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版本更新 · v2arXiv:2503.13383

An Embarrassingly Simple Data Selection Strategy for Million-Scale Multi-Modal Instruction-Tuning

Mengyao Lyu, Yan Li, Huasong Zhong, Hui Chen, Jungong Han, Guiguang Ding, Zhenheng Yang

原文摘要

Recent research demonstrates that pretrained large language models (LLMs) require minimal but carefully curated data for instruction-tuning. Building on LLMs, multi-modal models further contend with far larger data volumes, heterogeneous sources, and cross-modal alignment, amplifying both the necessity and the complexity of data selection. However, existing methods rely on deeply extracted embeddings, gradients or narrow quality scores that are time-consuming to compute and limited in expressiveness, frequently failing to surpass a simple random-sampling baseline. To overcome these limitations, we step back from complex embedding spaces and target the fundamental objective of instruction tuning: how and what knowledge should be elicited from the pretrained model and exposed to users. We leverage interaction \underline{S}tyles as a proxy for structural diversity, and decompose data quality into capability-specific \underline{S}cores, yielding an explicit yet \underline{R}ich (SSR) profile for each sample. We further cast the selection as submodular coverage maximization over the capability--style grid, providing an approximation guarantee. SSR consistently delivers superior performance across two distinct architecture stacks, LLaVA-1.5 (Vicuna + CLIP) and LLaVA-OneVision (Qwen2 + SigLIP), two large-scale data pools, six data budget settings, and 14 multi-modal benchmarks. To our knowledge, SSR is the first data selection method validated at million-scale, achieving 99.1% of full-data performance using only 30% of a 2.6M pool. Furthermore, the SSR profile models and selected subsets transfer training-free across data pools and model architectures with capability extensibility.

cs.CV · cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37314

A Lightweight Attention-Based CNN with Saliency-Map Interpretability for Skin Cancer Detection

Sanjana Islam Kasfia, Md. Ismiel Hossen Abir, AKM Bahalul Haque, Abir Mahmud Shahariar

原文摘要

Skin cancer is among the most prevalent and life threatening forms of cancer worldwide. Early and accurate diagnosis significantly improves patient survival rates. However, manual dermoscopic assessment remains challenging due to visual similarity. This study proposes an Explainable AI-driven framework for automated binary classification of skin lesions using a lightweight attention-based convolutional neural network (CNN-Attention). The model integrates channel attention to focus on informative regions of dermoscopic images while maintaining low computational complexity. A publicly available dataset comprising 2,637 training and 660 test images was used, with balanced class weights. The proposed model achieved 87 percent accuracy with 1.95 million parameters and 7.45 MB of storage, matching the best baseline (MobileNet) while using approximately half as many parameters and half as much storage. ROC AUC and PR AUC scores reached 0.9475 and 0.9430, respectively. Additionally, saliency maps were incorporated as an explainability technique to visualize regions that influence model decisions. Comparative analysis with VGG16,VGG19,ResNet101,and MobileNet demonstrates the proposed models effectiveness in balancing classification results, computational efficiency, and interpretability.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37309

MvT: Event-based Multi-view Projection for Multiple Object Tracking

Muxi Zha, Banglei Guan, Minzu Liang, Zibin Liu, Yang Shang, Qifeng Yu, Laurent Kneip

原文摘要

Event cameras are increasingly used for Multiple Object Tracking (MOT), but their asynchronous event output often requires specialized methods. Existing processing methods primarily follow two paradigms, pseudo-frames and event-by-event. The former is the prevailing approach since its data format aligns with images, making image-based techniques applicable. However, it suffers from tracking failures when trajectories overlap or are spatially close on pseudo-frames. Facing this challenge, we propose a multi-view pipeline, Multi-view Tracking (MvT), which preserves the 2D data format to leverage image-based techniques directly while introducing additional spatio-temporal views to resolve tracking ambiguities in a single view. MvT comprises a Multi-view Projection (MvP) module and a Multi-view Fusion (MvF) stage. MvP encodes events into three complementary spatio-temporal views while mitigating the pattern discretization. Within MvF, multi-view results are unified into a 3D coordinate system, and tracklets are associated through an optimization model subject to specific criteria combination. Evaluations on four datasets, including our self-collected Small Objects Dataset (SOD), show that MvT seamlessly integrates image-based methods and outperforms existing non-learning and learning trackers in generalized scenarios, and effectively resolves the single-view tracking ambiguities. Being training-free, MvT is applicable when ground-truth annotation is infeasible, thereby highlighting its practical, data-efficient potential. Code is available at https://github.com/zhazhabiu/MvTracking.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37290

Taming Noise Initialization for Zero-shot Video Restoration with Image Diffusion Priors

Chong Wang, Zixuan Fu, Hao Cheng, Siyuan Yang, Alex C. Kot, Bihan Wen

原文摘要

Recent advances in diffusion models have achieved remarkable success in restoring high-quality images in a zero-shot manner. Unfortunately, extending this success to video restoration is challenging due to the substantial computational resources required to pre-train video diffusion models. A rising trend seeks to address video restoration by utilizing a pre-trained image diffusion model, initiating restoration from either frame-wise independent or identical Gaussian noise. Yet, these approaches often fall short, as such simple noise initiations fail to capture and utilize the inherent temporal correlations within degraded video frames. In this work, we propose taming noise initialization for zero-shot video restoration, which preserves the intrinsic temporal correlations by generating initial noises directly using DDIM inversion on the corrupted video frames themselves. Building on this, we introduce an adaptive noise blending strategy that suppresses degradation patterns encoded during DDIM inversion while gradually steering the noise toward a Gaussian distribution. To further emphasize temporal consistency, we propose measurement-guided trajectory attention to better align features across frames. Extensive experiments on multiple video restoration tasks demonstrate that our method outperforms existing zero-shot approaches, consistently achieving superior results on the DAVIS, REDS4, and Set8 datasets.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.19511

Are Watermarked Images Editable? SafeMark for Watermark-Preserving Text-Guided Image Editing

Xiaodong Wu, Qi Li, Xiangman Li, Zelin Zhang, Lingshuang Liu, Jianbing Ni

原文摘要

This paper investigates a fundamental yet underexplored question: can watermarked images remain editable without compromising watermark integrity? We propose SafeMark, a framework for watermark-preserving text-guided image manipulation that explicitly integrates watermark integrity into the editing process. Specifically, SafeMark adds a thresholded watermark-decoding loss directly to the diffusion editor's training objective, fine-tuning the editor so that semantically valid edits also preserve the embedded watermark at the final output. This design admits a clean information-theoretic justification: maintaining high bit-accuracy on the edited image lower-bounds the mutual information that the editor channel preserves between watermark and edited output, the quantity that fundamentally controls watermark recoverability. SafeMark is compatible with differentiable diffusion-based editors, and requires no architectural modification. Extensive evaluations across multiple datasets, text-guided editing methods, and post-edit distortion settings demonstrate that SafeMark achieves high watermark bit accuracy across diverse editing settings while maintaining high-quality semantic edits, without sacrificing robustness to common post-edit distortions. These results demonstrate that semantic editability and watermark integrity are fundamentally compatible, enabling trustworthy image provenance in generative editing pipelines.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.00051

Latent Representation Learning from 3D Brain MRI for Latent Analysis and Prediction in Multiple Sclerosis

Trinh Ngoc Huynh, Nguyen Duc Kien, Nguyen Hai Anh, Dinh Tran Hiep, Manuela Vaneckova, Tomas Uher, Jeroen Van Schependom, Stijn Denissen 等 11 位作者

原文摘要

Neurological diseases can cause significant cognitive impairment, yet linking brain structure to cognitive function remains challenging due to the limitations of conventional methods and the black-box nature of deep learning. We propose InfoVAE-Med3D, an adaptation of InfoVAE for three-dimensional brain MRI that learns informative latent representations by relaxing the mutual information regularization penalty between inputs and latent representations. We evaluated the method on a healthy control (HC) dataset (\(n=6{,}503\)) with age and sex information and a multiple sclerosis (MS) dataset (\(n=916\)) with age, sex, and Symbol Digit Modalities Test (SDMT) scores. Two-dimensional visualizations revealed sex-related clustering and smooth transitions in grey and white matter volumes. Removing the mutual information regularization penalty generally improved MRI reconstruction and brain age and SDMT prediction using support vector regression, outperforming other variational autoencoder variants and achieving competitive performance with deep learning predictors, particularly on the MS dataset. InfoVAE-Med3D provides informative latent representations for investigating brain-related characteristics and supports latent-space analysis and imaging biomarker discovery for cognitive impairment, particularly in MS.

eess.IV · cs.CV · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37267

GradCO: Behavior-Aware Gradient-Conflict Offloading for Text-Based Person Anomaly Search

Zhiheng Fu, Zixu Li, Zhiwei Chen, Jiale Huang, Fangxu Liu, Jinhe Bi, Yupeng Hu

原文摘要

Text-based Person Anomaly Search (TPAS) retrieves a target pedestrian using natural-language descriptions of appearance and normal or anomalous behavior. Unlike conventional text-based person retrieval, TPAS must preserve identity cues while distinguishing fine-grained behaviors. It presents two challenges: same-identity, different-behavior hard negatives cause gradient conflict during granular alignment, while strong appearance cues can obscure weak behavioral evidence during aggregation. We propose GradCO (behavior-aware Gradient-Conflict Offloading), comprising three complementary components. Behavior-Aware Mixture-of-Experts (B-MoE) forms appearance, normal-behavior, and anomalous-behavior streams whose roles are learned from retrieval objectives without expert-specific semantic labels. Conflict Offload Learning (COL) uses pair-conditioned offload anchors in similarity computation to reduce the conflicting-gradient burden on the main text representations. Hierarchical Intent Alignment (HIA) aggregates expert similarities and features using query-conditioned weights. Experiments on five TPAS/TIPR benchmarks show GradCO significantly surpasses SOTA methods and validate our design.

cs.CV · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37236

HDVS: semi-supervised semantic segmentation via heterogeneous dual-branch voting supervision

Yongqi Shan, Yunzhi Zhuge, Huchuan Lu

原文摘要

Semi-supervised semantic segmentation, which aims to utilize large volumes of unlabeled images to achieve accurate segmentation results with fewer human annotations, has attracted increasing attention. Prior methods were primarily based on the assumption that the pseudo-labels generated by each branch were sufficiently reliable to supervise the remaining branches. However, there is a concern that the errors in pseudo-labels could accumulate during co-training, potentially resulting in suboptimal performance. To this end, we present a novel framework called heterogeneous dual-branch voting supervision (HDVS), which is designed to enhance the reliability of pseudo-labels and mitigate the issues arising from pseudo-labeling. Specifically, based on the cross-supervision framework, we introduce a voting mechanism that correlates pseudo-labels from heterogeneous branches to produce pseudo-labels with enhanced reliability. Concurrently, we employ a feature communication module to introduce perturbations at the feature level in each branch to maximize prediction diversity across the dual branches while maintaining convergence. Comprehensive evaluations of the proposed HDVS on two benchmark datasets (PASCAL VOC 2012 and Cityscapes) demonstrate its superiority to the state-of-the-art approaches.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37226

Subspace Clifford Networks

Zhongping Ji

原文摘要

In this work, we propose Subspace Clifford Networks, a reformulation that replaces global cyclic rolling with grouped subspace interactions. Instead of sampling sparse cyclic diagonals across the entire $D$-dimensional channel space, we project the features into an aligned space $\R^{\tilde{D}} = \bigoplus_{g=1}^{G} V_g$ partitioned into $G$ low-dimensional groups $V_g \cong \R^k$ with $k \in \{2, 3, 4\}$. Within each $k$-dimensional group, we compute all $\binom{k}{2}$ wedge coordinates and concatenate them with $\operatorname{SiLU}$-activated coordinate-wise products, while sandwiching the grouped interaction between learnable $1\times 1$ linear projections to enable cross-group channel mixing. This design reduces intermediate feature expansion from $2|\mathcal{S}|D$ (typically $10D$ for 5 shifts) down to $1.5\tilde{D}$ ($k=2$), $2.0\tilde{D}$ ($k=3$), or $2.5\tilde{D}$ ($k=4$). Furthermore, exploiting the small, fixed group size $k \in \{2, 3, 4\}$, we implement a fused CUDA kernel that evaluates the forward interaction and a recomputation-based analytical backward pass using thread-local variables without allocating global-memory buffers for elementwise intermediates. Finally, we extend the backbone from an isotropic columnar layout to a multi-resolution Hierarchical Clifford Network with configurable per-stage subspace dimensions. Experiments on CIFAR-100 show that the proposed models substantially reduce parameter count and training time across both regimes, while the hierarchical variants also improve classification accuracy over the evaluated shift-based hierarchical baselines.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37219HF 社区推荐

Vision-Language-Action or World-Action? Just Action Models

Yonghao Yu, Lang Huang, Runyi Li, Toshihiko Yamasaki

原文摘要

The vision-language-action (VLA) and world-action model (WAM) communities debate whether control policies should inherit pretrained knowledge from vision-language models or from visual generators. The debate, however, bundles three separable choices: backbone initialization, the objective applied to future observations, and whether the machinery for that future loss is kept at inference. A gap between a VLA and a WAM therefore cannot simply be attributed to any one of them. We show that decoupling these choices yields a common framework for both families, which we term Just Action Model (JAM): a pretrained visual backbone with an action expert, trained with an action loss and a foresight loss. The foresight loss trains current-observation features to predict frozen encodings of the recorded future through a prediction head discarded after training, so no visual generation is involved in either training or inference, and the backbone can be chosen freely. We first compare objectives within fixed policy architectures, then evaluate the foresight recipe across backbone families. The resulting JAM policies outperform their matched FastWAM and ImageWAM counterparts on LIBERO and LIBERO-Plus, and achieve similar or slightly higher success rates on RoboTwin. Compared with FastWAM, JAM improves the success rates from 97.0%/55.3%/91.2% to 98.2%/64.5%/91.4%, respectively. With cached foresight targets, JAM reduces estimated online training FLOPs per sample per update by 73% relative to FastWAM. Our results show that future supervision improves control without visual generation or additional inference machinery, and strong foresight performance is attainable with either generator-initialized or vision-language backbones. Project Page: https://y0uroy.github.io/JAM

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37218

TopoField: Technical Report for ECCV 2026 SceneFun3D Challenge

Zhiwei Chen, Zhiheng Fu, Zixu Li, Mingyu Zhang, Jiale Huang, Yupeng Hu

原文摘要

Functional segmentation requires locating small interactive parts, such as knobs, buttons, and sockets, in indoor point clouds and returning confidence-ranked instance masks. A point-wise semantic predictor produces a continuous probability field, whereas the benchmark evaluates a discrete set of scored instances. This creates a substantial field-to-instance gap. We present TopoField, a deterministic decoder built on top of VOLT. TopoField first converts 13 test-time forward passes into a semantic consensus field, then decodes instance candidates from the consensus geometry, and finally ranks the candidates in a class-aware manner. The augmentation stage retains the processed-point identity of every fragment prediction, accumulates probabilities in a common point array, normalizes by coverage count, and fuses the resulting caches. The decoding stage separates high-confidence cores from broader support regions, constructs a symmetric radius-pruned point graph, and uses a multi-source graph-geodesic partition to split support regions that contain multiple seeds. On 561 ground-truth instances across 30 validation scenes, an IoU-0.5-valid candidate exists for 67.2% of instances, and top-1 coverage is 20.0%. These complementary measurements motivate joint attention to candidate construction and ranking. TopoField achieves 28.286% AP, 41.383% AP50, and 47.558% AP25. TopoField achieved second place on the ECCV 2026 SceneFun3D Challenge.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37216

Zhengrong-Spatial-Aware 1.0: Advancing 3D Multimodal Intelligence

Yu Zhang, Xiaogang Wang, Yuhang Cheng, Jingrui Li, Wenbo Chen

原文摘要

This report presents the implementation and validation of two end-to-end multimodal adaptation pipelines for 3D-aware visual question answering on a custom spatial dataset. The first pipeline fine-tunes HiSpatial-3B, which augments RGB observations with metric-scale 3D point maps; the second adapts Qwen3-VL through memory-efficient QLoRA-based supervised fine-tuning. The completed engineering stack covers dataset conversion, image-annotation alignment, monocular geometry preprocessing, instruction construction, mixed-domain training, checkpoint management, batch inference, and automated evaluation. On the target spatial benchmark, HiSpatial adaptation increases overall accuracy from 1.73% before fine-tuning to 79.89% at 3,600 steps. On a separately collected styled split, accuracy improves from 14.9% to 55.1%, indicating transfer beyond the original training set. The Qwen3-VL pipeline is likewise implemented end-to-end for both 8B- and 32B-scale models using mixed-domain QLoRA adaptation. The resulting accuracy improves from 20.74% to 45.27% for Qwen3-VL-8B and from 30.79% to 47.60% for Qwen3-VL-32B. Together, these results establish two complementary and reproducible adaptation pipelines for geometry-aware and general-purpose multimodal spatial reasoning. These results establish a reproducible engineering foundation for subsequent work on metric 3D reasoning, spatial grounding, and domain-adapted multimodal systems, while also identifying training-data composition as a central factor in preserving general multimodal capability during specialization.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37213

Retrospective macromolecular pool fraction mapping from conventional clinical MRI: a physics-guided self-supervised deep learning framework

Jelmer van Lune, Miha Fuderer, Oscar van der Heide, Matteo Maspero, Martin B. Schilder, Jan Willem Dankbaar, Stefano Mandija, Alessandro Sbrizzi

原文摘要

Macromolecular pool fraction (MPF) is a quantitative imaging biomarker sensitive to myelin integrity and other changes in tissue macromolecular content, but specialized acquisitions and reconstruction methods limit its use in large-scale studies. We present a physics-guided, self-supervised deep learning framework for retrospectively estimating MPF directly from conventional clinical brain MRI ($T_1$-weighted, $T_2$-weighted, and fluid-attenuated inversion recovery [FLAIR]) without paired quantitative reference scans. The framework embeds an analytical two-pool magnetization transfer signal model that related sequence-averaged radiofrequency transmit power to apparent longitudinal relaxation. To accommodate heterogeneous clinical protocols, the network conditions intermediate features on acquisition parameters via adaptive instance normalization. It was trained and evaluated on a multisite, multivendor, multiprotocol dataset of 7,705 MRI sessions (3 T). Across 1,237 test sessions, mean MPF was $0.182 \pm 0.007$ in white matter and $0.144 \pm 0.009$ in gray matter, with limited variation across vendor-by-protocol groups (between-group coefficient of variation: 2.65% and 5.63%, respectively). In 70 sessions with demyelinating-disease labels, MPF was 35.6% lower in segmented lesions than in normal-appearing white matter. MPF maps from two different clinical protocols acquired in a healthy volunteer showed a mean voxel-wise absolute percentage difference of 7.7% and a concordance correlation coefficient of $0.888$, indicating good cross-protocol consistency. These results suggest that magnetization transfer-informed self-supervised learning enables stable MPF mapping from conventional clinical MRI, providing a scalable approach to investigate demyelinating pathology in both existing imaging archives and prospectively acquired conventional clinical scans without dedicated qMT acquisitions.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37211

Uncovering Shortcut Amplification in Video RLVR through the Lens of Perceptual Uncertainty Collapse

Jiafeng Liang, Xianhao Ou, Tao Ren, Rongchuan Mu, Runxuan Liu, Dandan Tu, Ming Liu, See-Kiong Ng 等 9 位作者

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for enhancing the video reasoning capability of Large Multimodal Models (LMMs). Despite its empirical effectiveness, we show that outcome-level optimization can introduce an undesirable shortcut amplification effect, whereby models increasingly favor text-prior answers even when they are not supported by the visual evidence. To characterize this behavior, we investigate the training dynamics of video RLVR through the lens of perceptual uncertainty, analyzing how predictive uncertainty evolves under video-conditioned and text-only contexts. Our analysis identifies a perceptual uncertainty collapse phenomenon, in which RL training continuously sharpens the model's answer distribution while the uncertainty reduction attributable to visual evidence progressively diminishes. A further gradient-direction analysis reveals that such confidence enhancement becomes increasingly less aligned with visually grounded uncertainty reduction and is instead dominated by text-prior compression. Motivated by these findings, we propose Bifocal Self-distillation Policy Optimization (BSPO), a self-distillation framework that regulates policy updates with two complementary distributional signals. Specifically, BSPO derives empirical reasoning principles from intra-group correct and incorrect rollouts to identify reasoning-sensitive tokens, and introduces shortcut-aware visual perturbation to penalize predictions that remain invariant when task-relevant visual evidence is weakened. By adaptively reweighting the original RL advantage, BSPO preserves the task-solving benefits of RLVR while promoting updates that are both reasoning-sensitive and visually grounded. Extensive experiments validate the effectiveness of BSPO in improving video reasoning performance and strengthening visual faithfulness.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37202

3D-SceneQ: Empowering 3D LLM With Query-Guided Adaptive Pruning and Multi-Modal Feature Enhancement

Yongqi Shan, Lu Zhang, Jiazuo Yu, Yunzhi Zhuge, Huchuan Lu

原文摘要

Current 3D scene understanding pipelines typically concatenate vast numbers of 3D object tokens with text tokens and feed the resulting sequence to a Large Language Model (LLM). However, existing 3D-LLMs for scene understanding encounter two major limitations: (i) excessive inclusion of task-irrelevant object data, introducing noise that reduces reasoning accuracy and increases hallucinations; and (ii) reliance solely on point cloud data, which inherently lacks the rich semantic information available in complementary 2D modalities, such as color, material properties, texture, and high-level contextual relationships. To address these challenges, we introduce 3D-SceneQ, a 3D LLM that unifies adaptive token pruning with multimodal semantic enrichment, markedly advancing scene understanding, reasoning, and grounding. Specifically, we propose a Query-Guided Adaptive Pruning (QGAP) module that adaptively selects task-relevant objects based on the intended meaning of user instructions, leveraging a learnable latent query to retain critical information while effectively suppressing irrelevant noise. In addition, we introduce a Multi-modal Object-level Feature Enhancement (MOFE) module, which integrates 2D feature embeddings from pre-trained models into 3D representation to enrich object-level semantic information. Evaluations show that 3D-SceneQ outperforms current state-of-the-art methods across multiple benchmarks (e.g., +5.7 CIDEr on Scan2Cap and +17.0 EM@1 on SQA3D), demonstrating its remarkable capabilities in various 3D language reasoning tasks.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37196

How Much Can Best-of-N Help a World Action Model? Measuring Selectable Headroom and Realizing It in Closed Loop

Haojia Gao, Yue Cao, Yumin Liu, Haohua Que, Mingkai Liu, Guanqiu Guo, Zhuo Zou, Bokui Chen 等 9 位作者

原文摘要

World action models (WAMs) predict the visual consequences of the actions they generate, which makes test-time selection a natural way to improve a fixed policy: imagine the futures of candidate actions and execute the best one. The benefit of selection is bounded by the selectable headroom, how much better the best candidate truly is, but the standard estimate, which executes each candidate once and keeps the best outcome, conflates candidate quality with the randomness of everything that follows. We introduce a denoised counterfactual protocol, based on bitwise-reproducible branching, repeated continuations, held-out oracles, and common random numbers, that measures the headroom a selector can realize, and use its measurements to build a fail-gated selector. Across four WAMs, two benchmarks, over 100,000 branched rollouts, and pre-registered tests, we find that (i) the standard estimate overstates the selectable headroom of native samples: bounds from 17 continuations per candidate place its selectable share between 24% and 66% for three WAMs; (ii) structured action perturbations create up to 16 points of selectable headroom; and (iii) in both WAMs with action-conditioned imagination, imagined futures carry candidate-outcome information with no detectable loss relative to real futures, information that the value head of Cosmos Policy leaves unused. Deviating from the policy chunk only when a readout of the WAM predicts failure, the selector raises closed-loop task success from 76.9% to 81.6% on held-out episodes of training tasks, with the largest gain where the policy fails most often, and from 62.6% to 69.8% on unseen tasks.

cs.RO · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37186

VGIQA: Fine-Grained Image Quality Assessment with Visual Grounding Rewards

Weichang Wang, Liyu Chen, Shuyue Guan, Jie Hu, Fei Gao, Wei Li, Nannan Wang

原文摘要

Image quality assessment (IQA) aims to evaluate the perceptual visual quality of images. Recently, the rapid development of Multi-modal Large Language Models (MLLMs) has significantly advanced the IQA field. By integrating chain-of-thought (CoT) reasoning and reinforcement learning (RL), these models can generate more accurate and interpretable results while relying on less training data. However, existing MLLM-based methods are predominantly constrained to holistic, image-level evaluations and lack the capability to produce fine-grained, localized assessments, which substantially limits their practical applicability. To address this limitation, we propose VGIQA, a novel IQA model endowed with both local and global quality assessment capabilities. VGIQA is trained via a two-stage paradigm comprising a cold-start phase followed by reinforcement learning. Driven by carefully designed local quality assessment reward functions, our model exhibits exceptional image quality understanding on VGSR-2K, a new dataset we propose specifically for local quality evaluation, significantly outperforming existing open-source MLLMs such as Qwen2.5-VL-72B-Instruct and Qwen3-VL-32B-Instruct. Furthermore, the local quality evaluation task can effectively enhance the model's performance on the degradation perception task.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37178

INTR++: INterpretable TRansformer with better Accuracy, Interpretability, and Adaptability

Dipanjyoti Paul, Arpita Chowdhury, Maneet Chatterjee, Daichi Kitaguchi, Tanya Berger-Wolf, Tetsuya Sakurai, Wei-Lun Chao

原文摘要

Interpretable Transformers often perform well in domain specific settings but lose effectiveness beyond that. Achieving ViT-like broad applicability requires models with consistent performance across domains. Recent works on Interpretable Transformers, INTR and Prompt-CAM, were developed keeping domain adaptability in mind. Prompt-CAM leverages learnable prompts to promote adaptability. However, these prompts are fixed in nature and therefore struggle to adapt to domains with substantial variation in input image characteristics, resulting in reduced interpretability. To address this limitation, we propose INTR++, which extends Prompt-CAM by incorporating a lightweight Prompt Adapter Module (PAM). PAM enables prompts to be adaptive by enriching them with domain-specific knowledge, thereby ensuring consistent interpretability across diverse domains including those with varying image characteristics such as medical imaging, micro-scopic imaging, and remote sensing imagery. INTR++ retains the standard architecture and end-to-end learning of Interpretable Transformer based models, while adding only a small number of additional parameters compared to Prompt-CAM. We demonstrate that INTR++ delivers consistent improvements in all aspects across domains like animals, plants, medical imaging, microscopic imaging, remote sensing imagery and object-centric data.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37153

FastBEVFusion: Simple Multi-Modal Fusion for 3D Object Detection at 50 FPS

Tom Sanitz, Emre Gülsoylu, Simone Frintrop, Christian Wilms

原文摘要

Reliable real-time 3D object detection based on LiDAR and camera data is critical for safety in autonomous driving, where systems must accurately perceive their surroundings with minimal latency. Many state-of-the-art systems achieve high-quality detection results but fail to meet real-time constraints, limiting their practical use. To address this challenge, we propose FastBEVFusion, a simple, fast, and robust fusion system for 3D object detection. Based on modality-specific features, FastBEVFusion projects these features into semi-aligned modality-specific BEV spaces, without relying on a computationally intensive and potentially error-prone depth estimation. Given these BEV spaces, we propose a light-weight, soft, and dynamic fusion module based on cross-attention, which adapts dynamically to the scene context and enables feature fusion across the entire BEV spaces. Experiments on the complex nuScenes dataset show that FastBEVFusion outperforms state-of-the-art real-time systems, and achieves over 50 FPS without deployment libraries. Additionally, our system shows strong robustness to calibration errors, making FastBEVFusion a powerful 3D object detection system for real-world applications.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37149

GCSR-OCC: Geometry Calibration and Semantic Routing for Open-Vocabulary Occupancy Prediction

Bohua Wang, Congyang Zhao, Tianxiong Lv, Chenxu Zhao, He Zheng, Hangqi Fan, Leigang Luo, Zhihao Liao 等 10 位作者

原文摘要

Open-vocabulary 3D occupancy prediction represents scene geometry and semantics in a unified voxel grid, enabling recognition beyond predefined categories. One approach reconstructs scenes with 3D Gaussians and converts them into voxel occupancy. However, Gaussian scale inflation and excessive overlap can distort voxel occupancy predictions, while semantic mixing can arise within Gaussians spanning multiple categories and during 3D--2D semantic feature alignment. We propose GCSR-OCC, which combines geometry calibration and multi-mode query-conditioned semantic routing for indoor occupancy prediction from a single RGB image. The Evidence-Conserving Gaussian-to-Occupancy operator (EC-G2O) uses kernel mass normalization, $p$-norm aggregation, and a learnable gain to suppress scale inflation and excessive local evidence accumulation while calibrating occupancy responses. Multi-mode Query-First Gaussian Semantic Routing (MM-QGSR) matches multiple semantic modes per Gaussian to text queries and aggregates query-specific evidence into voxels through a learnable text-conditioned router. Fine-grained subclass queries and smoothed semantic aggregation further refine voxel-level predictions. Without 3D semantic annotations, GCSR-OCC achieves state-of-the-art performance in open-vocabulary occupancy prediction on Occ-ScanNet, with 63.31% geometric IoU and 27.44% semantic mIoU. It also reduces geometric aggregation latency from 19.76 ms to 12.88 ms. Our code will be made publicly available.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25459

BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks

Yong Wang, Kecheng Zheng, Xing Zhu, Chao Xu, Yujun Shen, Shengze Cai

原文摘要

Bimanual manipulation with dexterous hands has attracted growing attention in research on robotic action models. However, few studies have developed benchmarks specifically tailored for dexterous manipulation, leaving the dexterous manipulation capabilities of existing models insufficiently characterized. In this paper, we present BiDex-100, a simulation benchmark for bimanual dexterous manipulation that combines broad task coverage, multi-dimensional evaluation, and high-DoF hand embodiments. Concretely, the benchmark comprises 106 tasks organized along five capability axes: generalization, instruction following, limited demonstrations, long horizon, and memory. Each task is paired with an out-of-domain variant featuring controlled visual and spatial changes, allowing systematic assessment of robustness. In addition, the benchmark supports two embodiments and provides 8,080 success-validated teleoperated demonstrations, totaling 6.25 million frames. We evaluate seven recent Vision-Language-Action models under a unified protocol, establishing baselines for capability-specific performance, robustness, and data efficiency. The website is available at https://robbyant-research.github.io/BiDex-100/.

cs.RO · cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37138

MixSIS: Mixed-supervised instance segmentation

Christian Wilms, Emre Gülsoylu, Sven Schreiber, Simone Frintrop

原文摘要

Weakly-supervised instance segmentation (WSIS) based on either box-, point-, or image-level annotations has been proposed to overcome the labor-intensive annotation process in fully-supervised instance segmentation. Despite decent results, most WSIS methods only focus on a single annotation level, ignoring that some objects are easy to spot from point annotations, while others require box or even mask annotations. Therefore, we propose the Mixed-Supervised Instance Segmentation (MixSIS) task, which allows a flexible mix of annotation levels (image, point, box, and mask) within a dataset. MixSIS has a high practical relevance, as it allows optimizing the annotation process by annotating challenging objects in more detail, while saving time on simpler instances with less detailed annotations. To address MixSIS, we propose AnnotationsMixer (AnnsMixer) as a baseline. AnnsMixer is a truly mixed-supervised instance segmentation system, which generates pseudo mask annotations from all annotation levels, even mixed within a single image. We evaluate AnnsMixer on both WSIS and MixSIS tasks, largely outperforming state-of-the-art methods. In three case studies, we also demonstrate how the unique features of MixSIS and AnnsMixer can substantially reduce the annotation time (-90%) or improve the results (+34%) in instance segmentation with a simple annotation rule or leveraging heterogeneous data. Our code is available at https://github.com/chwilms/MixSIS

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37134

MRI-to-CT Translation of Spinal Images via Anatomy-Guided Pseudo-Pairing and Adversarial Diffusion

Eduardo Nieto, Ibai Genua, Davide Scorza, Karen López-Linares

原文摘要

Magnetic Resonance Imaging (MRI) is routinely used to diagnose spinal disorders, but surgical planning often requires Computed Tomography (CT) for better visualization of vertebral bone anatomy. Synthesizing CT from MRI could avoid additional radiation exposure and streamline preoperative assessment. We propose a three-stage framework for spine MRI-to-CT synthesis. First, an anatomy-guided vertebral matching system identifies similar sagittal MRI and CT slices using spine segmentations and aligns them through rigid registration, creating pseudo-paired data from unpaired scans. Second, these pairs train an adversarial diffusion model for MRI-to-CT translation. Third, synthesized slices are assembled into 3D volumes and refined by a network that improves inter-slice continuity and structural coherence. The framework was trained on over 10,000 unpaired sagittal MRI and CT slices and evaluated on 1,911 slices from 83 MRI volumes spanning all spinal regions. Assessment combined distribution-based measures with vertebral segmentation metrics. Compared with diffusion alone, the proposed approach increased binary 3D vertebral Dice from 0.666 to 0.739. Volumetric post-processing reduced visible discontinuities between stacked slices and improved vertebral coherence in axial and coronal views. Combining anatomy-guided pseudo-pairing, adversarial diffusion and 3D refinement improves spine MRI-to-CT synthesis. Vertebral matching primarily enhanced anatomical alignment and fidelity, while volumetric refinement improved 3D consistency. These findings support further development of MRI-derived CT-like visualization for preoperative planning, particularly to preserve fine intensity details while maintaining volumetric coherence.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10473

Label-free cell counting and viability prediction with brightfield imaging and deep learning

Amir Reza Vazifeh, Christian Zeigler, Sornanathan Meyyappan, Richard Jeske, Jason W. Fleischer

原文摘要

Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. However, staining has several limitations: (a) chemical agents can perturb normal cellular processes of the cells being measured, (b) it is often ambiguous to assign viability to individual cells whose membrane integrity is only partially compromised, (c) photobleaching can undermine measurement accuracy over time when using fluorescent stains, and (d) staining cannot be performed in situ or in real time. Here, we show that (1) stained cells captured under brightfield imaging contain sufficient information to distinguish live and dead cells, and (2) cells captured under unstained brightfield imaging exhibit similar image features to their stained counterparts, enabling models trained on stained cells to generalize to unstained ones. We then report the development and validation of ViabiLens, an AI-assisted software tool for label-free cell viability analysis. ViabiLens combines a cell detection model for localizing individual cells with a convolutional neural network (CNN) classifier for live/dead prediction, paired with an interactive UMAP-based viewer for visualizing and exploring individual cells across the sample. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.33\% on unstained samples against fluorescence-based reference measurements. The demo of ViabiLens software is available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.

cs.CV · q-bio.CB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37127

MCP-Net: Real-Time Coronary Perception Network and Topological Analysis for Robotic Guidewire Navigation

Sahar Nasirihaghighi, Tianyuan Wang, Kenan Niu, Stefano Stramigioli

原文摘要

Coronary guidewire navigation during percutaneous coronary intervention requires continuous interpretation of vessel anatomy, guidewire position, and target stenosis under two-dimensional X-ray fluoroscopy. While existing approaches commonly address vessel segmentation or device localization as separate tasks, a robotic system requires a more structured perception of the coronary scene to support downstream navigation. In this work, we present a simulation-driven framework centered on a Multi-Task Coronary Perception Network (MCP-Net) for robotic guidewire navigation, integrating coronary vessel segmentation, vascular topology extraction, and roadmap generation. Starting from three-dimensional coronary CT angiography, we generate simulated fluoroscopy images containing vascular structures, simulated guidewires, stenotic lesions with varying severity, and diverse vascular geometries, together with simulation-derived ground truth. MCP-Net jointly performs coronary vessel segmentation, guidewire-tip localization, and stenosis localization from a single fluoroscopic frame. These predictions are subsequently converted into a structured vascular graph containing centerlines, bifurcations, endpoints, branch directions, and branch angles. By associating the predicted stenosis with this graph, the framework generates a roadmap from the automatically selected graph root toward the target lesion. The results demonstrate that the proposed framework can jointly recover coronary perception targets and transform them into an interpretable vascular representation suitable for downstream robotic navigation.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37118

Multibranch Ensemble Head in Vision Foundation Models for Efficient Cucumber Phenological Stage Classification

Kostiantyn Khabarlak

原文摘要

Vision Foundation Models (VFMs) pretrained with self-supervised learning, such as DINOv2, provide highly transferable features, yet adapting them to small, domain-shifted datasets is still challenging. We propose a regularizing multibranch ensemble head: a training and inference scheme that pairs low- and high-capacity branches, samples a single branch stochastically at each training step, and averages both branches in probability space at inference. The design adds negligible parameters and essentially no inference overhead over a single heavy head, while improving the accuracy-efficiency trade-off under data-scarce conditions. Accurate classification of plant growth stages is critical for precision agriculture, enabling timely decisions on fertilization, irrigation, and harvesting. Therefore, as a challenging practical case we use cucumber phenological stage classification, in which flowers and fruits are frequently occluded by foliage, and stage transitions manifest primarily as subtle changes in leaf morphology. The NALEF Food Safety dataset contains images of five cucumber growth stages captured under diverse real-world conditions. On it we evaluate a broad range of backbones, including DINOv2 and DINOv3 ViT and ConvNeXt variants, against strongly supervised counterparts (ResNet, EfficientNet, ViT). The proposed DINOv2 ViT-S with multibranch head achieves a Macro F1 of 0.978 and Cohen's Kappa of 0.971, matching the performance of models more than ten times larger in parameters and GFlops, while requiring only 12.2 GFlops and 22.3M parameters, making it deployable on smartphones and edge devices for phenological stage classification.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.12222

HiAP: A Multi-Granular Stochastic Auto-Pruning Framework for Vision Transformers

Andy Li, Aiden Durrant, Milan Markovic, Georgios Leontidis

原文摘要

Vision Transformers require significant computational resources and memory bandwidth, severely limiting their deployment on resource-constrained hardware. Most structured pruning methods reduce theoretical cost effectively, yet they typically operate at a single structural granularity and depend on multi-stage pipelines with importance ranking, auxiliary solvers or post-hoc magnitude thresholding, followed by a separate fine-tuning phase to recover accuracy. We propose Hierarchical Auto-Pruning (HiAP), which casts ViT pruning as a single budget-aware learning problem and jointly allocates sparsity across four granularities in one end-to-end phase. HiAP introduces stochastic Gumbel-Sigmoid gates at macro level (attention heads and FFN blocks) and micro level (intra-head dimensions and FFN neurons), and trains them against the task loss together with an analytical MAC cost term. Two penalty weights set how much compute is removed, while the gates gradually harden into a dense, smaller sub-network at convergence. It does not require importance heuristics, ranking metrics, auxiliary solvers or secondary fine-tuning. On ImageNet, HiAP compresses DeiT-Base to 7.4G MACs at 80.88% top-1 80.40% at 7.8G without distillation and DeiT-Small to 3.1G at 79.33%, competitive with substantially more complex pipelines at matched compute. The structurally pruned network can be accelerated natively on stock kernels, and more than 90% of the theoretical MAC reduction is realized as measured throughput at batch 256 on an A100.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37110

A First-Step Bias Restores Lost Diversity in Diffusion Model Distillation

Wenwu Tang, Dong Wang, Yun Cheng, Olga Saukh

原文摘要

Distribution-matching distillation enables high-quality image generation in only a few denoising steps. However, distilled student models often map different initial noise samples to similar outputs under the same prompt. To locate where diversity is lost, we compare the teacher and student along the sampling trajectory. The largest measured reduction in the response to different initial noises occurs during the first, high-noise step. When we replace the first step with the teacher's corresponding trajectory and leave the final three student steps unchanged, most of the output diversity returns. This suggests that the first step is a key point for restoring diversity. Motivated by this finding, we introduce \emph{ReSeed}, a post-distillation method that adds a seed-response bias adapter only to the first student step and aligns its velocity with the teacher's first-interval secant velocity. All student weights and later denoising steps remain unchanged. We evaluate ReSeed on three text-to-image models (SD3-Medium, SD3.5-Medium, and FLUX.1-dev) and one text-to-video model (Wan2.1-T2V-1.3B). For a distilled 4-step FLUX.1-dev model, \emph{ReSeed} requires only $0.00196\%$ additional trainable parameters relative to the base model and is trained on 8,000 prompt--noise pairs, while keeping the distilled student fully frozen and requiring no target images, perceptual losses, or adversarial losses. It improves DINO diversity by $48.3\%$ while maintaining competitive visual quality. These results show that substantial diversity can be recovered through a lightweight, data-efficient tuning of only the first denoising step, without finetuning the full student model.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.01767

LoBE-GS: Load-Balanced and Efficient 3D Gaussian Splatting for Large-Scale Scene Reconstruction

Sheng-Hsiang Hung, Ting-Yu Yen, Wei-Fang Sun, Simon See, Shih-Hsuan Hung, Hung-Kuo Chu

原文摘要

3D Gaussian Splatting (3DGS) has established itself as an efficient representation for real-time, high-fidelity 3D scene reconstruction. However, scaling 3DGS to large and unbounded scenes such as city blocks remains difficult. Existing divide-and-conquer methods alleviate memory pressure by partitioning the scene into blocks and training on multiple, non-communicating GPUs, but introduce new bottlenecks: (i) partitions suffer from severe load imbalance since uniform or heuristic splits do not reflect actual computational demands, and (ii) coarse-to-fine pipelines fail to exploit the coarse stage efficiently, often reloading the entire model and incurring high overhead. In this work, we introduce LoBE-GS, a Load-Balanced and Efficient 3D Gaussian Splatting framework, that re-engineers the large-scale 3DGS pipeline. Specifically, LoBE-GS introduces a load-balanced KD-tree scene partitioning scheme with optimized cutlines that balance per-block camera counts. To accelerate preprocessing, it employs depth-based back-projection for fast camera assignment, reducing processing time from hours to minutes. It further reduces training cost through two lightweight techniques: visibility cropping and selective densification. On large-scale urban and outdoor datasets, LoBE-GS reduces scene-partitioning time by up to an order of magnitude and worst-block fine-training time by up to $3.2\times$ relative to CityGaussian. It also substantially reduces end-to-end and post-coarse training time under matched GPU budgets while maintaining competitive reconstruction quality. LoBE-GS thus provides a communication-free alternative with favorable quality-efficiency trade-offs. The associated code is available at: https://github.com/Sunnyhong0326/lobe-gs

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37098

SplatScout: Query-Driven Part-Level Referring Segmentation in Gaussian Scenes

Yihan Zang, Da Li, Shinkyu Park, Ivan Viola, Dominik Engel

原文摘要

Existing referring segmentation methods on Gaussian scenes primarily target whole objects, relying on segmenter-generated candidates from which a vision-language model (VLM) selects. Such predetermined fixed candidates may omit fine-grained parts needed as targets or cues for identifying objects. We introduce SplatScout, a query-driven, closed-loop approach that places perception under VLM control. The model actively selects views, directs segmentation, and inspects rendered 3D results to refine its predictions. For part-level evaluation, we introduce PartRef-ScanNet++, comprising 261 manually verified queries across 12 ScanNet++ scenes with mesh-derived ground truth. On PartRef-ScanNet++, SplatScout achieves 0.538 mIoU, substantially outperforming existing methods. Ablations demonstrate the benefits of iterative refinement, while comparisons across VLM backbones highlight the importance of effective tool-feedback utilization. SplatScout also achieves higher scores on four existing referring benchmarks. Code and the PartRef-ScanNet++ benchmark are available at https://github.com/kaust-violab/SplatScout.

cs.CV

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arXiv ↗PDF ↗