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Edges Are All You Need: Robust Gait Recognition via Label-Free Structure
2026-03-04 05:48:07Chao Zhang, Zhuang Zheng, Ruixin Li, Zhanyong MeiAbstract
Gait recognition is a non-intrusive biometric technique for security applications, yet existing studies are dominated by silhouette- and parsing-based representations. Silhouettes are sparse and miss internal structural details, limiting discriminability. Parsing enriches silhouettes with part-level structures, but relies heavily on upstream human parsers (e.g., label granularity and boundary precision), leading to unstable performance across datasets and sometimes even inferior results to silhouettes. We revisit gait representations from a structural perspective and describe a design space defined by edge density and supervision form: silhouettes use sparse boundary edges with weak single-label supervision, while parsing uses denser cues with strong semantic priors. In this space, we identify an underexplored paradigm: dense part-level structure without explicit semantic labels, and introduce SKETCH as a new visual modality for gait recognition. Sketch extracts high-frequency structural cues (e.g., limb articulations and self-occlusion contours) directly from RGB images via edge-based detectors in a label-free manner. We further show that label-guided parsing and label-free sketch are semantically decoupled and structurally complementary. Based on this, we propose SKETCHGAIT, a hierarchically disentangled multi-modal framework with two independent streams for modality-specific learning and a lightweight early-stage fusion branch to capture structural complementarity. Extensive experiments on SUSTech1K and CCPG validate the proposed modality and framework: SketchGait achieves 92.9% Rank-1 on SUSTech1K and 93.1% mean Rank-1 on CCPG.
Abstract (translated)
步态识别是一种用于安全应用的非侵入性生物特征技术，但现有的研究主要集中在轮廓和解析表示上。轮廓是稀疏的，并且缺少内部结构细节，这限制了其鉴别能力；而解析则通过引入部分级结构来丰富轮廓，不过它严重依赖于上游的人体解析器（例如标签粒度和边界精度），导致在不同数据集上的表现不稳定，在某些情况下甚至比单纯的轮廓识别效果更差。我们从结构角度重新审视步态表示，并定义了一个由边缘密度和监督形式构成的设计空间：轮廓使用稀疏的边界边缘并带有弱单标签监督，而解析则利用更密集的线索以及强大的语义先验。在这个设计空间中，我们发现了一种被忽视的方法：没有显式的语义标签但具有密集的部分级结构，并引入SKETCH作为一种新的步态识别视觉模式。通过基于边缘检测器从RGB图像直接提取高频结构线索（例如肢体关节和自我遮挡轮廓），Sketch在无标签的情况下工作。我们进一步证明，引导式解析与非引导式草图在语义上是解耦的，在结构上是互补的。基于此，我们提出了SKETCHGAIT，这是一个分层解缠的多模态框架，包括两个独立的学习流和一个轻量级的早期融合分支来捕捉结构上的互补性。在SUSTech1K和CCPG数据集上的广泛实验验证了所提出的模式和框架的有效性：SketchGait在SUSTech1K上达到了92.9%的第一名准确率，并且在CCPG上实现了93.1%的平均第一名准确率。
URL
https://arxiv.org/abs/2603.05537
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From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
2026-03-01 10:28:35Haoyuan Zhang, Keyao Wang, Guosheng Zhang, Haixiao Yue, Zhiwen Tan, Siran Peng, Tianshuo Zhang, Xiao Tan, Kunbin Chen, Wei He, Jingdong Wang, Ajian Liu, Xiangyu Zhu, Zhen LeiAbstract
Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classification task as the generation of brief textual descriptions to improve cross-domain generalization. However, their generalizability is still limited, as such descriptions mainly capture intuitive semantic cues (e.g., mask contours) while struggling to perceive fine-grained visual patterns. To address this limitation, we incorporate external visual tools into MLLMs to encourage deeper investigation of subtle spoof clues. Specifically, we propose the Tool-Augmented Reasoning FAS (TAR-FAS) framework, which reformulates the FAS task as a Chain-of-Thought with Visual Tools (CoT-VT) paradigm, allowing MLLMs to begin with intuitive observations and adaptively invoke external visual tools for fine-grained investigation. To this end, we design a tool-augmented data annotation pipeline and construct the ToolFAS-16K dataset, which contains multi-turn tool-use reasoning trajectories. Furthermore, we introduce a tool-aware FAS training pipeline, where Diverse-Tool Group Relative Policy Optimization (DT-GRPO) enables the model to autonomously learn efficient tool use. Extensive experiments under a challenging one-to-eleven cross-domain protocol demonstrate that TAR-FAS achieves SOTA performance while providing fine-grained visual investigation for trustworthy spoof detection.
Abstract (translated)
面部识别仍然容易受到呈现攻击的影响，因此需要强大的防欺骗解决方案（Face Anti-Spoofing, FAS）。最近基于多模态大型语言模型 (MLLM) 的 FAS 方法将二元分类任务重新表述为生成简短的文本描述，以提高跨域泛化能力。然而，它们的泛化能力仍然有限，因为这些描述主要捕捉直观的语义线索（例如，面具轮廓），而在感知细微视觉模式方面却显得力不从心。为了克服这一限制，我们整合了外部视觉工具到 MLLM 中，鼓励更深入地调查微妙的欺骗线索。 具体而言，我们提出了带有视觉工具增强推理的 FAS (TAR-FAS) 框架，该框架将 FAS 任务重新表述为“带有视觉工具的链式思考”（CoT-VT）模式。这使 MLLM 可以从直观观察开始，并根据需要灵活调用外部视觉工具进行细微级别的调查。为此，我们设计了一种增强工具的数据注释流水线并构建了 ToolFAS-16K 数据集，该数据集中包含了多轮次的工具使用推理轨迹。 此外，我们还引入了一个考虑工具的 FAS 训练流水线，在这个过程中，多样化的工具组相对策略优化（DT-GRPO）使模型能够自主学习高效的工具使用方法。在具有挑战性的十一种跨域协议下进行的大量实验表明，TAR-FAS 达到了最先进的性能，并提供了细粒度的视觉调查，从而为值得信赖的欺骗检测提供支持。
URL
https://arxiv.org/abs/2603.01038
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Neural Image Space Tessellation
2026-02-27 07:31:40Youyang Du (Shandong University, Mohamed bin Zayed University of Artificial Intelligence), Junqiu Zhu (Shandong University), Zheng Zeng (University of California, Santa Barbara), Lu Wang (Shandong University), Lingqi Yan (Mohamed bin Zayed University of Artificial Intelligence)Abstract
We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approach that produces the visual effect of tessellated geometry while rendering only the original low-polygon meshes. Inspired by our observation from Phong tessellation, NIST leverages the discrepancy between geometric normals and shading normals as a minimal, view-dependent cue for silhouette refinement. At its core, NIST performs multi-scale neural tessellation by progressively deforming image-space contours with convolutional operators, while jointly reassigning appearance information through an implicit warping mechanism to preserve texture coherence and visual fidelity. Experiments demonstrate that our approach produces smooth, visually coherent silhouettes comparable to geometric tessellation, while incurring a constant per-frame cost and fully decoupled from geometric complexity, making it well-suited for large-scale real-time rendering scenarios. To the best of our knowledge, our NIST is the first work to reformulate tessellation as a post-processing operation, shifting it from a pre-rendering geometry pipeline to a screen space neural post-processing stage.
Abstract (translated)
我们介绍了神经图像空间镶嵌（Neural Image-Space Tessellation，NIST），这是一种轻量级的屏幕空间后期处理方法，在仅渲染原始低多边形网格的情况下即可产生几何镶嵌的效果。受到从Phong镶嵌中观察到的现象启发，NIST利用了几何法线和光照法线之间的差异作为最小化、视图依赖的轮廓细化提示符。在核心方面，NIST通过使用卷积操作逐步变形图像空间中的轮廓来执行多尺度神经镶嵌，并通过隐式扭曲机制重新分配外观信息，以保持纹理一致性和视觉保真度。实验表明，我们的方法可以生成平滑且与几何镶嵌相媲美的视觉上连贯的轮廓效果，同时每帧成本固定并且完全独立于几何复杂性，使其非常适合大规模实时渲染场景。据我们所知，NIST是首个将镶嵌重新定义为后期处理操作的工作，将其从预渲染的几何管道转移至屏幕空间神经后期处理阶段。
URL
https://arxiv.org/abs/2602.23754
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A Boundary-Metric Evaluation Protocol for Whiteboard Stroke Segmentation Under Extreme Imbalance
Abstract
The binary segmentation of whiteboard strokes is hindered by extreme class imbalance, caused by stroke pixels that constitute only $1.79%$ of the image on average, and in addition, the thin-stroke subset averages $1.14% \pm 0.41%$ in the foreground. Standard region metrics (F1, IoU) can mask thin-stroke failures because the vast majority of the background dominates the score. In contrast, adding boundary-aware metrics and a thin-subset equity analysis changes how loss functions rank and exposes hidden trade-offs. We contribute an evaluation protocol that jointly examines region metrics, boundary metrics (BF1, B-IoU), a core/thin-subset equity analysis, and per-image robustness statistics (median, IQR, worst-case) under seeded, multi-run training with non-parametric significance testing. Five losses -- cross-entropy, focal, Dice, Dice+focal, and Tversky -- are trained three times each on a DeepLabV3-MobileNetV3 model and evaluated on 12 held-out images split into core and thin subsets. Overlap-based losses improve F1 by more than 20 points over cross-entropy ($0.663$ vs $0.438$, $p < 0.001$). In addition, the boundary metrics confirm that the gain extends to the precision of the contour. Adaptive thresholding and Sauvola binarization at native resolution achieve a higher mean F1 ($0.787$ for Sauvola) but with substantially worse worst-case performance (F1 $= 0.452$ vs $0.565$ for Tversky), exposing a consistency-accuracy trade-off: classical baselines lead on mean F1 while the learned model delivers higher worst-case reliability. Doubling training resolution further increases F1 by 12.7 points.
Abstract (translated)
白板笔迹的二值分割受到极端类别不平衡的影响，这是因为笔迹像素平均只占图像的$1.79\%$，而细线子集在前景中的占比仅为$1.14\% \pm 0.41\%$。标准区域指标（F1, IoU）可能会掩盖细线分割错误，因为绝大多数背景占据了评分的主要部分。相比之下，增加边界感知度的评估指标和对细线子集公平性的分析改变了损失函数的排序方式，并揭示了隐藏的权衡。我们提出了一种评估协议，在种子多轮训练中同时考察区域指标、边界指标（BF1, B-IoU）、核心/细线子集公平性分析以及每张图片的鲁棒统计量（中位数，IQR，最坏情况），并通过非参数显著性测试进行验证。五种损失函数——交叉熵、焦点、Dice、Dice+焦点和Tversky，在DeepLabV3-MobileNetV3模型上各自训练三次，并在12张保留的图像（分成核心集和细线子集）上进行了评估。基于重叠的损失函数使F1得分相对于交叉熵提高了超过20个百分点（$0.663$ 对比 $0.438$，$p < 0.001$）。此外，边界指标证实这种增益扩展到了轮廓精度。自适应阈值和Sauvola二值化在原生分辨率下实现了更高的平均F1得分（Sauvola的平均F1得分为$0.787$），但最坏情况下的性能表现明显较差（F1得分从$0.452$ 降到 $0.565$，对应于Tversky模型）。这揭示了一个一致性与准确性之间的权衡：经典基线在平均F1得分上处于领先地位，而学习型模型则提供了更高的最坏情况下的可靠性。将训练分辨率加倍进一步提高了F1得分为$12.7$个百分点。
URL
https://arxiv.org/abs/2603.00163
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AutoSew: A Geometric Approach to Stitching Prediction with Graph Neural Networks
2026-02-25 16:03:33Pablo R\'ios-Navarro, Elena Garces, Jorge Lopez-MorenoAbstract
Automating garment assembly from sewing patterns remains a significant challenge due to the lack of standardized annotation protocols and the frequent absence of semantic cues. Existing methods often rely on panel labels or handcrafted heuristics, which limit their applicability to real-world, non-conforming patterns. We present AutoSew, a fully automatic, geometry-based approach for predicting stitch correspondences directly from 2D pattern contours. AutoSew formulates the problem as a graph matching task, leveraging a Graph Neural Network to capture local and global geometric context, and employing a differentiable optimal transport solver to infer stitching relationships-including multi-edge connections. To support this task, we update the GarmentCodeData dataset modifying over 18k patterns with realistic multi-edge annotations, reflecting industrial assembly scenarios. AutoSew achieves 96% F1-score and successfully assembles 73.3% of test garments without error, outperforming existing methods while relying solely on geometric input. Our results demonstrate that geometry alone can robustly guide stitching prediction, enabling scalable garment assembly without manual input.
Abstract (translated)
从裁剪图案自动组装服装仍然是一个重大挑战，因为缺乏标准化的标注协议以及语义线索的频繁缺失。现有方法通常依赖于面板标签或手工制作的经验法则，这限制了它们在处理现实世界中不规则图案时的应用能力。我们提出了AutoSew，这是一个完全自动化的基于几何的方法，可以从2D图案轮廓直接预测缝合对应关系。AutoSew将问题表述为图匹配任务，利用图神经网络捕捉局部和全局的几何上下文，并采用可微分最优传输求解器来推断包括多边连接在内的缝合关系。 为了支持这一任务，我们更新了GarmentCodeData数据集，修改超过18,000个图案并添加了现实主义的多边注释，反映工业组装场景。AutoSew达到了96%的F1分数，并成功地将测试服装中的73.3%无误地组合在一起，在仅依赖几何输入的情况下超越了现有方法。 我们的结果表明，仅凭几何信息就可以稳健地指导缝合预测，使没有手动干预的大规模服装组装成为可能。
URL
https://arxiv.org/abs/2602.22052
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SPRITETOMESH: Automatic Mesh Generation for 2D Skeletal Animation Using Learned Segmentation and Contour-Aware Vertex Placement
2026-02-24 17:58:31Bastien GimbertAbstract
We present SPRITETOMESH, a fully automatic pipeline for converting 2D game sprite images into triangle meshes compatible with skeletal animation frameworks such as Spine2D. Creating animation-ready meshes is traditionally a tedious manual process requiring artists to carefully place vertices along visual boundaries, a task that typically takes 15-60 minutes per sprite. Our method addresses this through a hybrid learned-algorithmic approach. A segmentation network (EfficientNet-B0 encoder with U-Net decoder) trained on over 100,000 sprite-mask pairs from 172 games achieves an IoU of 0.87, providing accurate binary masks from arbitrary input images. From these masks, we extract exterior contour vertices using Douglas-Peucker simplification with adaptive arc subdivision, and interior vertices along visual boundaries detected via bilateral-filtered multi-channel Canny edge detection with contour-following placement. Delaunay triangulation with mask-based centroid filtering produces the final mesh. Through controlled experiments, we demonstrate that direct vertex position prediction via neural network heatmap regression is fundamentally not viable for this task: the heatmap decoder consistently fails to converge (loss plateau at 0.061) while the segmentation decoder trains normally under identical conditions. We attribute this to the inherently artistic nature of vertex placement - the same sprite can be meshed validly in many different ways. This negative result validates our hybrid design: learned segmentation where ground truth is unambiguous, algorithmic placement where domain heuristics are appropriate. The complete pipeline processes a sprite in under 3 seconds, representing a speedup of 300x-1200x over manual creation. We release our trained model to the game development community.
Abstract (translated)
我们介绍了SPRITETOMESH，这是一种全自动流程，用于将2D游戏精灵图像转换为与Spine2D等骨骼动画框架兼容的三角网格。传统的做法是手动创建适合动画制作的网格，这个过程既耗时又繁琐，需要艺术家仔细地沿着视觉边界放置顶点，通常每个精灵花费15到60分钟的时间。我们的方法通过混合学习算法的方法解决了这个问题。 该流程包括一个经过训练的分割网络（使用EfficientNet-B0编码器和U-Net解码器），它在一个包含超过10万对游戏遮罩图像的数据库上进行训练，涉及来自172个不同游戏的数据集，获得了0.87的交并比（IoU），能够从任意输入图像中生成准确的二值掩膜。基于这些掩膜，我们利用道格拉斯-普克简化算法结合自适应弧细分来提取外部轮廓顶点，并通过双边滤波多通道Canny边缘检测与跟踪放置方法沿着视觉边界找到内部顶点。最后，在使用遮罩中心点过滤的基础上进行Delaunay三角剖分生成最终网格。 在受控实验中，我们证明了直接通过神经网络热图回归预测顶点位置对于此任务是不可行的：在相同的条件下，热图解码器始终无法收敛（损失函数停滞在0.061），而分割解码器正常训练。我们认为这源于顶点放置本质上是一种艺术性的过程——同一个精灵可以通过多种不同的方式被合理地网格化。这一结果验证了我们混合设计的有效性：对于地面实况明确的情况下采用学习分割方法，而对于领域启发式适用的场景则使用算法放置的方法。 整个流程可以在不到3秒内处理完一个精灵图像，相比于手动创建而言速度提高了300到1200倍。我们向游戏开发社区开放了训练好的模型。
URL
https://arxiv.org/abs/2602.21153
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Accurate Planar Tracking With Robust Re-Detection
2026-02-23 09:13:55Jonas Serych, Jiri MatasAbstract
We present SAM-H and WOFTSAM, novel planar trackers that combine robust long-term segmentation tracking provided by SAM 2 with 8 degrees-of-freedom homography pose estimation. SAM-H estimates homographies from segmentation mask contours and is thus highly robust to target appearance changes. WOFTSAM significantly improves the current state-of-the-art planar tracker WOFT by exploiting lost target re-detection provided by SAM-H. The proposed methods are evaluated on POT-210 and PlanarTrack tracking benchmarks, setting the new state-of-the-art performance on both. On the latter, they outperform the second best by a large margin, +12.4 and +15.2pp on the p@15 metric. We also present improved ground-truth annotations of initial PlanarTrack poses, enabling more accurate benchmarking in the high-precision p@5 metric. The code and the re-annotations are available at this https URL
Abstract (translated)
我们介绍了SAM-H和WOFTSAM，这两种是结合了由SAM 2提供的稳健长期分割跟踪与8自由度单应性姿态估计的新型平面追踪器。SAM-H通过从分割掩模轮廓中估算出单应矩阵，因此对目标外观变化具有高度鲁棒性。WOFTSAM通过利用SAM-H提供的丢失目标重新检测功能，显著提升了当前最先进的平面追踪器WOFT的表现。 我们提出的这些方法在POT-210和PlanarTrack追踪基准测试上进行了评估，并在这两个基准测试中均设立了新的性能标杆。特别是在后者中，它们超越了第二名一大截，在p@15指标上分别高出+12.4和+15.2个百分点。 此外，我们还提供了改进后的初始PlanarTrack姿态的地面真实注释，这使得在高精度的p@5度量标准下进行更准确的基准测试成为可能。代码以及重新标注的数据可在以下网址获得：[此处应提供链接]（请根据实际情况填写具体URL）。
URL
https://arxiv.org/abs/2602.19624
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FoundationPose-Initialized 3D-2D Liver Registration for Surgical Augmented Reality
2026-02-19 16:31:14Hanyuan Zhang, Lucas He, Runlong He, Abdolrahim Kadkhodamohammadi, Danail Stoyanov, Brian R. Davidson, Evangelos B. Mazomenos, Matthew J. ClarksonAbstract
Augmented reality can improve tumor localization in laparoscopic liver surgery. Existing registration pipelines typically depend on organ contours; deformable (non-rigid) alignment is often handled with finite-element (FE) models coupled to dimensionality-reduction or machine-learning components. We integrate laparoscopic depth maps with a foundation pose estimator for camera-liver pose estimation and replace FE-based deformation with non-rigid iterative closest point (NICP) to lower engineering/modeling complexity and expertise requirements. On real patient data, the depth-augmented foundation pose approach achieved 9.91 mm mean registration error in 3 cases. Combined rigid-NICP registration outperformed rigid-only registration, demonstrating NICP as an efficient substitute for finite-element deformable models. This pipeline achieves clinically relevant accuracy while offering a lightweight, engineering-friendly alternative to FE-based deformation.
Abstract (translated)
URL
https://arxiv.org/abs/2602.17517
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Breaking the Sub-Millimeter Barrier: Eyeframe Acquisition from Color Images
Abstract
Eyeframe lens tracing is an important process in the optical industry that requires sub-millimeter precision to ensure proper lens fitting and optimal vision correction. Traditional frame tracers rely on mechanical tools that need precise positioning and calibration, which are time-consuming and require additional equipment, creating an inefficient workflow for opticians. This work presents a novel approach based on artificial vision that utilizes multi-view information. The proposed algorithm operates on images captured from an InVision system. The full pipeline includes image acquisition, frame segmentation to isolate the eyeframe from background, depth estimation to obtain 3D spatial information, and multi-view processing that integrates segmented RGB images with depth data for precise frame contour measurement. To this end, different configurations and variants are proposed and analyzed on real data, providing competitive measurements from still color images with respect to other solutions, while eliminating the need for specialized tracing equipment and reducing workflow complexity for optical technicians.
Abstract (translated)
URL
https://arxiv.org/abs/2602.16281
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Quantifying LLM Attention-Head Stability: Implications for Circuit Universality
2026-02-17 23:30:59Karan Bali, Jack Stanley, Praneet Suresh, Danilo BzdokAbstract
In mechanistic interpretability, recent work scrutinizes transformer "circuits" - sparse, mono or multi layer sub computations, that may reflect human understandable functions. Yet, these network circuits are rarely acid-tested for their stability across different instances of the same deep learning architecture. Without this, it remains unclear whether reported circuits emerge universally across labs or turn out to be idiosyncratic to a particular estimation instance, potentially limiting confidence in safety-critical settings. Here, we systematically study stability across-refits in increasingly complex transformer language models of various sizes. We quantify, layer by layer, how similarly attention heads learn representations across independently initialized training runs. Our rigorous experiments show that (1) middle-layer heads are the least stable yet the most representationally distinct; (2) deeper models exhibit stronger mid-depth divergence; (3) unstable heads in deeper layers become more functionally important than their peers from the same layer; (4) applying weight decay optimization substantially improves attention-head stability across random model initializations; and (5) the residual stream is comparatively stable. Our findings establish the cross-instance robustness of circuits as an essential yet underappreciated prerequisite for scalable oversight, drawing contours around possible white-box monitorability of AI systems.
Abstract (translated)
URL
https://arxiv.org/abs/2602.16740
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How to Sample High Quality 3D Fractals for Action Recognition Pre-Training?
2026-02-12 10:48:25Marko Putak, Thomas B. Moeslund, Joakim Bruslund HaurumAbstract
Synthetic datasets are being recognized in the deep learning realm as a valuable alternative to exhaustively labeled real data. One such synthetic data generation method is Formula Driven Supervised Learning (FDSL), which can provide an infinite number of perfectly labeled data through a formula driven approach, such as fractals or contours. FDSL does not have common drawbacks like manual labor, privacy and other ethical concerns. In this work we generate 3D fractals using 3D Iterated Function Systems (IFS) for pre-training an action recognition model. The fractals are temporally transformed to form a video that is used as a pre-training dataset for downstream task of action recognition. We find that standard methods of generating fractals are slow and produce degenerate 3D fractals. Therefore, we systematically explore alternative ways of generating fractals and finds that overly-restrictive approaches, while generating aesthetically pleasing fractals, are detrimental for downstream task performance. We propose a novel method, Targeted Smart Filtering, to address both the generation speed and fractal diversity issue. The method reports roughly 100 times faster sampling speed and achieves superior downstream performance against other 3D fractal filtering methods.
Abstract (translated)
合成数据在深度学习领域中被认为是一种有价值的现实标记数据的替代方案。其中一种生成合成数据的方法是公式驱动监督学习（Formula Driven Supervised Learning，FDSL），它可以通过公式驱动的方式，如分形或轮廓，提供无限量的完美标注数据，而无需人工劳动，并且避免了隐私和其他伦理问题。在这项工作中，我们使用3D迭代函数系统（IFS）生成3D分形来预训练动作识别模型。这些分形在时间上变换形成视频，作为下游动作识别任务的预训练数据集。 我们发现传统的生成分形的方法速度慢，并且产生的三维分形质量较差。因此，我们系统地探索了替代方法以生成分形，结果发现过于严格的限制性方法虽然可以产生美观的分形图像，但对下游任务性能有害。为此，我们提出了一种新的方法——目标智能过滤法（Targeted Smart Filtering），旨在同时解决生成速度和分形多样性的问题。该方法报告称采样速度快了大约100倍，并且在与其他3D分形筛选方法相比时，在下游任务中表现出色。
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https://arxiv.org/abs/2602.11810
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Quantum walk inspired JPEG compression of images
2026-02-12 05:40:33Abhishek Verma, Sahil Tomar, Sandeep KumarAbstract
This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a continuous parameter space of frequency band scaling factors under a unified rate distortion objective that jointly considers reconstruction fidelity and compression efficiency. The proposed framework is evaluated on MNIST, CIFAR10, and ImageNet subsets, using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Bits Per Pixel (BPP), and error heatmap visual analysis as evaluation metrics. Experimental results show average gains ranging from 3 to 6 dB PSNR, along with better structural preservation of edges, contours, and luminance transitions, without modifying decoder compatibility. The structure remains JPEG compliant and can be implemented using accessible scientific packages making it ideal for deployment and practical research use.
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https://arxiv.org/abs/2602.12306
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Strategizing at Speed: A Learned Model Predictive Game for Multi-Agent Drone Racing
2026-02-06 18:20:13Andrei-Carlo Papuc, Lasse Peters, Sihao Sun, Laura Ferranti, Javier Alonso-MoraAbstract
Autonomous drone racing pushes the boundaries of high-speed motion planning and multi-agent strategic decision-making. Success in this domain requires drones not only to navigate at their limits but also to anticipate and counteract competitors' actions. In this paper, we study a fundamental question that arises in this domain: how deeply should an agent strategize before taking an action? To this end, we compare two planning paradigms: the Model Predictive Game (MPG), which finds interaction-aware strategies at the expense of longer computation times, and contouring Model Predictive Control (MPC), which computes strategies rapidly but does not reason about interactions. We perform extensive experiments to study this trade-off, revealing that MPG outperforms MPC at moderate velocities but loses its advantage at higher speeds due to latency. To address this shortcoming, we propose a Learned Model Predictive Game (LMPG) approach that amortizes model predictive gameplay to reduce latency. In both simulation and hardware experiments, we benchmark our approach against MPG and MPC in head-to-head races, finding that LMPG outperforms both baselines.
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自主无人机竞速推动了高速运动规划和多智能体策略决策的边界。在这个领域取得成功需要无人机不仅要在极限条件下导航，还要能够预测并应对竞争对手的动作。本文研究了一个在该领域中出现的基本问题：一个智能体在采取行动之前应深入思考到什么程度？为此，我们比较了两种规划范式：模型预测博弈（Model Predictive Game, MPG），它以更长的计算时间为代价找到考虑到互动策略的方法；以及轮廓模型预测控制（contouring Model Predictive Control, MPC），这种方法可以快速制定策略但不考虑交互。通过广泛的实验来研究这一权衡，我们发现MPG在中等速度下优于MPC，但在高速时由于延迟问题而失去优势。为解决这个问题，我们提出了一种学习模型预测博弈（Learned Model Predictive Game, LMPG）方法，该方法将模型预测游戏进行分摊处理以减少延迟。通过模拟和硬件实验，在一对一竞速中将我们的方法与MPG和MPC进行了基准测试，发现LMPG优于两个基线方法。 这段翻译阐明了自主无人机竞速中的策略制定问题，并比较了几种不同的规划方法的效果，突出了所提出的LMPG方法在减少延迟的同时维持性能方面的优势。
URL
https://arxiv.org/abs/2602.06925
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Contour Refinement using Discrete Diffusion in Low Data Regime
2026-02-05 16:55:08Fei Yu Guan, Ian Keefe, Sophie Wilkinson, Daniel D. B. Perrakis, Steven WaslanderAbstract
Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and manufacturing, where many of these applications are plagued with scarce labeled data and low in situ computational resources. While recent image segmentation studies focus on segmentation mask alignment with ground-truth, the task of boundary detection remains understudied, especially in the low data regime. In this work, we present a lightweight discrete diffusion contour refinement pipeline for robust boundary detection in the low data regime. We use a Convolutional Neural Network(CNN) architecture with self-attention layers as the core of our pipeline, and condition on a segmentation mask, iteratively denoising a sparse contour representation. We introduce multiple novel adaptations for improved low-data efficacy and inference efficiency, including using a simplified diffusion process, a customized model architecture, and minimal post processing to produce a dense, isolated contour given a dataset of size <500 training images. Our method outperforms several SOTA baselines on the medical imaging dataset KVASIR, is competitive on HAM10K and our custom wildfire dataset, Smoke, while improving inference framerate by 3.5X.
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不规则和透明物体的边界检测是一个重要的问题，它在医学成像、环境监测及制造业中有着广泛的应用。然而，在这些领域中，许多应用由于缺乏标注数据且现场计算资源有限而面临挑战。尽管最近的图像分割研究主要集中在与地面真实值的分割掩码对齐上，但低数据环境下边界检测的任务仍然较少被探索。在这项工作中，我们提出了一种轻量级离散扩散轮廓细化流水线，用于在低数据情况下进行稳健的边界检测。 我们的方法采用带有自注意力层的卷积神经网络（CNN）架构作为核心，并基于分割掩码条件，迭代地去噪稀疏轮廓表示。为了提高低数据下的有效性和推理效率，我们引入了多种新颖的适应性改进措施，包括简化扩散过程、定制模型架构以及最小化后处理步骤，在仅使用少于500张训练图像的数据集上产生稠密且独立的轮廓。 在医学成像数据集KVASIR上，我们的方法优于多个最先进的基线。同时，在HAM10K和我们自定义的野火数据集Smoke上的性能也具有竞争力，并且推理帧率提高了3.5倍。
URL
https://arxiv.org/abs/2602.05880
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Disco: Densely-overlapping Cell Instance Segmentation via Adjacency-aware Collaborative Coloring
2026-02-05 08:05:48Rui Sun, Yiwen Yang, Kaiyu Guo, Chen Jiang, Dongli Xu, Zhaonan Liu, Tan Pan, Limei Han, Xue Jiang, Wu Wei, Yuan ChengAbstract
Accurate cell instance segmentation is foundational for digital pathology analysis. Existing methods based on contour detection and distance mapping still face significant challenges in processing complex and dense cellular regions. Graph coloring-based methods provide a new paradigm for this task, yet the effectiveness of this paradigm in real-world scenarios with dense overlaps and complex topologies has not been verified. Addressing this issue, we release a large-scale dataset GBC-FS 2025, which contains highly complex and dense sub-cellular nuclear arrangements. We conduct the first systematic analysis of the chromatic properties of cell adjacency graphs across four diverse datasets and reveal an important discovery: most real-world cell graphs are non-bipartite, with a high prevalence of odd-length cycles (predominantly triangles). This makes simple 2-coloring theory insufficient for handling complex tissues, while higher-chromaticity models would cause representational redundancy and optimization difficulties. Building on this observation of complex real-world contexts, we propose Disco (Densely-overlapping Cell Instance Segmentation via Adjacency-aware COllaborative Coloring), an adjacency-aware framework based on the "divide and conquer" principle. It uniquely combines a data-driven topological labeling strategy with a constrained deep learning system to resolve complex adjacency conflicts. First, "Explicit Marking" strategy transforms the topological challenge into a learnable classification task by recursively decomposing the cell graph and isolating a "conflict set." Second, "Implicit Disambiguation" mechanism resolves ambiguities in conflict regions by enforcing feature dissimilarity between different instances, enabling the model to learn separable feature representations.
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准确的细胞实例分割是数字病理学分析的基础。现有的基于轮廓检测和距离映射的方法在处理复杂且密集的细胞区域时仍然面临重大挑战。基于图着色的方法为这一任务提供了一种新的范式，但在现实场景中，尤其是在存在大量重叠和复杂拓扑结构的情况下，这种方法的有效性尚未得到验证。 为了应对这一问题，我们发布了大规模数据集GBC-FS 2025，该数据集中包含了高度复杂的密集亚细胞核排列。我们在四个不同的数据集上进行了首次关于细胞邻接图的色度特性的系统分析，并揭示了一个重要的发现：大多数现实世界的细胞图是非二部图，且含有大量的奇数长度循环（主要是三角形）。这表明简单的二着色理论不足以处理复杂组织的问题，而更高色度的模型会导致表示冗余和优化困难。 基于对这种复杂现实环境的理解，我们提出了一种新的方法Disco（密集重叠细胞实例分割通过邻接感知协作着色），这是一种基于“分治”原则并利用邻接信息的框架。这种方法独特地结合了数据驱动拓扑标签策略与受约束的深度学习系统，以解决复杂的邻接冲突问题。 首先，“显式标记”策略通过递归分解细胞图并将“冲突集”隔离出来，将拓扑挑战转化为可学习分类任务。 其次，“隐式消歧机制”通过强制不同实例之间的特征差异性来解析冲突区域中的模糊性，使模型能够学习到分离的特征表示。
URL
https://arxiv.org/abs/2602.05420
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Multi-head automated segmentation by incorporating detection head into the contextual layer neural network
2026-02-02 18:51:25Edwin Kys, Febian FebianAbstract
Deep learning based auto segmentation is increasingly used in radiotherapy, but conventional models often produce anatomically implausible false positives, or hallucinations, in slices lacking target structures. We propose a gated multi-head Transformer architecture based on Swin U-Net, augmented with inter-slice context integration and a parallel detection head, which jointly performs slice-level structure detection via a multi-layer perceptron and pixel-level segmentation through a context-enhanced stream. Detection outputs gate the segmentation predictions to suppress false positives in anatomically invalid slices, and training uses slice-wise Tversky loss to address class imbalance. Experiments on the Prostate-Anatomical-Edge-Cases dataset from The Cancer Imaging Archive demonstrate that the gated model substantially outperforms a non-gated segmentation-only baseline, achieving a mean Dice loss of $0.013 \pm 0.036$ versus $0.732 \pm 0.314$, with detection probabilities strongly correlated with anatomical presence, effectively eliminating spurious segmentations. In contrast, the non-gated model exhibited higher variability and persistent false positives across all slices. These results indicate that detection-based gating enhances robustness and anatomical plausibility in automated segmentation applications, reducing hallucinated predictions without compromising segmentation quality in valid slices, and offers a promising approach for improving the reliability of clinical radiotherapy auto-contouring workflows.
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URL
https://arxiv.org/abs/2602.02471
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SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
2026-01-31 05:12:17Kunal Mahatha, Jose Dolz, Christian DesrosiersAbstract
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-derived affinities. Such approaches, based on global graph partitioning and eigenvector-based formulations of affinity matrices, suffer from several fundamental drawbacks, they require pre-selecting the number of clusters, induce boundary oversmoothing due to spectral relaxation, and remain highly sensitive to noisy or multi-modal affinity distributions. Moreover, many prior works neglect the importance of local neighborhood structure, which plays a crucial role in stabilizing affinity propagation and preserving fine-grained contours. To address these limitations, we reformulate training-free segmentation as a stochastic flow equilibrium problem over diffusion-induced affinity graphs, where segmentation emerges from a stochastic propagation process that integrates global diffusion attention with local neighborhoods extracted from stable diffusion, yielding a sparse yet expressive affinity structure. Building on this formulation, we introduce a Markov propagation scheme that performs random-walk-based label diffusion with an adaptive pruning strategy that suppresses unreliable transitions while reinforcing confident affinity paths. Experiments across seven widely used semantic segmentation benchmarks demonstrate that our method achieves state-of-the-art zero-shot performance, producing sharper boundaries, more coherent regions, and significantly more stable masks compared to prior spectral-clustering-based approaches.
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URL
https://arxiv.org/abs/2602.00516
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Advanced Geometric Correction Algorithms for 3D Medical Reconstruction: Comparison of Computed Tomography and Macroscopic Imaging
2026-01-30 17:16:17Tomasz Les, Tomasz Markiewicz, Malgorzata Lorent, Miroslaw Dziekiewicz, Krzysztof SiwekAbstract
This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging, where fully learning-based registration (e.g., VoxelMorph) often fails to generalize due to limited training diversity and large nonrigid deformations that exceed the capture range of unconstrained convolutional filters. In the proposed pipeline, the Optimal Cross-section Matching (OCM) algorithm first performs constrained global alignment: translation, rotation, and uniform scaling to establish anatomically consistent slice initialization. Next, a lightweight deep-learning refinement network, inspired by VoxelMorph, predicts residual local deformations between consecutive slices. The core novelty of this architecture lies in its hierarchical decomposition of the registration manifold. This hybrid OCM+DL design integrates explicit geometric priors with the flexible learning capacity of neural networks, ensuring stable optimization and plausible deformation fields even with few training examples. Experiments on an original dataset of 40 kidneys demonstrated better results compared to single-stage baselines. The pipeline maintains physical calibration via Hough-based grid detection and employs Bezier-based contour smoothing for robust meshing and volume estimation. Although validated on kidney data, the proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections. By decoupling interpretable global optimization from data-efficient deep refinement, the method advances the precision, reproducibility, and anatomical realism of multimodal 3D reconstructions for surgical planning, morphological assessment, and medical education.
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URL
https://arxiv.org/abs/2602.00220
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Towards Pixel-Level VLM Perception via Simple Points Prediction
2026-01-27 05:50:40Tianhui Song, Haoyu Lu, Hao Yang, Lin Sui, Haoning Wu, Zaida Zhou, Zhiqi Huang, Yiping Bao, Y. Charles, Xinyu Zhou, Limin WangAbstract
We present SimpleSeg, a strikingly simple yet highly effective approach to endow Multimodal Large Language Models (MLLMs) with native pixel-level perception. Our method reframes segmentation as a simple sequence generation problem: the model directly predicts sequences of points (textual coordinates) delineating object boundaries, entirely within its language space. To achieve high fidelity, we introduce a two-stage SF$\to$RL training pipeline, where Reinforcement Learning with an IoU-based reward refines the point sequences to accurately match ground-truth contours. We find that the standard MLLM architecture possesses a strong, inherent capacity for low-level perception that can be unlocked without any specialized architecture. On segmentation benchmarks, SimpleSeg achieves performance that is comparable to, and often surpasses, methods relying on complex, task-specific designs. This work lays out that precise spatial understanding can emerge from simple point prediction, challenging the prevailing need for auxiliary components and paving the way for more unified and capable VLMs. Homepage: this https URL
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URL
https://arxiv.org/abs/2601.19228
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FBSDiff++: Improved Frequency Band Substitution of Diffusion Features for Efficient and Highly Controllable Text-Driven Image-to-Image Translation
2026-01-27 02:39:20Xiang Gao, Yunpeng JiaAbstract
With large-scale text-to-image (T2I) diffusion models achieving significant advancements in open-domain image creation, increasing attention has been focused on their natural extension to the realm of text-driven image-to-image (I2I) translation, where a source image acts as visual guidance to the generated image in addition to the textual guidance provided by the text prompt. We propose FBSDiff, a novel framework adapting off-the-shelf T2I diffusion model into the I2I paradigm from a fresh frequency-domain perspective. Through dynamic frequency band substitution of diffusion features, FBSDiff realizes versatile and highly controllable text-driven I2I in a plug-and-play manner (without need for model training, fine-tuning, or online optimization), allowing appearance-guided, layout-guided, and contour-guided I2I translation by progressively substituting low-frequency band, mid-frequency band, and high-frequency band of latent diffusion features, respectively. In addition, FBSDiff flexibly enables continuous control over I2I correlation intensity simply by tuning the bandwidth of the substituted frequency band. To further promote image translation efficiency, flexibility, and functionality, we propose FBSDiff++ which improves upon FBSDiff mainly in three aspects: (1) accelerate inference speed by a large margin (8.9$\times$ speedup in inference) with refined model architecture; (2) improve the Frequency Band Substitution module to allow for input source images of arbitrary resolution and aspect ratio; (3) extend model functionality to enable localized image manipulation and style-specific content creation with only subtle adjustments to the core method. Extensive qualitative and quantitative experiments verify superiority of FBSDiff++ in I2I translation visual quality, efficiency, versatility, and controllability compared to related advanced approaches.
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URL
https://arxiv.org/abs/2601.19115
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