardized multi-corruption robustness benchmark for isolated Tamil HCR has not been established. We introduce uTHCD-C, a corruption-robustness benchmark based on the unconstrained Tamil Handwritten Character Database (uTHCD). The benchmark defines ten corruption types, including a handwriting-specific stroke-thinning corruption, at five severity levels. We evaluate nine convolutional and transformer-based architectures using five-fold stratified cross-validation and writer-independent testing. Robustness is quantified using mean Corruption Error (mCE), normalized against a VGG16-BN reference. We additionally compare parameter count, computational complexity, and inference throughput to characterize the computational requirements of the evaluated architectures. The results establish comparative robustness baselines for Tamil HCR, revealing substantial differences across architectures: ConvNeXt and Swin achieve the lowest mCE values (0.75 and 0.83, respectively), whereas ShuffleNet and MnasNet exhibit higher values (1.55 and 1.85, respectively). The accompanying efficiency measurements characterize the trade-offs between corruption robustness and computational requirements. By providing standardized corruption configurations, evaluation protocols, and comparative results, uTHCD-C establishes a reproducible basis for future research on robust and computationally efficient Tamil HCR.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35554

Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU

Davide Cuttini, Diego Di Tommaso, Alessio Antoniacomi, Luca Simonetti

原文摘要

Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained from smartphone ARKit or ARCore poses captured alongside the camera. This paper evaluates a self-contained alternative: the IMU embedded in the Insta360 X5 and X6, recorded at 1 kHz. The sensor is characterised (noise density, camera-IMU synchronisation, accelerometer scale factor, bias stability), the camera-IMU rotation and frame timing are estimated from the data, and the scale is recovered by a linear visual-inertial alignment over the SfM poses of a whole survey. The standard formulation, with gravity and accelerometer bias shared across the survey, overestimates the scale by 3.4% on average; estimating each contiguous segment independently and averaging removes the bias. On nine surveys with a co-mounted LiDAR scanner the inertial scale deviates from the LiDAR by 0.81% in mean absolute error, against 1.94% for the smartphone scale; on printed fiducial markers both agree within the 1% uncertainty of the target. On the reference trajectories of thirty sequences of a public benchmark, brought to scales of 1/40, 1 and 40, the estimator recovers the metric scale with a mean error of -0.07% and a standard deviation of 1.28%. On fifty production surveys where the smartphone is the only reference, the two scales agree to a standard deviation of 2.3%, the inertial scale being 0.3% lower on average; the difference is uncorrelated with run length, grows with the extent of the smartphone anchor, and cannot be attributed to either reference. An ablation over window construction and a comparison with monocular metric-depth networks complete the study.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.15187

Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

Youliang Tao, Yanhua Han, Bin Zhao, Juho Kannala, Joni Pajarinen, Rongzhen Zhao

原文摘要

Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features into object-level representations and then from them reconstructs masked image content, i.e., Random Masking (RM), to provide self-supervision. The recent method DIAS simply masks image patches at uniform randomness yet achieves competitive object discovery accuracy. Since attention during aggregation already possesses object discovery ability, we explore using it to develop a better image patch masking strategy, i.e., Attention Guided Masking (AGM), thereby providing better self-supervision. Results on six recognized datasets show that AGM does not always outperform RM. Under unconditional slot initialization, AGM substantially improves background segmentation on datasets with realistic textures (COCO and VOC); Regardless of conditional or unconditional slot initialization and across datasets, foreground object discovery remains comparable or decreases. We suggest peer researchers in the OCL community that attempts to exploit internal attention semantics to improve OCL with masked decoding are risky. Our source code, model checkpoints and evaluation logs are available on https://github.com/und-entropy/Does-Attention-Guided-Masking-Really-Help-Object-Discovery-in-Object-Centric-Learning-.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36844

AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection

Chi Ruan, Tianqi Li, Xin He, Xue Geng, Ming-Ming Cheng, Yun Liu

原文摘要

Camouflaged object detection (COD) depends on weak texture and boundary cues that can be destroyed by aggressive post-training quantization (PTQ). Existing PTQ methods mainly optimize quantizers or reconstruct network blocks, while using a single precision-allocation domain throughout hierarchical backbones. Instead, we introduce AxisQ, a depth-adaptive bit-allocation framework motivated by the observation that the useful allocation axis changes with feature depth. Under a fixed per-layer average activation-bit budget, AxisQ allocates precision in the channel domain for shallow features and in the local-frequency domain for deep features, using COD-aware sensitivity to better preserve subtle foreground regions and ambiguous boundaries. We also formulate a boundary-aware Hessian approximation for reconstruction by incorporating boundary-weighted gradient and error statistics. Experiments across multiple COD models and diverse benchmarks demonstrate improved accuracy over the strongest baselines at low bit widths, with relative weighted F-measure gains of 12.7% for CFRN and 33.3% for CamoFormer-P on CAMO under W2A2 quantization. These results support adapting the allocation domain to feature depth as a transferable quantization strategy for hierarchical COD models.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.25322

Simulation of temporomandibular joint space changes based on rigid-body transformation using virtual and semi-virtual models

Agnieszka Anna Tomaka, Krzysztof Domino, Michał Tarnawski, Emil Kalinowski, Małgorzata Domino, Dariusz Pojda

原文摘要

The integration of multimodal data within a common coordinate system is essential for spatial analysis of the stomatognathic system. This study aims to develop a multimodal data integration methodology for digital modeling of spatial changes in the temporomandibular joint (TMJ) across various mandible positions. A transformation tree framework was proposed to integrate CBCT, facial, and dental representation images into a common coordinate system and to simulate seven mandible positions using rigid-body transformations. Two proprietary models were applied. Spatial changes in mandible positions were propagated to TMJ articular surface positions using both models, although the results were model-related. The model-based uncertainty was quantified using error metrics. The consistency error revealed variability that dominated in the translation component over the rotation component of the rigid-body transformation, and showed anisotropy in the latero-lateral direction. The simulation errors were not uniform across mandible positions and were higher (p<0.0001) at the TMJ level (1.46 mm) than at the dental level (0.47 mm). As even small discrepancies in mandible position can lead to significant changes in the TMJ joint space, the proposed methodology may be useful for evaluating geometrically related positions of the mandible and TMJ, particularly when repeated radiographic imaging of the TMJs is undesirable.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36838

MVRCap: Towards Comprehensive Video Captioning via Hierarchical Multi-Dimensional Verifiable Rewards

Weihuang Lin, Xianing Chen, Zhibin Wang, Congzhi Zhang, Yiwei Ma, Jiayi Ji, Miao Chen, Jun Song 等 12 位作者

原文摘要

Despite significant progress, current models struggle to generate comprehensive video captions, constrained primarily by coarse-grained supervision, which often fails to capture spatial-temporal dynamics and the underlying logical intent. To address this issue, we introduce MVRCap, a novel framework that reformulates video captioning as a reinforcement learning task steered by structured verification. Central to our approach is a Fine-Grained Verifiable Reward mechanism, serving as dense supervision to enforce rigorous visual-semantic alignment. Specifically, we construct this reward system anchored by three complementary pillars: a Hierarchical Concept Coverage Reward utilizing our newly constructed MVRCap-QA dataset to ensure multi-dimensional completeness and semantic depth, a Factuality Verification Reward to enhance factual consistency, and a Conciseness Constraint to maximize semantic density. Furthermore, we curate the high-quality MVRCap-SFT dataset, providing structured annotations across six key dimensions, to serve as a robust foundational initialization for the RL policy in this training pipeline. Extensive experiments demonstrate that MVRCap achieves state-of-the-art performance on authoritative benchmarks including VDC, CAPability, and VC4VG.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36832

Drop the Details, Get the Gist: Dynamic Conceptual Representation for Video Recognition

Ryoya Yoshiyama, Bowen Wang, Yuta Nakashima

原文摘要

Concept Bottleneck Models (CBMs), which route predictions through a layer of human-interpretable concepts, have substantially improved the interpretability of visual classification by exposing an explicit, language-based rationale for each prediction. Most existing work focuses on static images, and temporal dynamics in videos have been underexplored in conceptual representation. We propose a framework that extends concept-based image recognition to procedural video understanding. It represents a multi-step video as a temporal sequence of segment-level concept score vectors and feeds this sequence into a Transformer encoder to predict the class label. We also explore a way to estimate the contribution of each concept for further interpretability. We evaluated three concept sets of different granularities on cooking and household-activity videos and found that coarser-grained concepts are sufficient for classifying these videos, which implies that fine-grained textual descriptions may be unnecessary.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36831

Right Click, Wrong Box: Grading GUI Grounding by What a Click Does

Xiaokang Yuan, Binhui Yao, Luwei Yang, Ruimin Chen, Zeng Wang

原文摘要

Leading GUI grounders score 93 to 96% on ScreenSpot-v2, and reported gaps between new and prior models are about a point. At this level the useful question is what the remaining errors are. Benchmarks count a click as correct only inside one annotated box, but interfaces often expose one action through several elements: a duplicated link, the label of a checkbox, the title next to a logo. The box is not wrong. The rule is incomplete. We call a click correct when executing it produces the same outcome as clicking the annotated target. A probe that executes and fingerprints about a thousand clicks per page turns this definition into outcome maps and an execution judge for live web applications, with no new annotation. We execute the predictions of three strong 7--8B grounders on a code forge and an online shop. About one in five and one in three of the clicks that box scoring rejects reach the intended outcome, and the shares remain one in seven and one in three when the instructions are rewritten as natural tasks. A blind audit of 11 models on ScreenSpot-v2 (three 7--8B grounders and eight 3B variants) finds the same effect on web screenshots, with two independent reviewers on a 60-unit subset. The share is highest for the strongest grounders. Training seeds and test-set sampling add noise that more runs and paired intervals control. Box scoring instead under-counts correct clicks, which repetition does not remove, and the audit-based correction of 0.2 to 1.8 points on ScreenSpot-v2 is as large as the gaps the field reports. The correction is needed in evaluation. In our 3B GRPO study, crediting equivalent clicks in the reward gives no stable gain over box rewards across three seeds and four test sets. We release the probe, the outcome maps, the audit and every prediction.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36823

TAPESTRY: A Single Backbone for Video Generation and Robot Control

Jeremy A. Collins, Seungjae Lee, Rachanon Wachakorn, Krishnan Srinivasan, Animesh Garg, Vitor Campagnolo Guizilini, Paarth Shah

原文摘要

Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control typically requires either resource-intensive video generation at test time or action heads trained from scratch that do not fully utilize the pretrained backbone. We introduce Tokenizing Actions as Video Patches in a Shared Transformer Policy (TAPESTRY), a world action model that repurposes a pretrained video diffusion transformer into a robotic policy without requiring video generation at inference. Our key idea is to treat robot actions as single-patch tokens in the video model's latent space. A lightweight linear projection maps each action timestep to the patch dimensions of the pretrained VAE, and the resulting tokens are processed by the same patchifying layers and transformer backbone used for video. The shared backbone is optimized through three diffusion objectives sampled per minibatch element: Video diffusion uses VAE latents from robot and human videos to establish dynamics priors. Joint video+action diffusion trains on robot demonstrations using a coupled diffusion timestep and an asymmetric attention mask. Action-only diffusion denoises actions conditioned on clean image latents. At test time, the model generates a compact action token sequence in a single DDIM step from pure noise, producing smooth action chunks at 84 ms per action chunk on consumer hardware and avoiding the cost of full video generation. Across simulation benchmarks and real-robot experiments, TAPESTRY achieves strong performance with substantially lower inference cost than comparable joint video--action policies. More information can be found at https://tapestry-wam.github.io.

cs.RO · cs.AI · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36809

Latent Visual Contrastive Reasoning with Multi-Branch Exploration

Kegeng Tang, Jingbo Wang, Yangming Zhang, Huanshu Zhang, Shaogang Ren, Zihao Wang

原文摘要

Multimodal large language models have made substantial progress in visual question answering, but complex visual reasoning often requires locating and comparing multiple pieces of question-relevant visual evidence. Existing methods that move intermediate reasoning into the visual latent space usually follow a single linear reasoning trajectory, which can be limiting when multiple visual hypotheses or competing evidence regions are relevant. We propose Latent Visual Contrastive Reasoning, a branch-aware latent reasoning framework that generates multiple question-conditioned latent visual branches as soft hypotheses over image tokens. To train these branches, our method uses bounding-box evidence annotations to build target evidence representations and introduces a soft contrastive branch-evidence alignment objective. This objective encourages evidence-relevant branches to align with annotated visual regions while contrasting them against negative evidence. We instantiate our method on Qwen2.5-VL backbones and train it with standard supervised fine-tuning. Experiments on perception-intensive visual reasoning benchmarks show that the proposed method achieves overall improvements over the corresponding backbone and single-path latent reasoning baselines at both 3B and 7B scales.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36793

Visual Autoregressive Generation with Next Token Imagination

Ruihang Jiang, Zongyao Zhao, Zhaolin Wang, Yuanwei Liu, Xiatian Zhu

原文摘要

The space of possible visual content is enormous, and arguably unbounded. Any training set, however large, therefore covers only a tiny fraction of it. Yet generative models are typically trained to fit only the images they are given. In visual autoregressive (AR) generation, for example, each token is predicted from the preceding ground-truth (GT) tokens and supervised solely by the GT next token. Confining learning to observed data in this way limits how much of the visual distribution a model can capture. In this work, we hypothesize that letting a model imagine beyond its training data, by exploring plausible visual realizations it has never observed, can ease this limitation. We use visual AR generation as a proof of concept, since its fixed, discrete token space makes imagination easier to steer and constrain. We propose Next Token Imagination (NTI), a method that allows the model to explore beyond the GT next token, subject to a carefully designed criterion that keeps this imagination bounded by the underlying visual distribution. Extensive experiments on ImageNet show that NTI substantially and consistently improves the fidelity of generated images, approaching state-of-the-art diffusion and AR models while using a smaller model and lower training cost.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36785

OmniGlimpse: Asymmetric Identity Memory for Online Multi-View Tracking

Rangya Zhang, Jiaping Xiao, Yujing Shang, Yuhang Zhang, Mingsheng Li, Mir Feroskhan

原文摘要

Multi-view tracking extends visual coverage beyond a single camera, providing complementary observations when targets are occluded or leave the view. This raises a question: how can observations from different views be matched to the correct identities without offline scene-specific training or camera calibration? Progress toward this goal remains limited because many offline formulations assume such deployment-specific knowledge, while online adaptation can amplify association errors. We study this problem as online multi-view identity maintenance (OMIM): targets are initialized once per view, with no further identity labels provided after each initialization. To this end, we introduce OmniGlimpse, an asymmetric propose-verify framework that separates adaptive association from stable identity consolidation. At the representation level, short-term local and cross-view evidence is mapped into target-specific adaptive spaces to propose associations, whereas long-term identity distributions are maintained for verification in a canonical space defined by a frozen encoder. At the update level, short-term evidence may guide current associations, whereas, after bootstrapping, only verified observations may update persistent memory or drive adaptation. Under a unified target-specified evaluation protocol, OmniGlimpse improves over the best evaluated online baseline for each metric by 9.1 IDF1 and 22.1 CVIDF1 points on M3Track. The corresponding gains on MvMHAT are 20.3 and 26.2 points. Despite using no offline scene-specific training, it outperforms offline-trained references in IDF1 and remains competitive in cross-view association. These results support an approach to online identity maintenance that adapts to changing appearances while consolidating reliable evidence into stable identity anchors.

cs.CV

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新论文 · v1arXiv:2610.36781

CVBL-CCTV-Face Dataset for Real-World Face Enhancement and Recognition in Surveillance

Manish Das, Ajay Kumar Yadav, Shiv Ram Dubey, Satish Kumar Singh

原文摘要

In real-world surveillance settings, facial captures from closed-circuit television systems suffer from degradations such as low resolution, motion blur, noise, and occlusions, which undermine dependable face recognition. In this study, we propose a framework for super-resolution and identity recognition, aimed at surveillance applications. For this purpose, we present the CVBL-CCTV-Face Dataset which is a dataset of real-world images taken in an unpaired manner where each pair consists of a low-quality (LQ) image of a face captured by a 8MP IP Dome CCTV camera together with a high-quality (HQ) reference image that is taken in controlled settings. In order to cope with the various problems posed by such datasets, we construct a modular system that involves face detection, deep learning-based super-resolution of low-quality face images, and identity recognition through L2-normalized embeddings extracted by an embedding model. Enhancement models are trained and tested according to three different setups, and the system is deployed for identity verification in a real-time unconstrained environment.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.01517

SuperMotion: Source-Preserving Denoising for Text-Driven Human Motion Editing

Fa-Ting Hong, Peter Wonka

原文摘要

Text-driven human motion editing aims to realize a requested change while preserving compatible source content. Existing diffusion editors rely largely on learned conditioning for preservation of the unedited part, yet their outputs can lose temporal detail as denoising proceeds. We propose the \textbf{Source-Preserving Denoising framework (SuperMotion)}, which explicitly reuses the source at each reverse step for source preservation. We first align the source motion to the output timeline and predict a preservation gate that controls reuse across frames and feature dimensions. A clean-space source anchor then utilizes the learned preservation gate to blend the predicted clean motion with the aligned source and passes the corrected estimate directly to the sampling posterior. Because the aligned source is a realized motion rather than a regression output, the anchor injects sample-level temporal detail that a reconstruction-trained denoiser tends to smooth away. To learn effective source reuse, we supervise the anchored estimate against the editing target and match its second temporal differences through a temporal high-frequency loss. These objectives require no explicit edit masks. Extensive experiments show that SuperMotion improves editing accuracy, reaching 33.20\% full-pool R@1 on MotionFix, while reducing temporal-detail attenuation and preserving motion dynamics as it realizes the requested changes. Ablations confirm that the learned preservation gate is responsible for the gain and that it reuses the source to retain the unedited content properly.

cs.CV

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新论文 · v1arXiv:2610.36760

GeoGSplat: Geometry-Grounded Non-Pixel-Aligned Gaussian Splatting

Jiale Wang, Chen Zhao, Haoyang Zhang, Yanhao Wu, Fei He, Yanhu Shan, Rui Wu, Wei Ke 等 9 位作者

原文摘要

Feed-forward Gaussian Splatting enables efficient novel view synthesis, but pixel-aligned prediction makes the primitive count grow with image resolution and view count, leading to substantial redundancy and increased computational cost. Non-pixel-aligned methods address this limitation by predicting a fixed number of Gaussians. Without explicit ray and depth constraints, these methods yield misplaced Gaussian centers, causing rendering artifacts under large viewpoint changes. To address this issue, we introduce GeoGSplat, a geometry-grounded feed-forward model that explicitly incorporates geometric priors into the non-pixel-aligned framework. A dual-branch encoder constructs Anchors and Triplane to capture coarse 3D structure and fine-grained scene features, respectively. Subsequently, a hierarchical decoder progressively decodes dense 3D points and Gaussian primitives, enabling 3D reconstruction and novel view synthesis. GeoGSplat surpasses both state-of-the-art pixel-aligned and non-pixel-aligned Gaussian Splatting methods on DTU and NRGBD in terms of novel view synthesis and reconstruction. The advantage of our geometry-grounded paradigm further increases as target viewpoints move farther from the input cameras, demonstrating stronger generalization to large viewpoint changes. Project Page: https://comedy0913.github.io/projects/geogsplat

cs.CV

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新论文 · v1arXiv:2610.36759

DocSkill: Skill-Integrated Agentic Reasoning for Multilingual Multi-Hop Document Question Answering

Jing Huang, Zhiya Tan, Changtao Miao, Shutao Gong, Yang Wang, Joey Tianyi Zhou, Jianshu Li, Ying Yan

原文摘要

Multilingual multi-hop document question answering (MMHop-DocQA) requires locating and integrating evidence across complex layouts, document pages, and diverse languages. Existing approaches include single-pass processing and adaptive tool use, but learning to coordinate evidence acquisition, cross-page integration, and answer verification remains challenging. To address this challenge, we present DocSkill, an agentic framework with a learned skill-orchestration policy for adaptive evidence acquisition and reasoning. The policy selects, revisits, or skips skills according to the evolving evidence state. To construct supervision for skill orchestration, bounded Monte Carlo Tree Search (MCTS) explores alternative skill trajectories offline. These trajectories provide demonstrations for supervised fine-tuning (SFT) and preference pairs for Direct Preference Optimization (DPO). We further introduce MMHop-DocBench, a multilingual multi-hop document QA dataset with training and test splits, containing 60K QA pairs across 10 languages and eight document domains. Its HopGen pipeline builds hierarchical page-element graphs to derive traceable cross-page evidence paths and generate grounded QA pairs with page- and element-level evidence annotations. Experiments show that DocSkill with Qwen3-VL-8B achieves 41.0 accuracy and 49.2 F1 on MMHop-DocBench, exceeding the strongest evaluated baseline by 5.5 and 6.3 points, respectively, and achieving competitive performance on MMLongBench-Doc and DocBench.

cs.CL · cs.CV

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新论文 · v1arXiv:2610.36756

Efficient Remote Sensing Image-Text Retrieval via Interaction-Free Recall and Text-Guided Reranking

Xi Chen, Xu Chen, Xiamgyang Jia, Wei Wang, Xu Zhang, Zhenyuan Shun

原文摘要

Remote sensing (RS) image-text retrieval plays a critical role in understanding massive RS imagery. However, the dense multi-object distribution and complex backgrounds in RS imagery make it difficult to simultaneously achieve fine-grained cross-modal alignment and efficient retrieval. Existing methods either rely on complex cross-modal interactions that lead to low retrieval efficiency, or depend on large-scale vision-language adaptation, which requires massive data and computational resources. To address these issues, we develop a fast-then-fine (FTF) retrieval framework centered on interaction-free multi-granular recall and parameter-free text-guided cross-modal reranking with the Balanced Text-guided Interaction Block (BTIB). Specifically, interaction-free multi-granular recall pre-computes gallery embeddings and matches them with the corresponding query embedding without full-gallery token-level text-image interaction, while text-guided cross-modal reranking with BTIB refines local semantic correspondence within the top-K candidate set. Furthermore, an inter- and intra-modal loss is designed to jointly optimize cross-modal alignment across multi-granular representations. Extensive experiments on public benchmarks demonstrate that the FTF achieves competitive retrieval accuracy while significantly improving retrieval efficiency compared with existing methods.

cs.CV

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新论文 · v1arXiv:2610.36750

Multi-task Disentangling Fusion Mamba-CNN for MODIS Spectral-Temporal Unmixing with Endmember Variability Modeling

Motasem Alkayid, Yimin Zhu, Naser El-Sheimy, Lincoln Linlin Xu

原文摘要

Spectral-temporal unmixing of MODIS time series is essential for applications such as abundance mapping and spectral library construction, yet it remains challenging due to (i) spectral-temporal information coupling in MODIS data, (ii) intra-class endmember spectral variability and inter-class signature ambiguity, and (iii) the instability of naively fusing multiple expert representations. This paper presents the Multi- task Disentangling Fusion Mamba (MDF-Mamba) to address these limitations. To resolve spectral-temporal coupling, we design a disentangling architecture that pairs per-channel spectral experts with a dedicated bidirectional state-space (Mamba) temporal branch. Convolutional modules each process a single spectral channel, while the Mamba branch encodes the cross-channel seasonal trajectory, forming a multi-branch expert ensemble with soft inter-expert reweighting based on squeeze-and-excitation. For the integration of these spectral and temporal expert outputs, we introduce mHCFusion, adapting the manifold-constrained hyper-connection framework to merge the outputs in a stable and interpretable manner. Endmember variability is addressed by extending the linear mixing model with pixel-wise learnable spectral variability weights that capture intra-class endmember variation. Finally, we design a multi-task training objective that combines supervised abundance estimation with self-supervised spectral reconstruction and endmember regularization terms on the same training data, yielding a highly efficient framework for spectral-temporal unmixing. Experiments on MODIS time series covering the Alberta and Saskatchewan provinces show that MDF-Mamba achieves the best performance on 12 of 14 evaluation metrics across both datasets (7 of 7 on Alberta; 5 of 7 on Saskatchewan), ranking second on the remaining two.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33484

Can One Hear Geometry? Gauge-Invariant Spectral Descriptors for Text-to-B-rep Retrieval

Hongzhu Yi, Hao Liu, Tao Yu, Tiankun Yang, Yujia Yang, Zhenghao Zhang, Tianyu Zong, Zhenyu Guan 等 13 位作者

原文摘要

Text-to-3D solid retrieval faces a fundamental representation gap: while natural language describes intrinsic shapes, Boundary Representation (B-rep) entangles precise geometry with arbitrary variations in pose, scale, and boundary encoding conventions. Existing methods either discretize B-reps at the cost of analytic precision, or learn features from topological graphs without explicit invariance guarantees. We propose a text-to-B-rep retrieval framework based on geometric equivalence classes and spectral invariants, mathematically eliminating representation ambiguities prior to cross-modal alignment. Formalizing translation, rotation, reflection, uniform scaling, and contour re-indexing via group actions, we exploit the planar-profile-and-extrusion structure of engineering solids to construct a unified spectral descriptor across cross-sectional regions, contour boundaries, and extrusion depths. On public benchmarks, our method achieves state-of-the-art recall performance across all evaluation metrics.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2311.17098

DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors

Daeun Seo, Hoeseok Yang, Hyungshin Kim

原文摘要

Achieving constant accuracy in object detection is challenging due to the inherent variability of object sizes. One effective approach to this problem involves optimizing input resolution, referred to as a multi-resolution strategy. Previous approaches to resolution optimization have often been based on pre-defined resolutions with manual selection. However, there is a lack of study on run-time resolution optimization for existing architectures. This paper introduces DyRA, a dynamic resolution adjustment network providing an image-specific scale factor for existing detectors. This network is co-trained with detectors utilizing specially designed loss functions, namely ParetoScaleLoss and BalanceLoss. ParetoScaleLoss determines an adaptive scale factor for robustness, while BalanceLoss optimizes overall scale factors according to the localization performance of the detector. The loss function is devised to minimize the accuracy drop across contrasting objectives of different-sized objects for scaling. Our proposed network can improve accuracy across various models, including RetinaNet, Faster-RCNN, FCOS, DINO, and H-Deformable-DETR. The code is available at https://github.com/DaEunFullGrace/DyRA.git.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36743

Readout and Operating-Point Sensitivity in Event-Stream Candidate Selection

Yifan Shuai

原文摘要

Event-triggered recording needs a useful combination of target capture and false triggers, yet representation comparisons can change with readout and threshold calibration. We evaluate real event accumulations from 520 DailyDVS-200 clips and 47 participants using participant-disjoint training, validation, and testing. The original study varies frozen encoder scale, accumulation duration, and temporal view count; a post-review extension adds two supervised-pretrained convolutional encoders, a 61,586-parameter trained CNN with three seeds, ten repeated participant partitions, and calibration sensitivity. At 50 ms/eight views under the primary handshake-or-fall policy, DINO-S CLS captures 101/132 targets with 40/388 false triggers, versus 78/132 and 35/388 for patch mean. DINO-B CLS has lower selected-threshold capture but higher fold-wise low-FPR partial AUC (0.746 versus 0.732). MobileNetV3-Small captures 97/132 with 53/388 false triggers, demonstrating why lightweight baselines and actual operating points matter. Across ten dependent repartitions, mean normalized partial AUC is 0.738 for DINO-B CLS and 0.674 for MobileNet. The original LeJEPA S-to-B capture difference is 8.3 points, but its multiplicity-adjusted interval includes zero. Duration and view-count effects remain policy dependent. We find no established JEPA-specific advantage. The evidence concerns offline action-defined candidate selection on previously used development data; it does not establish personal recording value, continuous false alarms per hour, or eyewear energy.

cs.CV

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