Computer Vision
Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification
Z. Rozsa, Á. Madaras, Q. Wei et al. (+5 more)
LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, pedestrians, and traffic signs. While this dense data enables accurate perception, it also increases computational cost and power consumption, which can limit real-time deployment. Existing point cloud sampling methods typically face a trade-off: very fast approaches tend to reduce accuracy, while more accurate methods are computationally expensive. To address this limitation, we propose an efficient learned point cloud simplification method for
LiDAR data. The method combines a feature
embedding module with an
attention-based sampling module to prioritize task-relevant regions and is trained end-to-end. We evaluate the method against farthest point sampling (FPS) and random sampling (RS) on 3D object detection on the KITTI
dataset and on object classification across four
datasets. The method was consistently faster than FPS and achieved similar, and in some settings better, accuracy, with the largest gains under aggressive downsampling. It was slower than RS, but it typically preserved accuracy more reliably at high sampling ratios.
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All authors Z. Rozsa, Á. Madaras, Q. Wei, X. Lu, M. Golarits, H. Yuan, T. Sziranyi, R. Hamzaoui
Computer Vision
Models as Lego Builders: Assembling Malice from Benign Blocks via Semantic Blueprints
Chenxi Li, Xianggan Liu, Dake Shen et al. (+9 more)
Despite the rapid progress of Large
Vision-
Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that adversaries can exploit to elicit biased or malicious outputs. In this paper, we demonstrate an underexplored vulnerability via semantic slot filling, where LVLMs complete missing slot values with unsafe content even when the slot types are deliberately crafted to appear benign. Building on this finding, we propose StructAttack, a simple yet effective single-query jailbreak framework under black-box settings. StructAttack decomposes a harmful query into a central topic and a set of benign-looking slot types, then embeds them as structured visual
prompts (e.g., mind maps, tables, or sunburst diagrams) with small random perturbations. Paired with a completion-guided instruction, LVLMs automatically recompose the concealed semantics and generate unsafe outputs without triggering safety mechanisms. Although each slot appears benign in isolation (local benignness), StructAttack exploits LVLMs'
reasoning to assemble these slots into coherent harmful semantics. Extensive experiments on multiple models and
benchmarks show the efficacy of our proposed StructAttack.
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All authors Chenxi Li, Xianggan Liu, Dake Shen, Yaosong Du, Zhibo Yao, Hao Jiang, Linyi Jiang, Chengwei Cao, Jingzhe Zhang, RanYi Peng, Peiling Bai, Xiande Huang
Computer Vision
3DGS-HPC: Distractor-free 3D Gaussian Splatting with Hybrid Patch-wise Classification
Jiahao Chen, Yipeng Qin, Ganlong Zhao et al. (+3 more)
3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and 3D scene reconstruction, but its quality often degrades in real-world environments due to transient distractors, such as moving objects and varying shadows. Existing methods commonly introduce semantic priors from pre-trained
vision models either to group pixels into coherent regions or to define perceptual error metrics. However, semantic grouping is often misaligned with the binary static/transient distinction, while perceptual features can be fragile under appearance perturbations introduced during 3DGS optimization. We propose 3DGS-HPC, a framework that addresses these issues by combining two complementary principles: a patch-wise classification strategy that leverages local spatial consistency for robust region-level decisions, and a hybrid classification metric that adaptively integrates photometric and perceptual cues for more reliable separation. Extensive experiments demonstrate the superiority and robustness of our method in mitigating distractors to improve 3DGS-based novel view synthesis. Our project page is https://cnhaox.github.io/3DGS-HPC/ .
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All authors Jiahao Chen, Yipeng Qin, Ganlong Zhao, Xin Li, Wenping Wang, Guanbin Li
Computer Vision
Integration of deep generative Anomaly Detection algorithm in high-speed industrial line
Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli et al. (+2 more)
Industrial visual inspection in pharmaceutical production requires high accuracy under strict constraints on cycle time, hardware footprint, and operational cost. Manual inline inspection is still common, but it is affected by operator variability and limited throughput. Classical rule-based computer
vision pipelines are often rigid and difficult to scale to highly variable production scenarios. To address these limitations, we present a semi-supervised anomaly detection framework based on a generative
adversarial architecture with a residual autoencoder and a dense bottleneck, specifically designed for online deployment on a high-speed Blow-Fill-Seal (BFS) line. The model is trained only on nominal samples and detects anomalies through reconstruction residuals, providing both classification and spatial localization via heatmaps. The training set contains 2,815,200 grayscale patches. Experiments on a real industrial test kit show high detection performance while satisfying timing constraints compatible with a 500 ms acquisition slot.
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All authors Niccolò Ferrari, Nicola Zanarini, Michele Fraccaroli, Alice Bizzarri, Evelina Lamma
Computer Vision
A Systematic Comparison of Training Objectives for Out-of-Distribution Detection in Image Classification
Furkan Genç, Onat Özdemir and Emre Akbaş
Out-of-distribution (OOD) detection is critical in safety-sensitive applications. While this challenge has been addressed from various perspectives, the influence of training objectives on OOD behavior remains comparatively underexplored. In this paper, we present a systematic comparison of four widely used training objectives: Cross-
Entropy Loss, Prototype Loss, Triplet Loss, and Average Precision (AP) Loss, spanning probabilistic, prototype-based, metric-learning, and ranking-based supervision, for OOD detection in image classification under standardized OpenOOD protocols. Within the evaluated ResNet-18/OpenOOD setting and objective-specific OOD scoring rules, Cross-
Entropy Loss, Prototype Loss, and AP Loss achieve comparable in-distribution accuracy, while Cross-
Entropy Loss provides the most consistent near- and far-OOD AUROC overall; the other objectives can be competitive in specific settings.
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Computer Vision
Efficient RGB-D Scene Understanding via Multi-task Adaptive Learning and Cross-dimensional Feature Guidance
Guodong Sun, Junjie Liu, Gaoyang Zhang et al. (+2 more)
Scene understanding plays a critical role in enabling intelligence and autonomy in robotic systems. Traditional approaches often face challenges, including occlusions, ambiguous boundaries, and the inability to adapt
attention based on task-specific requirements and sample variations. To address these limitations, this paper presents an efficient RGB-D scene understanding model that performs a range of tasks, including semantic
segmentation, instance
segmentation, orientation estimation, panoptic
segmentation, and scene classification. The proposed model incorporates an enhanced fusion encoder, which effectively leverages redundant information from both RGB and depth inputs. For semantic
segmentation, we introduce normalized focus channel layers and a context feature interaction layer, designed to mitigate issues such as shallow feature misguidance and insufficient local-global feature representation. The instance
segmentation task benefits from a non-bottleneck 1D structure, which achieves superior contour representation with fewer parameters. Additionally, we propose a multi-task adaptive loss function that dynamically adjusts the learning strategy for different tasks based on scene variations. Extensive experiments on the NYUv2, SUN RGB-D, and Cityscapes
datasets demonstrate that our approach outperforms existing methods in both
segmentation accuracy and processing speed.
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All authors Guodong Sun, Junjie Liu, Gaoyang Zhang, Bo Wu, Yang Zhang
Computer Vision
GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module
Niccolò Ferrari, Michele Fraccaroli and Evelina Lamma
Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task, that usually is achieved using a basic comparison between generated image and the original one, implementing some blob-analysis or image-editing
algorithms, in the post-processing step, which is very biased towards the source
dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a Generative
Adversarial Network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image
segmentation, spotting defects. This method learns from a
dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training
dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using pre-processing
algorithms, formerly developed with blob-analysis and image-editing procedures. To test our model we used challenging MVTec anomaly detection
datasets and an industrial large
dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network.
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Computer Vision
Geometric-Topological Perception and Motion Prior for Real-Time Satellite Video Object Tracking
Zixiao Wen, Guangyao Zhou, Jiawei Li et al. (+4 more)
Satellite
video object tracking (SVOT) remains fundamentally challenging due to texture scarcity, arbitrary rotation, aspect ratio changes, and severe occlusions. While recent state-of-the-art trackers excel in general scenarios, their reliance on rich appearance details or rigid spatial matching mechanisms leads to significant performance degradation in the satellite domain. To bridge this gap, we propose SiamGM, a real-time spatial-temporal unified tracking framework tailored for satellite
videos. Instead of conventionally stacking modules, we synergize
geometric-topological perception with temporal-kinematic prior, addressing the core limitations of SVOT. Spatially, we closely couple a Topological
Attention Module (TAM) with a Geometry-Constrained Label Assignment (GCLA) method during the training phase, where a Local Graph Propagation (LGP) mechanism enforces neighborhood-consistent template-search correspondences. This joint paradigm establishes fine-grained structural correspondences and explicitly suppresses surrounding background noise. Temporally, rather than passively relying on visual cues, we propose an Online Motion Model Refinement (OMMR) strategy during the tracking phase. Gated by an online self-calibrated confidence indicator, OMMR adaptively fuse long-term stable and short-term instantaneous motion estimation for robust tracking. Evaluations on two challenging SatSOT and SV248S
benchmarks confirm that SiamGM outperforms not only recent Siamese-based satellite trackers but also state-of-the-art
Transformer trackers in both precision and success metrics. Notably, this highly unified architecture introduces only minor computational overhead, enabling real-time tracking at 130 frames per second (FPS).
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All authors Zixiao Wen, Guangyao Zhou, Jiawei Li, Xiantai Xiang, Zhen Yang, Yuxin Hu, Yuhan Liu
Computer Vision
Brain-WM: Brain Glioblastoma World Model
Chenhui Wang, Boyun Zheng, Liuxin Bao et al. (+4 more)
Precise prognostic modeling of glioblastoma (GBM) under varying treatment interventions is essential for optimizing clinical outcomes. While generative AI has shown promise in simulating GBM
evolution, existing methods typically treat interventions as static conditional inputs rather than dynamic decision variables. Consequently, they fail to capture the complex, reciprocal interplay between tumor
evolution and treatment response. To bridge this gap, we present
Brain-WM, a pioneering
brain GBM world model that unifies next-step treatment prediction and future MRI generation, thereby capturing the co-evolutionary dynamics between tumor and treatment. Specifically,
Brain-WM encodes spatiotemporal dynamics into a shared latent space for joint autoregressive treatment prediction and flow-based future MRI generation. Then, instead of a conventional monolithic framework,
Brain-WM adopts a novel Y-shaped Mixture-of-
Transformers (MoT) architecture. This design structurally disentangles heterogeneous objectives, successfully leveraging cross-task synergies while preventing feature collapse. Finally, a synergistic multi-timepoint mask
alignment objective explicitly anchors latent representations to anatomically grounded tumor structures and progression-aware semantics. Extensive validation on internal and external multi-institutional cohorts demonstrates the superiority of
Brain-WM, achieving 91.5% accuracy in treatment planning and SSIMs of 0.8524, 0.8581, and 0.8404 for FLAIR, T1CE, and T2W sequences, respectively. Ultimately,
Brain-WM offers a robust clinical sandbox for optimizing patient healthcare. The source code is made available at https://github.com/thibault-wch/
Brain-GBM-world-model.
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All authors Chenhui Wang, Boyun Zheng, Liuxin Bao, Zhihao Peng, Peter Y.M. Woo, Hongming Shan, Yixuan Yuan
Computer Vision
PureCC: Pure Learning for Text-to-Image Concept Customization
Zhichao Liao, Xiaole Xian, Qingyu Li et al. (+7 more)
Existing concept customization methods have achieved remarkable outcomes in high-fidelity and multi-concept customization. However, they often neglect the influence on the original model's behavior and capabilities when learning new personalized concepts. To address this issue, we propose PureCC. PureCC introduces a novel decoupled learning objective for concept customization, which combines the implicit guidance of the target concept with the original conditional prediction. This separated form enables PureCC to substantially focus on the original model during training. Moreover, based on this objective, PureCC designs a dual-branch training pipeline that includes a frozen extractor providing purified target concept representations as implicit guidance and a trainable flow model producing the original conditional prediction, jointly achieving pure learning for personalized concepts. Furthermore, PureCC introduces a novel adaptive guidance scale $\lambda^\star$ to dynamically adjust the guidance strength of the target concept, balancing customization fidelity and model preservation. Extensive experiments show that PureCC achieves state-of-the-art performance in preserving the original behavior and capabilities while enabling high-fidelity concept customization. The code is available at https://github.com/lzc-sg/PureCC.
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All authors Zhichao Liao, Xiaole Xian, Qingyu Li, Wenyu Qin, Meng Wang, Weicheng Xie, Siyang Song, Pingfa Feng, Long Zeng, Liang Pan
Computer Vision
Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning
Weijia Feng, Jingyu Yang, Ruojia Zhang et al. (+7 more)
Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical monitoring. However, their low amplitude, short duration, and strong inter-subject variability make existing deep models prone to degradation under low-sample, noisy, and cross-subject conditions. This paper presents an active inference-based framework for micro-gesture recognition, featuring Expected Free Energy (EFE)-guided temporal sampling and uncertainty-aware adaptive learning. The model actively selects the most discriminative temporal segments under EFE guidance, enabling dynamic observation and information gain maximization. Meanwhile, sample weighting driven by predictive uncertainty mitigates the effects of label noise and distribution shift. Experiments on the SMG
dataset demonstrate the effectiveness of the proposed method, achieving consistent improvements across multiple mainstream backbones. Ablation studies confirm that both the EFE-guided observation and the adaptive learning mechanism are crucial to the performance gains. This work offers an interpretable and scalable paradigm for temporal behavior modeling under low-resource and noisy conditions, with broad applicability to wearable sensing, HCI, and clinical
emotion monitoring.
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All authors Weijia Feng, Jingyu Yang, Ruojia Zhang, Fengtao Sun, Qian Gao, Chenyang Wang, Tongtong Su, Jia Guo, Xiaobai Li, Minglai Shao
Computer Vision
ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction
Haibao Yu, Kuntao Xiao, Jiahang Wang et al. (+7 more)
High-fidelity visual reconstruction and novel-view synthesis are essential for realistic closed-loop evaluation in autonomous driving. While 4D Gaussian Splatting (4DGS) offers a promising balance of accuracy and efficiency, existing per-scene optimization methods require costly iterative refinement, rendering them unscalable for extensive urban environments. Conversely, current feed-forward approaches often suffer from degraded photometric quality. To address these limitations, we propose ReconDrive, a feed-forward framework that leverages and extends the 3D foundation model VGGT for rapid, high-fidelity 4DGS generation. Our architecture introduces two core adaptations to tailor the foundation model to dynamic driving scenes: (1) Hybrid Gaussian Prediction Heads, which decouple the regression of spatial coordinates and appearance attributes to overcome the photometric deficiencies inherent in generalized foundation features; and (2) a Static-Dynamic 4D Composition strategy that explicitly captures temporal motion via velocity modeling to represent complex dynamic environments. Benchmarked on nuScenes, ReconDrive significantly outperforms existing feed-forward baselines in reconstruction, novel-view synthesis, and 3D perception. It achieves performance competitive with per-scene optimization while being orders of magnitude faster, providing a scalable and practical solution for realistic driving simulation.
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All authors Haibao Yu, Kuntao Xiao, Jiahang Wang, Ruiyang Hao, Yuxin Huang, Guoran Hu, Haifang Qin, Bowen Jing, Yuntian Bo, Ping Luo
Computer Vision
DreamSAC: Learning Hamiltonian World Models via Symmetry Exploration
Jinzhou Tang, Fan Feng, Minghao Fu et al. (+3 more)
Learned world models excel at interpolative generalization but fail at extrapolative generalization to novel physical properties. This limitation arises because they learn statistical correlations rather than the environment's underlying generative rules, such as physical invariances and conservation laws. We argue that learning these invariances is key to robust extrapolation. To achieve this, we first introduce
Symmetry Exploration, an unsupervised exploration strategy where an
agent is intrinsically motivated by a
Hamiltonian-based curiosity bonus to actively probe and challenge its understanding of conservation laws, thereby collecting physically informative data. Second, we design a
Hamiltonian-based world model that learns from the collected data, using a novel self-supervised contrastive objective to identify the invariant physical state from raw, view-dependent pixel observations. Our framework, DreamSAC, trained on this actively curated data, significantly outperforms state-of-the-art baselines in 3D physics simulations on tasks requiring extrapolation.
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All authors Jinzhou Tang, Fan Feng, Minghao Fu, Wenjun Lin, Biwei Huang, Keze Wang
Computer Vision
CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization
Anh-Duy Le, Van-Linh Pham, Thanh-Nam Vo et al. (+2 more)
One-shot styled handwriting image generation, despite achieving impressive results in recent years, remains challenging due to the difficulty in capturing the intricate and diverse characteristics of human handwriting by using solely a single reference image. Existing methods still struggle to generate visually appealing and realistic handwritten images and adapt to complex, unseen writer styles, struggling to isolate invariant style features (e.g., slant, stroke width,
curvature) while ignoring irrelevant noise. To tackle this problem, we introduce Patch Contrastive Enhancement and Style-Aware Quantization via Denoising
Diffusion (CONSTANT), a novel one-shot handwriting generation via
diffusion model. CONSTANT leverages three key innovations: 1) a Style-Aware Quantization (SAQ) module that models style as discrete visual
tokens capturing distinct concepts; 2) a contrastive objective to ensure these
tokens are well-separated and meaningful in the
embedding style space; 3) a latent patch-based contrastive (LLatentPCE) objective help improving quality and local structures by aligning multiscale spatial patches of generated and real features in latent space. Extensive experiments and analysis on
benchmark datasets from multiple
languages, including English, Chinese, and our proposed ViHTGen
dataset for Vietnamese, demonstrate the superiority of adapting to new reference styles and producing highly detailed images of our method over state-of-the-art approaches. Code is available at GitHub
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All authors Anh-Duy Le, Van-Linh Pham, Thanh-Nam Vo, Xuan Toan Mai, Tuan-Anh Tran
Computer Vision
How Long Can Unified Multimodal Models Generate Images Reliably? Taming Long-Horizon Interleaved Image Generation via Context Curation
Haoyu Chen, Qing Liu, Yuqian Zhou et al. (+7 more)
Unified
multimodal models hold the promise of generating extensive, interleaved narratives, weaving text and imagery into coherent long-form stories. However, current systems suffer from a critical reliability gap: as sequences grow, generation quality rapidly collapses. In this work, we investigate the mechanism behind this failure and argue that it is distinct from standard long-context challenges. We reveal that in generation, accumulated visual history acts as a source of active pollution, a decay governed specifically by the number of image events rather than raw
token count. We identify a structural vulnerability where dense visual
tokens overwhelm the
attention mechanism, creating noise that distorts future synthesis. Guided by these mechanistic insights, we propose UniLongGen, a training-free inference strategy that prioritizes safe conditioning over total recall. Instead of retaining all history, UniLongGen dynamically curates the model's
memory, identifying and discarding interfering visual signals based on the model's own internal relevance rankings. Extensive experiments demonstrate that this active forgetting approach is essential for stability: UniLongGen significantly outperforms baselines in long-horizon fidelity and consistency, while simultaneously reducing
memory footprint and inference time.
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All authors Haoyu Chen, Qing Liu, Yuqian Zhou, He Zhang, Zhaowen Wang, Mengwei Ren, Jingjing Ren, Xiang Wang, Zhe Lin, Lei Zhu
Computer Vision
Scale-Aware UAV-to-Satellite Cross-View Geo-Localization: A Semantic Geometric Approach
Yibin Ye, Shuo Chen, Kun Wang et al. (+5 more)
Cross-View Geo-Localization (CVGL) between
UAV imagery and satellite images plays a crucial role in target localization and
UAV self-positioning. However, most existing methods rely on the idealized assumption of scale consistency between
UAV queries and satellite galleries, overlooking the severe scale ambiguity commonly encountered in real-world scenarios. This discrepancy leads to field-of-view misalignment and feature mismatch, significantly degrading CVGL robustness. To address this issue, we propose a
geometric framework that recovers the absolute metric scale from
monocular UAV images using semantic anchors. Specifically, small vehicles (SVs), characterized by relatively stable prior size distributions and high detectability, are exploited as metric references. A Decoupled Stereoscopic Projection Model is introduced to estimate the absolute image scale from these semantic targets. By decomposing vehicle dimensions into radial and tangential components, the model compensates for perspective distortions in 2D detections of 3D vehicles, enabling more accurate scale estimation. To further reduce intra-class size variation and detection noise, a dual-dimension fusion strategy with Interquartile Range (IQR)-based robust aggregation is employed. The estimated global scale is then used as a physical constraint for scale-adaptive satellite image cropping, improving
UAV-to-satellite feature
alignment. Experiments on augmented DenseUAV and
UAV-VisLoc
datasets demonstrate that the proposed method significantly improves CVGL robustness under unknown
UAV image scales. Additionally, the framework shows strong potential for downstream applications such as passive
UAV altitude estimation and 3D model scale recovery.
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All authors Yibin Ye, Shuo Chen, Kun Wang, Xiaokai Song, Jisheng Dang, Qifeng Yu, Xichao Teng, Zhang Li
Computer Vision · Cross-listed from cs.RO
ACCURATE: Arbitrary-shaped Continuum Reconstruction Under Robust Adaptive Two-view Estimation
Yaozhi Zhang, Shun Yu, Yugang Zhang et al. (+1 more)
Accurate reconstruction of arbitrary-shaped long slender continuum bodies, such as guidewires, catheters and other soft continuum manipulators, is essential for accurate mechanical simulation. However, existing image-based reconstruction approaches often suffer from limited accuracy because they often underutilize camera geometry, or lack generality as they rely on rigid
geometric assumptions that may fail for continuum
robots with complex and highly deformable shapes. To address these limitations, we propose ACCURATE, a 3D reconstruction framework integrating an image
segmentation neural network with a geometry-constrained
topology traversal and dynamic programming
algorithm that enforces global biplanar
geometric consistency, minimizes the cumulative point-to-epipolar-line distance, and remains robust to occlusions and epipolar ambiguities cases caused by noise and discretization. Our method achieves high reconstruction accuracy on both simulated and real phantom
datasets acquired using a clinical X-ray C-arm system, with mean absolute errors below 1.0 mm.
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All authors Yaozhi Zhang, Shun Yu, Yugang Zhang, Yang Liu
Computer Vision
SketchGraphNet: A Memory-Efficient Hybrid Graph Transformer for Large-Scale Sketch Corpora Recognition
Shilong Chen, Mingyuan Li, Zhaoyang Wang et al. (+2 more)
This work investigates large-scale sketch recognition from a graph-native perspective, where free-hand sketches are directly modeled as structured graphs rather than raster images or stroke sequences. We propose SketchGraphNet, a hybrid graph neural architecture that integrates local message passing with a
memory-efficient global
attention mechanism, without relying on auxiliary positional or structural encodings. To support systematic evaluation, we construct SketchGraph, a large-scale
benchmark comprising 3.44 million graph-structured sketches across 344 categories, with two variants (A and R) to reflect different noise conditions. Each sketch is represented as a spatiotemporal graph with normalized stroke-order attributes. On SketchGraph-A and SketchGraph-R, SketchGraphNet achieves Top-1 accuracies of 83.62% and 87.61%, respectively, under a unified training configuration. MemEffAttn further reduces peak GPU
memory by over 40% and training time by more than 30% compared with Performer-based global
attention, while maintaining comparable accuracy.
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All authors Shilong Chen, Mingyuan Li, Zhaoyang Wang, Zhonglin Ye, Haixing Zhao
Computer Vision
EvolveReason: Self-Evolving Reasoning Paradigm for Explainable Deepfake Facial Image Identification
Binjia Zhou, Dawei Luo, Shuai Chen et al. (+7 more)
With the rapid advancement of AIGC technology, developing identification methods to address the security challenges posed by deepfakes has become urgent. Face forgery identification techniques can be categorized into two types: traditional classification methods and explainable VLM approaches. The former provides classification results but lacks explanatory ability, while the latter, although capable of providing coarse-grained explanations, often suffers from
hallucinations and insufficient detail. To overcome these limitations, we propose EvolveReason, which mimics the
reasoning and observational processes of human auditors when identifying face forgeries. By constructing a chain-of-thought
dataset, CoT-Face, tailored for advanced VLMs, our approach guides the model to think in a human-like way, prompting it to output
reasoning processes and judgment results. This provides practitioners with reliable analysis and helps alleviate
hallucination. Additionally, our framework incorporates a forgery latent-space distribution capture module, enabling EvolveReason to identify high-frequency forgery cues difficult to extract from the original images. To further enhance the reliability of textual explanations, we introduce a self-
evolution exploration strategy, leveraging reinforcement learning to allow the model to iteratively explore and optimize its textual descriptions in a two-stage process. Experimental results show that EvolveReason not only outperforms the current state-of-the-art methods in identification performance but also accurately identifies forgery details and demonstrates generalization capabilities.
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All authors Binjia Zhou, Dawei Luo, Shuai Chen, Feng Xu, Seow, Haoyuan Li, Jiachi Wang, Jiawen Wang, Zunlei Feng, Yijun Bei
Computer Vision · Cross-listed from cs.LG
A Unified View of Score-Based and Drifting Models
Chieh-Hsin Lai, Bac Nguyen, Naoki Murata et al. (+5 more)
Drifting models train one-step generators by optimizing a kernel-induced mean-shift discrepancy between the data and model distributions, with Laplace kernels used by default in practice. At each point, this discrepancy compares the kernel-weighted displacement toward nearby data samples with the corresponding displacement toward nearby model samples, thereby defining a transport direction for generated samples. In this paper, we show that drifting is more closely connected to score-based generative modeling than it may first appear, establishing a precise link to the score-matching principle underlying
diffusion models. For Gaussian kernels, the population mean-shift field exactly equals the difference between the scores (i.e., the gradient-log-densities) of the Gaussian-smoothed data and model distributions. This identity follows from Tweedie's formula, which links the score of a Gaussian-smoothed density to its conditional mean, and implies that Gaussian-kernel drifting is exactly a score-matching objective on smoothed distributions. More generally, we derive an exact decomposition for radial kernels in which mean shift equals a score-based field plus a residual term. For the practical Laplace kernel, we further show theoretically and empirically that this residual is negligible in high dimension, implying that the transport field used in practice is nearly score-based. Our results reveal a structural connection to
diffusion models: both methods use score-mismatch transport directions, but drifting realizes the score nonparametrically through kernel-based estimates, whereas
diffusion models learn it parametrically with neural networks.
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All authors Chieh-Hsin Lai, Bac Nguyen, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yuki Mitsufuji, Stefano Ermon, Molei Tao
Computer Vision
High-Fidelity Medical Shape Generation via Skeletal Latent Diffusion
Guoqing Zhang, Jingyun Yang, Siqi Chen et al. (+2 more)
Anatomy shape modeling is a fundamental problem in medical data analysis. However, the
geometric complexity and topological variability of anatomical structures pose significant challenges to accurate anatomical shape generation. In this work, we propose a skeletal latent
diffusion framework that explicitly incorporates structural priors for efficient and high-fidelity medical shape generation. We introduce a shape auto-encoder in which the encoder captures global
geometric information through a differentiable skeletonization module and aggregates local surface features into shape latents, while the decoder predicts the corresponding implicit fields over sparsely sampled coordinates. New shapes are generated via a latent-space
diffusion model, followed by neural implicit decoding and mesh extraction. To address the limited availability of medical shape data, we construct a large-scale
dataset, MedSDF, comprising surface point clouds and corresponding signed distance fields across multiple anatomical categories. Extensive experiments on MedSDF and vessel
datasets demonstrate that the proposed method achieves superior reconstruction and generation quality while maintaining a higher computational efficiency compared with existing approaches. Code is available at: https://github.com/wlsdzyzl/meshage.
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All authors Guoqing Zhang, Jingyun Yang, Siqi Chen, Anping Zhang, Yang Li
Computer Vision
AMR-CCR: Anchored Modular Retrieval for Continual Chinese Character Recognition
Yuchuan Wu, Yinglian Zhu, Haiyang Yu et al. (+3 more)
Ancient Chinese character recognition is a core capability for cultural heritage digitization, yet real-world workflows are inherently non-stationary: newly excavated materials are continuously onboarded, bringing new classes in different scripts, and expanding the class space over time. We formalize this process as Continual Chinese Character Recognition (Continual CCR), a script-staged, class-incremental setting that couples two challenges: (i) scalable learning under continual class growth with subtle inter-class differences and scarce incremental data, and (ii) pronounced intra-class diversity caused by writing-style variations across writers and carrier conditions. To overcome the limitations of conventional closed-set classification, we propose AMR-CCR, an anchored modular
retrieval framework that performs recognition via
embedding-based dictionary matching in a shared
multimodal space, allowing new classes to be added by simply extending the dictionary. AMR-CCR further introduces a lightweight script-conditioned injection module (SIA+SAR) to calibrate newly onboarded scripts while preserving cross-stage
embedding compatibility, and an image-derived multi-prototype dictionary that clusters within-class
embeddings to better cover diverse style modes. To support systematic evaluation, we build EvoCON, a six-stage
benchmark for continual script onboarding, covering six scripts (OBC, BI, SS, SAC, WSC, CS), augmented with meaning/shape descriptions and an explicit zero-shot split for unseen characters without image exemplars.
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All authors Yuchuan Wu, Yinglian Zhu, Haiyang Yu, Ke Niu, Bin Li, Xiangyang Xue
Computer Vision
DocCogito: Aligning Layout Cognition and Step-Level Grounded Reasoning for Document Understanding
Yuchuan Wu, Minghan Zhuo, Teng Fu et al. (+3 more)
Document understanding with
multimodal large
language models (MLLMs) requires not only accurate answers but also explicit, evidence-grounded
reasoning, especially in high-stakes scenarios. However, current document MLLMs still fall short of forming a complete, human-like
reasoning process, because even when they improve both layout encoding and CoT-style prompting, the interaction between the two is typically learned implicitly and remains loosely coupled rather than being enforced as a systematic mechanism. So we propose DocCogito, a unified framework that integrates global layout perception with structured, region-grounded
reasoning. DocCogito introduces a lightweight layout tower that distills page structure into learnable global layout prior
tokens, and a deterministic Visual-Semantic Chain (VSC)-a concise structured representation less ambiguous than free-form natural-
language CoT-to supervise fine-grained intermediate
reasoning aligned with evidence regions. Training follows a progressive recipe, including layout perception pretraining, VSC-guided cold start, rejection sampling, and GRPO. To further strengthen the internal coupling between layout priors and VSC execution, we augment standard rewards with a fine-grained region-confidence signal that encourages
reasoning traces to stay aligned with corresponding evidence regions. Extensive experiments on six
benchmarks (DocVQA, WTQ, ChartQA, TextVQA, OCRBench, and InfoVQA) demonstrate strong generalization, achieving state-of-the-art results on four
benchmarks.
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All authors Yuchuan Wu, Minghan Zhuo, Teng Fu, Mengyang Zhao, Bin Li, Xiangyang Xue
Computer Vision
RayD3D: Distilling Depth Knowledge Along the Ray for Robust Multi-View 3D Object Detection
Rui Ding, Zhaonian Kuang, Zongwei Zhou et al. (+3 more)
Multi-view 3D detection with bird's eye view (BEV) is crucial for autonomous driving and robotics, but its robustness in real-world is limited as it struggles to predict accurate depth values. A mainstream solution, cross-modal
distillation, transfers depth information from
LiDAR to camera models but also unintentionally transfers depth-irrelevant information (e.g.
LiDAR density). To mitigate this issue, we propose RayD3D, which transfers crucial depth knowledge along the ray: a line projecting from the camera to true location of an object. It is based on the fundamental imaging principle that predicted location of this object can only vary along this ray, which is finally determined by predicted depth value. Therefore, distilling along the ray enables more effective depth information transfer. More specifically, we design two ray-based
distillation modules. Ray-based Contrastive
Distillation (RCD) incorporates contrastive learning into
distillation by sampling along the ray to learn how
LiDAR accurately locates objects. Ray-based Weighted
Distillation (RWD) adaptively adjusts
distillation weight based on the ray to minimize the interference of depth-irrelevant information in
LiDAR. For validation, we widely apply RayD3D into three representative types of BEV-based models, including BEVDet, BEVDepth4D, and BEVFormer. Our method is trained on clean NuScenes, and tested on both clean NuScenes and RoboBEV with a variety types of data corruptions. Our method significantly improves the robustness of all the three base models in all scenarios without increasing inference costs, and achieves the best when compared to recently released multi-view and
distillation models.
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All authors Rui Ding, Zhaonian Kuang, Zongwei Zhou, Meng Yang, Xinhu Zheng, Gang Hua
Computer Vision
RobustSCI: Beyond Reconstruction to Restoration for Snapshot Compressive Imaging under Real-World Degradations
Hao Wang, Zhankuo Xu, Jiong Ni et al. (+3 more)
Deep learning
algorithms for
video Snapshot Compressive Imaging (SCI) have achieved great success, yet they predominantly focus on reconstructing from clean measurements. This overlooks a critical real-world challenge: the captured signal itself is often severely degraded by motion blur and low light. Consequently, existing models falter in practical applications. To break this limitation, we pioneer the first study on robust
video SCI restoration, shifting the goal from "reconstruction" to "restoration"–recovering the underlying pristine scene from a degraded measurement. To facilitate this new task, we first construct a large-scale
benchmark by simulating realistic, continuous degradations on the DAVIS 2017
dataset. Second, we propose RobustSCI, a network that enhances a strong encoder-decoder backbone with a novel RobustCFormer block. This block introduces two parallel branches–a multi-scale deblur branch and a frequency enhancement branch–to explicitly disentangle and remove degradations during the recovery process. Furthermore, we introduce RobustSCI-C (RobustSCI-Cascade), which integrates a pre-trained Lightweight Post-processing Deblurring Network to significantly boost restoration performance with minimal overhead. Extensive experiments demonstrate that our methods outperform all SOTA models on the new degraded testbeds, with additional validation on real-world degraded SCI data confirming their practical effectiveness, elevating SCI from merely reconstructing what is captured to restoring what truly happened.
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All authors Hao Wang, Zhankuo Xu, Jiong Ni, Xing Liu, Haoyang Liu, Xin Yuan
Computer Vision
Multi-Modal Decouple and Recouple Network for Robust 3D Object Detection
Rui Ding, Zhaonian Kuang, Yuzhe Ji et al. (+3 more)
Multi-modal 3D object detection with bird's eye view (BEV) has achieved desired advances on
benchmarks. Nonetheless, the accuracy may drop significantly in the real world due to data corruption such as sensor configurations for
LiDAR and scene conditions for camera. One design bottleneck of previous models resides in the tightly coupling of multi-modal BEV features during fusion, which may degrade the overall system performance if one modality or both is corrupted. To mitigate, we propose a Multi-Modal Decouple and Recouple Network for robust 3D object detection under data corruption. Different modalities commonly share some high-level invariant features. We observe that these invariant features across modalities do not always fail simultaneously, because different types of data corruption affect each modality in distinct ways.These invariant features can be recovered across modalities for robust fusion under data corruption.To this end, we explicitly decouple Camera/
LiDAR BEV features into modality-invariant and modality-specific parts. It allows invariant features to compensate each other while mitigates the negative impact of a corrupted modality on the other.We then recouple these features into three experts to handle different types of data corruption, respectively, i.e.,
LiDAR, camera, and both.For each expert, we use modality-invariant features as robust information, while modality-specific features serve as a complement.Finally, we adaptively fuse the three experts to exact robust features for 3D object detection. For validation, we collect a
benchmark with a large quantity of data corruption for
LiDAR, camera, and both based on nuScenes. Our model is trained on clean nuScenes and tested on all types of data corruption. Our model consistently achieves the best accuracy on both corrupted and clean data compared to recent models.
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All authors Rui Ding, Zhaonian Kuang, Yuzhe Ji, Meng Yang, Xinhu Zheng, Gang Hua
Computer Vision
Wenqi Cai, Yawen Zou, Guang Li et al. (+2 more)
Dataset distillation (DD) aims to synthesize compact training sets that enable models to achieve high accuracy with significantly fewer samples. Recent
diffusion-based DD methods commonly introduce semantic guidance through late-stage cross-
attention, where textual
prompts tend to dominate the generative process. Although this strategy enforces label relevance, it diminishes the contribution of visual latents, resulting in over-corrected samples that mirror
prompt patterns rather than reflecting intrinsic visual features. To solve this problem, we introduce an Early
Vision-
Language Fusion (EVLF) method that aligns textual and visual
embeddings at the transition between the encoder and the generative backbone. By incorporating a lightweight cross-
attention module at this transition, the early representations simultaneously encode local textures and global semantic directions across the denoising process. Importantly, EVLF is plug-and-play and can be easily integrated into any
diffusion-based
dataset distillation pipeline with an encoder. It works across different denoiser architectures and sampling schedules without any task-specific modifications. Extensive experiments demonstrate that EVLF generates semantically faithful and visually coherent synthetic data, yielding consistent improvements in downstream classification accuracy across varied settings. Source code is available at https://github.com/wenqi-cai297/earlyfusion-for-dd/.
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All authors Wenqi Cai, Yawen Zou, Guang Li, Chunzhi Gu, Chao Zhang
Computer Vision
FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentation
Xiaokang Zhang, Xuran Xiong, Jianzhong Huang et al. (+1 more)
Remote sensing image
segmentation (RSIS) in
federated environments has gained increasing
attention because it enables collaborative model training across distributed
datasets without sharing raw imagery or annotations.
Federated RSIS combined with parameter-efficient fine-tuning (PEFT) can unleash the generalization power of pretrained foundation models for real-world applications, with minimal parameter aggregation and communication overhead. However, the dynamic adaptation of pretrained models to heterogeneous client data inevitably increases update uncertainty and compromises the reliability of collaborative optimization due to the lack of uncertainty estimation for each local model. To bridge this gap, we present FedEU, a
federated optimization framework for fine-tuning RSIS models driven by evidential uncertainty. Specifically, personalized evidential uncertainty modeling is introduced to quantify epistemic variations of local models and identify high-risk areas under local data distributions. Furthermore, the client-specific feature
embedding (CFE) is exploited to enhance channel-aware feature representation while preserving client-specific properties through personalized
attention and an element-aware parameter update approach. These uncertainty estimates are uploaded to the server to enable adaptive global aggregation via a Top-k uncertainty-guided weighting (TUW) strategy, which mitigates the impact of distribution shifts and unreliable updates. Extensive experiments on three large-scale heterogeneous
datasets demonstrate the superior performance of FedEU. More importantly, FedEU enables balanced model adaptation across diverse clients by explicitly reducing prediction uncertainty, resulting in more robust and reliable
federated outcomes. The source codes will be available at https://github.com/zxk688/FedEU.
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All authors Xiaokang Zhang, Xuran Xiong, Jianzhong Huang, Lefei Zhang
Computer Vision
Classifying Novel 3D-Printed Objects without Retraining: Towards Post-Production Automation in Additive Manufacturing
Fanis Mathioulakis, Gorjan Radevski, Silke GC Cleuren et al. (+4 more)
Reliable classification of 3D-printed objects is essential for automating post-production workflows in industrial additive manufacturing. Despite extensive automation in other stages of the printing pipeline, this task still relies heavily on manual inspection, as the set of objects to be classified can change daily, making frequent model retraining impractical. Automating the identification step is therefore critical for improving operational efficiency. A
vision model that could classify any set of objects by utilizing their corresponding CAD models and avoiding retraining would be highly beneficial in this setting. To enable systematic evaluation of
vision models on this task, we introduce ThingiPrint, a new publicly available
dataset that pairs CAD models with real photographs of their 3D-printed counterparts. Using ThingiPrint, we
benchmark a range of existing
vision models on the task of 3D-printed object classification. We additionally show that contrastive fine-tuning with a rotation-invariant objective allows effective prototype-based classification of previously unseen 3D-printed objects. By relying solely on the available CAD models, this avoids the need for retraining when new objects are introduced. Experiments show that this approach outperforms standard pretrained baselines, suggesting improved generalization and practical relevance for real-world use.
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All authors Fanis Mathioulakis, Gorjan Radevski, Silke GC Cleuren, Michel Janssens, Brecht Das, Koen Schauwaert, Tinne Tuytelaars
Computer Vision
Selective Transfer Learning of Cross-Modality Distillation for Monocular 3D Object Detection
Rui Ding, Meng Yang and Nanning Zheng
Monocular 3D object detection is a promising yet ill-posed task for autonomous vehicles due to the lack of accurate depth information. Cross-modality knowledge
distillation could effectively transfer depth information from
LiDAR to image-based network. However, modality gap between image and
LiDAR seriously limits its accuracy. In this paper, we systematically investigate the negative transfer problem induced by modality gap in cross-modality
distillation for the first time, including not only the architecture inconsistency issue but more importantly the feature overfitting issue. We propose a selective learning approach named MonoSTL to overcome these issues, which encourages positive transfer of depth information from
LiDAR while alleviates the negative transfer on image-based network. On the one hand, we utilize similar architectures to ensure spatial
alignment of features between image-based and
LiDAR-based networks. On the other hand, we develop two novel
distillation modules, namely Depth-Aware Selective Feature
Distillation (DASFD) and Depth-Aware Selective Relation
Distillation (DASRD), which selectively learn positive features and relationships of objects by integrating depth uncertainty into feature and relation distillations, respectively. Our approach can be seamlessly integrated into various CNN-based and DETR-based models, where we take three recent models on KITTI and a recent model on NuScenes for validation. Extensive experiments show that our approach considerably improves the accuracy of the base models and thereby achieves the best accuracy compared with all recently released SOTA models.
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