Computer Vision
Flow6D: Discrete-to-Continuous Flow Matching for Efficient and Accurate Category-Level 6D Pose Estimation
Mingyu Mei, Li Zhang, Zibo Dai et al. (+4 more)
6D pose estimation is a key task in computer
vision and
embodied AI, widely used in robotic manipulation, augmented reality, etc. Existing methods directly regress in a high-dimensional continuous space, facing two key challenges in category-level pose estimation: limited accuracy due to noise and local optima, and inefficient search over an infinite space that hinders real-time performance. This paper proposes Flow6D, a hierarchical flow matching framework with a two-stage discrete latent space localization-continuous pose regression strategy. Rotation and translation parameters are first discretized into bins, with a discrete flow matching model locking the latent space around the true pose to reduce search complexity. Then, by sampling in the latent space, a continuous flow matching model predicts local pose residuals to optimize the estimate and regress to an accurate pose. The framework also naturally extends to articulated objects, outperforming state-of-the-art methods on synthetic and real
datasets with real-time inference at 70 FPS. Project website: https://flow6d.github.io/.
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All authors Mingyu Mei, Li Zhang, Zibo Dai, Han Sun, Xinyue Zhao, Huiliang Shen, Zaixing He
Computer Vision
Transfer learning-based method for automated ewaste recycling in smart cities
Nermeen Abou Baker, Paul Szabo-Müller and Uwe Handmann
Sorting a huge stream of waste accurately within a short period can be done with the support of digitalization, particularly Artificial Intelligence, instead of traditional methods. The overlap of Artificial Intelligence and Circular Economy can flourish many services in the environmental technology domain, in particular smart ewaste recycling, resulting in enabling circular smart cities. We analyse the growing need for automated ewaste recycling as an essential requirement to cope with the fast growing ewaste stream and we shed the light on the impact of Artificial Intelligence in supporting the recycling process through smart classification of devices, where the smartphone is our case study. Our study applies transfer learning as a special technique of Artificial Intelligence by finetuning the output layers of AlexNet as a pretrained model and perform the implementation on a small size
dataset that contains 12 classes from 6 smartphone brands. We evaluate the performance of our model by tuning the learning rate, choosing the best optimizer, and augmenting the original
dataset to avoid overfitting. We found that the optimizer of
Stochastic Gradient Descent with Momentum and 3e-4 as a learning rate brings almost 98% model accuracy with generalization. Our study supports automated ewaste recycling in decreasing the error rate of ewaste sorting and investigates the advantages of applying transfer learning as the best scenario to overcome the rising challenges.
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Computer Vision
BoxCtrl: 3D-Aware Visual Prompting for Geometric Image Editing
Feifei Wang, Shiyuan Yang, Xiaoyu Li et al. (+1 more)
As instruction-based editing models and
multimodal large
language models advance, diverse image editing tasks have become feasible. However, achieving precise and consistent
geometric image editing, such as translating, scaling, and rotating in 3D space, remains a major challenge. In this work, we introduce BoxCtrl, a 3D-aware visual prompting framework. Unlike text-only or coarse 2D-guided approaches, our method introduces informative RGB 3D bounding boxes projected onto 2D images as visual
prompts. The three orthogonal faces of each box are painted with distinct RGB colors, simultaneously encoding position, size, and orientation to provide a compact, intuitive in-context visual example. The key to BoxCtrl's success lies in these well-designed bounding boxes, which decouple
geometric control from appearance control. This enables the model to learn consistent correspondences between faces of the same color in the latent space, leading to a precise understanding of
geometric intentions and accurate editing results. We introduce a two-stage training paradigm: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL). To address paired data scarcity, we construct a large-scale synthetic
dataset for SFT, equipping the model with fundamental editing capabilities. To bridge the synthetic-to-real domain gap, we incorporate an online RL stage leveraging unpaired real-world data. Guided by a reward function evaluating
geometric accuracy and visual fidelity, our SFT-RL strategy significantly enhances
geometric precision while maintaining photorealistic quality. Extensive experiments demonstrate that BoxCtrl achieves state-of-the-art performance across translation, rotation, scaling, and composite editing tasks.
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All authors Feifei Wang, Shiyuan Yang, Xiaoyu Li, Jing Liao
Computer Vision
Safe Few-Step Generation via Velocity Editing
Yujin Choi and Jaehong Yoon
Flow matching has recently emerged as a strong paradigm for state-of-the-art text-to-image (T2I) generation, enabling high-quality generation with a small number of sampling steps. As these models are increasingly integrated into real-world applications, ensuring safety has become a critical requirement. However, adapting safety and concept removal methods to this new generation framework remains an open challenge. Specifically, prior methods largely rely on iterative trajectory steering across a number of denoising steps or on CLIP-centric
prompt embedding manipulation. These design assumptions pose fundamental bottlenecks for safety in flow matching-based T2I generation, where limited sampling steps constrain iterative correction and modern context-aware text encoders diminish the effectiveness of
embedding-level interventions. In this paper, we propose VESFlow, a training-free safety method tailored to flow matching, even with extremely few sampling steps. Leveraging the fact that flow matching models learn the marginal velocity (or average velocity in MeanFlow), we directly edit the velocity field via a
Bayesian decomposition of the safe-conditional posterior. VESFlow steers the trajectory toward safe outputs while leaving the conditioning
prompt unchanged. Building on the observation that VESFlow leaves outputs unchanged under benign
prompts, we further introduce a risk score-based filtering that bypasses velocity editing to reduce computational cost while preserving benign
prompt generation. Experimental results show that VESFlow removes the target concept, reducing the attack success rate for NudeNet to 15.2% on Ring-A-Bell and 7.5% on MMA-
Diffusion on the 4-step MeanFlow model, while preserving fidelity on benign
prompts.
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Computer Vision
P-JEPA: Procedural Video Representation Learning via Joint Embedding Predictive Architecture
Felix Tristram, Stefano Gasperini, Benjamin Killeen et al. (+4 more)
The increasing maturity of
embodied AI platforms has driven a growing interest in procedural
video representation learning to support intelligent assistance systems for complex, multi-step tasks. Leveraging large-scale latent predictive training,
video foundation models capture
video dynamics, enabling downstream tasks such as activity understanding, spatiotemporal localization, and predictive control. However, procedural
videos include actions with long-range dependencies that these models do not support, due to the quadratic complexity of self-
attention. Distinct actions, for example, may be visually similar despite appearing at different points in the procedure, such as turning the stove on versus off. Here, we propose a backbone-agnostic approach that learns long-duration
video representations by reducing the problem to a dense, frame-aligned action space and predicting pooled masked latent vectors. This approach allows our Procedural Joint
Embedding Predictive Architecture (P-JEPA) to ingest
videos over 30 minutes long, enabling effective long-form understanding of procedural steps. We evaluate P-JEPA using features extracted with VJEPA2.1, TSM, and I3D over the EgoExo4D, EgoProceL, and Assembly101
datasets, finding that it consistently improves linear separability, streaming inference, and temporal action
segmentation performance, achieving state-of-the-art results on EgoExo4D fine-grained action classification while using an order of magnitude fewer parameters than LLM-based methods and running in real time.
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All authors Felix Tristram, Stefano Gasperini, Benjamin Killeen, Marcel Walch, Christian Benz, Nassir Navab, Ghazal Ghazaei
Computer Vision
SteerVTE: Seamless Video Text Editing with Style and Glyph Control
Kai Zeng, Moran Li, Zhengwei Wang et al. (+6 more)
Visual text editing aims to precisely modify text in images and
videos while preserving stylistic consistency and visual realism. Despite significant advances in the image domain,
video text editing remains largely unexplored: it is a localized task demanding stroke-level precision within small text regions, which compounds the challenges of cross-frame accuracy, temporal coherence, and stylistic fidelity. We introduce SteerVTE, a unified framework that steers a frozen
video diffusion model to perform precise
Video Text Editing through style and glyph control. Built on a frozen
diffusion transformer, SteerVTE attaches a lightweight text context adapter with two complementary modules: a style encoder capturing the original text's visual attributes, and dual-granularity glyph encoders encoding the target text at both the line and character levels. To overcome the inherently weak text rendering priors of
video foundation models, we further propose a glyph-aware spatial-focal loss and a three-stage progressive training curriculum that scales from image to
video data. To support large-scale training, we also develop an automatic synthesis pipeline and construct SteerVTE-1M, a
dataset of one million triplets spanning diverse scenes, fonts, and stylistic effects. Extensive experiments demonstrate that SteerVTE substantially outperforms existing
video editing baselines across text accuracy, style consistency, and temporal coherence. Our project page is available at https://zengkaiya.github.io/SteerVTE/.
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All authors Kai Zeng, Moran Li, Zhengwei Wang, Yingchen Yu, Yiheng Lin, Ruichuan An, Ming Lu, Qi She, Wentao Zhang
Computer Vision
Privacy-Preserving Person Re-Identification from Temporal Sequences with Transformer and Hungarian Optimization
Raphaël Delécluse, Hazem Wannous and Laurent Guimas
Person re-identification (Re-ID) is a crucial task in surveillance and human behavior analysis, often used in public spaces such as transport hubs. Traditional RGB-based Re-ID methods raise
privacy concerns and are highly sensitive to lighting variations and occlusion. In this paper, we propose a novel Re-ID approach that leverages depth images, which inherently obscures facial and other identifiable features, making it a
privacy-preserving solution. Our method addresses the association problem between multiple views of individuals by applying the Hungarian
algorithm, optimizing the matching process through minimization of the global cost across the distance
matrix. We further enhance the approach by introducing temporal sequences of frames as input to a
Transformer encoder architecture, which exploits both RGB and depth modalities. This architecture captures dynamic movement patterns, improving feature extraction and re-identification accuracy. Additionally, we employ batch hard triplet loss to enhance discriminative feature learning by focusing on the hardest samples. We evaluate both depth-only and RGB-D models on several top-view
datasets, including TVPR2, GODPR, and BIWI RGBD-ID. Our results demonstrate that depth-only re-identification can achieve competitive performance compared to state-of-the-art methods, as measured by standard metrics such as Cumulative Matching Characteristics (CMC) and Mean Average Precision (mAP), while prioritizing
privacy preservation.
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Computer Vision
PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography
Zixu Li, jianjun Qian, Hang Shao et al. (+2 more)
Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial
videos, serving as a vital tool for health monitoring. However, current deep learning methods often struggle under complex disturbances, particularly varying illumination, facial expressions, and unconstrained head movements. In such scenarios, subtle physiological signals are easily dominated by external interference, making the recovered rPPG waveform unstable and unreliable. One important reason is that most existing methods directly model the rPPG signal in a unified manner, where different signal components are coupled during reconstruction. This makes it difficult to preserve weak pulse-related variations when strong disturbance-induced changes are present. To address this challenge, we propose PhysFlow, a frequency-decoupled dual-field rectified flow framework tailored for robust rPPG estimation. Specifically, the ground-truth rPPG signal is decomposed into trend and amplitude components, which are used as separate supervisory targets. Based on the extracted facial features, PhysFlow learns two component-specific conditional velocity fields to model the two components separately. This design reduces mutual interference between different components and improves the robustness of rPPG reconstruction under complex disturbances. Moreover, the rectified flow formulation enables efficient waveform reconstruction with only a few ordinary differential equation (ODE) integration steps. Extensive experiments on multiple
benchmark datasets demonstrate that PhysFlow outperforms state-of-the-art methods in both heart-rate estimation and rPPG waveform reconstruction across diverse challenging scenarios.
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All authors Zixu Li, jianjun Qian, Hang Shao, Lei Luo, Jian Yang
Computer Vision
RS-Gen: A Multi-Stage Agentic Framework for Reasoning and Search-Augmented Image Generation
Feifei Bian, Zhimin Zheng, Wei Deng et al. (+2 more)
Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. However, when handling ambiguous intentions, logical
reasoning, and Out-of-Distribution (OOD) knowledge, existing image models often yield sub-optimal results due to a lack of deep
reasoning capabilities and real-time external information. Although emerging unified understanding-and-generation models attempt to bridge this gap, they remain constrained by their intrinsic parameter scales and static knowledge gaps. Inspired by agentic paradigms, we propose RS-Gen: a plug-and-play, training-free, multi-stage image agentic framework. RS-Gen innovatively introduces a "Questioning-and-Solving" closed-loop mechanism to accurately identify logical issues and knowledge gaps, autonomously planning actions to bridge information deficits and execute deep logical
reasoning. Extensive experiments demonstrate that RS-Gen significantly expands the capability boundaries of foundational image generation and editing models. Specifically, on the WISE Verified and RISEBench
benchmarks, RS-Gen yields substantial absolute performance gains of 0.313 for Qwen-Image and 19.70 for Qwen-Image-Edit-2511, respectively, successfully elevating both to the state-of-the-art (SOTA) level among open-source models.
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All authors Feifei Bian, Zhimin Zheng, Wei Deng, Daiguo Zhou, Jian Luan
Computer Vision
Temporally Aware Densification for Dynamic 3D Gaussian Splatting
Vikram Sandu, Mayurdeep Pathak and Rajiv Soundararajan
Despite modeling temporal motion, dynamic 3D Gaussian Splatting (3DGS) methods still inherit a static densification strategy that is ill-suited for dynamic scenes. This neglect of temporal behavior leads to under-reconstructed and blurry dynamic regions, as short-lived Gaussians receive sparse supervision and fail to densify effectively. We propose a Visibility-Aware Densification (VAD) framework that integrates temporal visibility into the densification process, ensuring that Gaussians are refined based on their actual temporal presence. A Temporally-Adaptive Thresholding (TAT) mechanism further adjusts each Gaussian's densification threshold according to its temporal lifespan, promoting balanced refinement of both static and dynamic regions. Finally, a Temporal Offset Warping (TOW) design enhances deformation capacity around temporal centers, extending the lifespan of highly dynamic Gaussians and facilitating more effective densification. Our approach achieves substantial improvements in the visual quality of dynamic regions, outperforming existing methods across three dynamic multi-view
benchmark datasets. Moreover, the proposed VAD module generalizes across diverse dynamic 3DGS methods, consistently improving dynamic reconstruction as a plug-and-play component.
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Computer Vision
CFPO: Counterfactual Policy Optimization for Multimodal Reasoning
Zhangyuan Yu, Wanran Sun, Guangjing Yang et al. (+2 more)
Large
Vision-
Language Models (LVLMs) have demonstrated remarkable capabilities in
multimodal reasoning. However, prevailing reinforcement learning (RL) paradigms lack explicit counterfactual enhancement and causal learning mechanisms. This fundamental deficiency results in severe grounding failures, manifesting as a tendency to ignore visual evidence in favor of
language priors or exhibiting
hallucination drift during long chain-of-thought
reasoning. To address this root cause, we propose CounterFactual Policy Optimization (CFPO), a novel framework that enforces causal consistency between visual perception and textual
reasoning. CFPO introduces a cross-modal counterfactual enhancement mechanism, which regularizes the policy by maximizing the discrepancy between the model's predictions and those from a counterfactual state where critical visual cues are suppressed. This approach seamlessly integrates with standard
algorithms like GRPO and DAPO without requiring external reward models or additional supervision. Extensive experiments demonstrate that CFPO significantly improves
reasoning fidelity, achieving consistent gains of 3.17%-6.25% over standard RL baselines and 1.32%-2.13% over the state-of-the-art perception-aware method (PAPO). Code is available at https://github.com/Raven-July/CFPO.
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All authors Zhangyuan Yu, Wanran Sun, Guangjing Yang, Xiaohu Wu, Qicheng Lao
Computer Vision
Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets
Iris Dominguez-Catena, Daniel Paternain and Mikel Galar
Large-scale image-text
datasets, such as LAION-5B, are foundational to modern AI systems, yet their vast scale and uncurated nature raise significant concerns about demographic and stereotypical biases. This study presents a comprehensive analysis of the demographic composition and representational, stereotypical, and intersectional biases in LAION-2B-en and LAION-2B-multi, the two main components of the LAION-5B
dataset. Using state-of-the-art models – FairFace, DeepFace, and Emo-AffectNet – we analyze faces detected in the
dataset to identify biases across age, gender, race, and expressed
emotion. Our findings reveal substantial overrepresentation of young adults (20–39), White individuals, and males, alongside consistent underrepresentation of minority racial groups and middle-aged or older women across both
dataset components. We also observe stereotypical associations between demographic attributes and
emotions, such as “Anger” being predominantly linked to males and “Happiness” to females, pointing to systemic imbalances in the data. The consistency of these patterns across two demographic models and both components of LAION-5B demonstrates that these biases are deeply embedded in one of the most widely-used training
datasets. Given the scale at which LAION-5B is used to train generative models, these demographic imbalances could shape the behavior and outputs of numerous downstream AI systems.
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Computer Vision · Cross-listed from eess.IV
NGPS: Structure-Preserving Self-Supervised Denoising via Neighbor-Guided Patch Sampling
Jaehyun Cho and YoungJoon Yoo
Neighboring-slice self-supervised denoising is attractive for volumetric medical imaging, yet inter-slice misalignment breaks anatomical correspondence and often yields ghosting and blurred margins when adjacent slices are used naively as targets. We propose Neighbor-Guided Patch Sampling (NGPS), a lightweight framework that constructs neighboring supervision under local inter-slice misalignment without explicit registration. To avoid learning from misleading targets, prior methods commonly mask discrepant regions, but this stabilizes training at the cost of leaving a non-trivial portion of neighboring evidence unexploited, particularly around high-frequency anatomical boundaries. NGPS addresses this by decoupling structure matching from signal
retrieval: for each masked location, it searches a local neighborhood for structurally similar candidate patches using a simple guide image (e.g., fast bilateral filtering), while retrieving the supervision signal directly from the raw noisy neighbor at the matched coordinates. By matching on a noise-attenuated guide while retrieving raw values from neighboring slices, NGPS constructs local pseudo targets without a learned registration module. Across the evaluated CT and synthetic-Rician MRI settings, NGPS improves fidelity and structure-sensitive metrics. Code is available at https://github.com/cv-cho/NGPS .
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Computer Vision
StreamPPG: Low-Latency rPPG Estimation via Consistent Privileged Learning
Yiming Li, Yihan Yang, Yuguang Chu et al. (+4 more)
Remote photoplethysmography (rPPG) estimates the blood volume pulse (BVP) signal from facial
videos, enabling contact-free health monitoring. Conventional clip-wise approaches, which use
video clips as input, require capturing over one hundred frames before inference, thus introducing several seconds of delay and hindering real-time use. Meanwhile, frame-wise approaches struggle to capture long-range temporal and periodic features of physiological rhythms, and therefore lead to reduced estimation accuracy. To overcome these issues, we propose StreamPPG, a unified architecture that enables low-latency frame-wise physiological signal estimation while achieving competitive accuracy compared with clip-wise approaches. StreamPPG is trained under a consistent privileged learning (CPL) strategy, which leverages ground-truth rPPG signals as privileged information to enhance the model's representation capability. Extensive experiments demonstrate that StreamPPG achieves state-of-the-art accuracy across multiple
datasets while maintaining real-time throughput on edge devices.
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All authors Yiming Li, Yihan Yang, Yuguang Chu, Yuanhui Hu, Lu Yu, Xiaohan Zhang, Hui-Liang Shen
Computer Vision
Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability
Qi Li, Yuliang Huang, Shaheer U. Saeed et al. (+7 more)
Deep learning-based medical image
segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While probabilistic multi-rater approaches can emulate annotator-specific delineations, annotator characteristics are typically encoded implicitly in deep latent feature space, making direct analysis of their influence on predictive distributions less straightforward. We propose a logit-space probabilistic
segmentation framework based on
stochastic variational Gaussian Process that explicitly decomposes predictions into an image-dependent reference logit distribution and annotator specific perturbations parameterised by bias and variance. This formulation enables more explicit analysis on how intra- and inter-rater variability propagate to predictive distributions. We evaluate the method on a multi-annotator medical image
dataset, which shows that explicitly modelling annotator specific perturbations improves uncertainty calibration while maintaining comparable
segmentation accuracy, compared with state-of-the-art multi-rater probabilistic
segmentation method. The learned bias and variance parameters quantitatively reflect annotator-specific behaviour. Furthermore, controlled perturbation experiments over bias and variance demonstrate how changes in annotator parameters systematically influence predictive performance. The code used in this paper is made publicly available at https://github.com/QiLi111/GPS-Var.
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All authors Qi Li, Yuliang Huang, Shaheer U. Saeed, Qianye Yang, Vasilis Stavrinides, Zachary M. C. Baum, Dean C. Barratt, J. Alison Noble, Tom Vercauteren, Yipeng Hu
Computer Vision
Koshur Pixel: a large-scale synthetic ocr dataset for kashmiri
Haq Nawaz Malik, Faizan Iqbal and Nahfid Nissar
Optical Character Recognition (OCR) for low-resource
languages is often constrained by the lack of annotated training data and the complexity of script-specific rendering. Kashmiri, written primarily in the Perso-Arabic Nastaliq script, presents additional challenges due to contextual glyph shaping, dense ligatures, and orthographic variability. We introduce Koshur Pixel, the first large-scale synthetic OCR
dataset for Kashmiri, comprising 613,078 image-text pairs generated from the KS-PRET-5M corpus using the SynthOCR-Gen framework. The
dataset spans multiple fonts and textual granularities, ranging from individual words to full-page documents, and incorporates more than 25 augmentation strategies that emulate real-world document degradations. Koshur Pixel provides a scalable and cost-effective alternative to manual annotation, establishing a foundational resource for training OCR systems, digitizing Kashmiri textual heritage, and advancing
language technologies for a severely under-resourced
language.
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Computer Vision
T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models
Jaehyuk Jang, Minseok Seo, Seungju Cho et al. (+2 more)
Vision-
language models (VLMs) achieve strong zero-shot recognition, but they remain highly vulnerable to
adversarial perturbations. Recent test-time adaptations improve robustness without retraining, but they do not directly adapt the corrupted visual representation itself.
Prompt-based methods adapt the learnable text
prompts, while input-space methods optimize pixels or padding at test time. These approaches can improve predictions, but they do so through an indirect and expensive optimization path. We propose Test-time Visual Subspace Steering (T-VSS), a lightweight defense that performs test-time adaptation directly in the visual feature space. T-VSS first builds a sample-specific low-rank subspace from multi-view feature residuals anchored at the attacked image. It then learns a shared feature correction within this subspace using reliability-weighted
entropy minimization. By constraining adaptation to a compact visual geometry, T-VSS steers attacked features toward more stable and discriminative predictions while avoiding noisy full-space updates. Experiments on fine-grained, ImageNet, and ImageNet-OOD
benchmarks show that T-VSS improves
adversarial robustness while maintaining competitive clean accuracy and better efficiency than prior test-time adaptations.
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All authors Jaehyuk Jang, Minseok Seo, Seungju Cho, Kangwook Ko, Changick Kim
Computer Vision
Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation
Amir Ebrahimzadeh, Nazila Esmaeili, Michael Ghadimi et al. (+1 more)
Background: Laparoscopic camera
navigation (LCN) is a critical skill, yet its current assessment typically relies on manual rating systems which are time-consuming and difficult to scale. Automated feedback could significantly enhance
surgical training by providing immediate, standardized metrics. This study aims to define, clinically evaluate the relevance, and establish the technical readiness of a set of approaches for LCN assessment. Methods: We developed a detailed taxonomy of 14 key aspects of camera
navigation, categorized into Framing & Composition, Visibility & Clarity, Orientation & Stability, Motion & Dynamics, and Safety & Awareness. For each aspect, we assessed the technological readiness of automated measurement based on the current state of the art (SoTA) in computer
vision (CV). To establish clinical relevance, we designed a survey for practicing laparoscopic surgeons to rate the importance of each aspect on a 5-point Likert scale and to select the five most critical skills. Results: 23 surgeons participated in the survey. Foundational aspects like Field of View, Focus and Centering were rated as most important by surgeons. We present a "Clinical Importance vs. CV Technological Readiness"
matrix, identifying high-priority targets for development–aspects that are both clinically crucial and technologically ready to measure. Conclusion: This work establishes a foundational framework for quantifying LCN skills. By aligning surgeon priorities with CV capabilities, we provide a clear roadmap for automatic skill assessment. This foundation enables the development of AI-driven assistance tools that can accelerate the learning curve for
surgical assistants and potentially improve
surgical safety and efficiency.
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All authors Amir Ebrahimzadeh, Nazila Esmaeili, Michael Ghadimi, Jannis Hagenah
Computer Vision
Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations
Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti et al. (+1 more)
Implicit Neural Representations (INRs) have been proven successful in encoding continuous signals through coordinate-based networks, yet facing a spectral dilemma: periodic activations capture fine details but act as all-pass filters that memorise noise, while spatially compact activations regularise effectively but suffer from low-frequency bias. Existing attempts to resolve this trade-off introduce computational overhead or tuning frailty. We propose to model each
neuron's activation as the steady-state response of a sinusoidally-forced damped harmonic oscillator, whose amplitude naturally governs the network's spectral selectivity during training. By jointly optimising the oscillator parameters alongside the network weights, our method adapts to the target signal's spectral content without explicit regularisation. Initialised in the stopband, the network exhibits a coarse-to-fine learning curriculum that progressively expands its spectral gate, capturing low-frequency structures first and high-frequency details only when justified by the reconstruction objective. Comprehensive experiments show that our approach consistently achieves state-of-the-art or competitive results against established INRs, while requiring no task-specific tuning of any hyperparameters.
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All authors Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi Di Stefano
Computer Vision
MambaADv2: Evolving Duality-enhanced State Space Model for Unsupervised Anomaly Detection
Xiaobin Hu, Haoyang He, Bo Yin et al. (+5 more)
While recent advancements in anomaly detection have demonstrated the efficacy of CNN- and
Transformer-based approaches, these architectures face inherent limitations: CNNs struggle to capture long-range dependencies, whereas
Transformers suffer from quadratic computational complexity. Consequently, Mamba-based architectures have attracted considerable
attention, as they successfully combine superior long-range dependency modeling with linear computational complexity. By critically rethinking the structural
evolution across the Mamba lineage 1-3 series, this paper proposes MambaADv2, a framework tailored for multi-class unsupervised anomaly detection. MambaADv2 comprises a pre-trained encoder and a Mamba-inspired decoder, equipped with Duality-enhanced State Space (DSS) modules across multiple scales. The proposed DSS module effectively models both global dependencies and local representations by integrating parallel-cascaded Hybrid State Space (HSS) blocks and frequency-enhanced convolution operations. The structure of the Hybrid State Space (HSS) block is tailored by following the SSD-based Mamba lineage and incorporating Mamba3-style position-aware state-space modeling, leveraging the dual computational paths of linear recurrence and parallel
matrix formulation to model local continuity and global contextual comparison, thereby better serving the core anomaly detection objective of precisely reconstructing normal representations while magnifying anomalous deviations. Additionally, we propose a semantics-adaptive progressive scanning strategy that decays scanning complexity along the feature pyramid.
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All authors Xiaobin Hu, Haoyang He, Bo Yin, Yu He, Lei Xie, Jiangning Zhang, Yu-Gang Jiang, Shuicheng Yan
Computer Vision
LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions
Aman Kumar Pandey and Anil Singh Parihar
Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world scenes. Existing low-light
datasets often lack sufficient action diversity, capture realism, or balanced class distribution, limiting the development of robust models. To address this, we introduce LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions, comprising 6,784 clips across 26 action classes, recorded from 22 subjects across 20 indoor and outdoor locations under naturally occurring low-light conditions. We also propose Illumi-Net: An Illumination-Adaptive Mixture-of-Experts Network, which leverages
video-level illumination cues to guide adaptive enhancement and
transformer-based spatio-temporal feature extraction, with expert-conditioned decision fusion. Our method surpasses previous state-of-the-art performance on ELLAR (Top-1: 55.13%, Top-5: 78.87%) and establishes a strong baseline on LUMINA-26 (Top-1: 75.95%, Top-5: 93.58%), offering a practical
benchmark for future low-light action recognition research.
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Computer Vision
Technical Report for the ICRA 2026 GOOSE 2D Fine-Grained Semantic Segmentation Challenge: Pretraining-Diverse Ensemble of Foundation Vision Encoders for Robust Outdoor Scene Understanding
Boyan Wang, Yongxi Huang, Wenjing Li et al. (+4 more)
This report presents our solution for the ICRA 2026 GOOSE 2D Fine-Grained Semantic
Segmentation Challenge, which requires parsing unstructured outdoor scenes from four camera platforms into 56 fine-grained categories. Our approach pairs foundation
vision encoders (including DINOv3, SigLIP2, and InternImage) with a Mask2Former decoder, and trains them with a strong recipe including long training schedules, exponential moving average, a larger crop size, and multi-scale plus flip test-time augmentation. The three encoders, chosen for their complementary pretraining objectives, are combined into a pretraining-diverse ensemble through per-class validation-IoU weighting. Evaluated on the official GOOSE test set, our submission achieves 75.40% composite mIoU and wins the second place of the challenge. Our study further shows that the encoder's pretraining recipe, rather than its parameter count or the decoder design, is the dominant factor for accuracy on this
benchmark.
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All authors Boyan Wang, Yongxi Huang, Wenjing Li, Tianrui Hui, Shaofei Huang, Nan Pu, Zhun Zhong
Computer Vision
Zhan Peng, Jie Ma, Huiqiang Sun et al. (+6 more)
Video world models hold promise for simulating interactive environments, yet maintaining consistent long-term
memory across complex camera trajectories remains a critical challenge. Existing methods typically rely on computationally expensive context scaling or rigid heuristic
retrieval mechanisms, which lacks generalization to varying camera trajectories and environments. In this paper, we propose Compression and
Retrieval (CaR), an
attention-driven implicit
memory retrieval mechanism to overcome these limitations. By injecting viewpoint information via positional encoding, our method performs flexible
memory retrieval through
attention computation. To efficiently process extended contexts with minimal computational overhead, we further introduce a lightweight context compression network. Furthermore, we construct SceneFly, a large-scale synthetic
dataset featuring realistic camera trajectories and frame-level annotations to train and evaluate long-horizon
video world models. Extensive experiments demonstrate that our approach achieves state-of-the-art results on established
benchmarks and exhibits strong generalization to open-domain scenes.
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All authors Zhan Peng, Jie Ma, Huiqiang Sun, Chong Gao, Zhijie Xue, Zhiyu Pan, Zhiguo Cao, Jun Liang, Jing Li
Computer Vision
Scene-agnostic ALS boresight self-calibration
Aurélien Brun and Jan Skaloud
ALS boresight calibration has relied for two decades on dedicated flight patterns over structured scenes containing planar surfaces of varied aspect and slope. While reliable, this approach imposes constraints on the scene content and operations, which limits its applicability to boresight recovery within routine mapping missions. We present a practical approach that substantially relaxes these requirements by replacing plane-based constraints with scene-agnostic point-to-point correspondences extracted automatically from overlapping ALS strips. Two complementary formulations are proposed to estimate boresight with
laser vector observations: (i) a simpler parametric adjustment utilizing INS/GNSS trajectory; (ii) a rigorous formulation treating GNSS and raw inertial data within an existing factor-graph, i.e. a dynamic network, where boresight is added as an additional parameter. Both formulations are evaluated across four operational ALS flights equipped with five inertial systems, covering a wide range of flight altitudes, overlap geometries, terrain types and inertial sensor classes. The analysis draws a clear boundary between the legacy plane-based conditioning that falls short outside the calibration scenario and the proposed formulations, which either recover or absorb boresight effects under conventional mapping geometry. Among them, the lightweight formulation is sufficient for boresight recovery using tactical and
navigation grade inertial sensors, while the general factor-graph approach is clearly superior when the inertial sensor errors are less observable within an optimal smoother. This supports the hypothesis that, for INS/GNSS trajectory of sufficient quality, the boresight calibration can be performed without particular scene prerequisites during routine mapping operations using a minimum of 3-4 overlapping strips, with either proposed formulation...
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Computer Vision
Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoising
Xirou Zhou, Zijing Xu, Yibo Qu et al. (+3 more)
The
quantum nature of light determines the inherent Poisson stochasticity of
photon detection, which is ubiquitous in photography, microscopy, and astronomy. However, our controlled numerical studies reveal that the signal-dependency, heteroscedasticity, and statistical asymmetry of Poisson-mixed noise make it challenging for existing denoisers to learn. In contrast, i.i.d. Gaussian noise, with its statistical independence and symmetric distribution, is easier to model for networks. To address this gap, we propose Poisson2Gaussian (P2G), a noise Gaussianization method that explicitly converts complex real-world noise to i.i.d. Gaussian noise via probability density matching beyond low-order moments. We also design an unbiased denoising framework that synergizes P2G with downstream denoisers, ensuring convergence to the underlying signal without requiring paired clean data or explicit noise parameters. Extensive experiments demonstrate that P2G consistently achieves state-of-the-art performance across diverse
datasets. In challenging scenarios where noise strongly deviates from Gaussian statistics, our method improves the PSNR by up to 0.75 dB. Notably, P2G is architecture-agnostic and can provide universal improvements for various denoisers. The source code will be publicly available.
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All authors Xirou Zhou, Zijing Xu, Yibo Qu, Qi Zhang, Xiaowan Hu, Xinyang Li
Computer Vision
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection
KunHo Heo, Seungjae Kim, Wongyu Lee et al. (+2 more)
Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent prototype-based similarity learning approaches enable training-free adaptation by matching query features with class prototypes. However, they suffer from two fundamental limitations: (i) class confusion arising from inter-class similarity margin collapse, and (ii) insufficient visual cues for precise localization, as similarity scores capture only class-level semantic affinity while providing limited spatial information. To address these issues, we introduce two complementary components. Text-Anchored Semantic Mask (TSMa) leverages class-level text features as semantic anchors to identify semantically aligned channels through channel-wise interaction between visual and text features. By suppressing style-induced spurious responses and emphasizing class-intrinsic signals, TSMa enlarges inter-class similarity margins and mitigates class confusion. We further propose Stage-Aligned Hierarchical Autoregressive Regression (SHARe), which reformulates localization as a hierarchical autoregressive process that progressively refines bounding boxes across multiple stages. SHARe leverages the layer-wise characteristics of ViT representations by aligning feature abstraction levels with regression stages: deeper layers guide early coarse localization, while shallower layers rich in edge and texture cues refine spatial details in later stages. Experiments on COCO demonstrate a new state of the art, outperforming the previous best by +10.1 nAP, with extensive analysis validating each component. The code is available at https://github.com/VisualScienceLab-KHU/ReSet.
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All authors KunHo Heo, Seungjae Kim, Wongyu Lee, SuYeon Kim, MyeongAh Cho
Computer Vision
Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs
Chuangxin Zhao, Canran Xiao, Siyuan Ma et al. (+5 more)
Multimodal large
language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instructions, yet continual fine-tuning often causes severe forgetting of previously acquired
multimodal skills. Existing continual
vision-
language methods mainly preserve outputs, replay data or pseudo-data, regularize
embedding geometry, or allocate task-specific parameters, but they provide limited control over how internal cross-modal
attention patterns supporting old skills drift during adaptation. We propose
Attention-Spectrum Regularization (ASR), a replay-free continual learning framework that preserves skill-conditioned structures of cross-modal
attention. ASR treats cross-
attention maps as two-dimensional signals, summarizes their scale and directional properties into compact spectral statistics, and stores only skill-wise prototype distributions instead of replaying past image-question pairs, generated pseudo-examples, or old-stage teacher snapshots. In later stages, a phase-invariant spectral regularizer constrains harmful drift of these prototypes while allowing instance-level
attention to adapt to new tasks. We provide theoretical analysis showing that skill-conditioned spectral drift controls forgetting under a spectral sufficiency assumption, and that
Fourier power spectra are stable to spatial translations and bounded perturbations. Experiments on continual VQA and
multimodal instruction-tuning
benchmarks, including VQA v2, VQACL, CLT-VQA, CoIN, and UCIT, show that ASR consistently improves final performance and reduces forgetting over strong replay-, regularization-, and adapter-based baselines. Preserving skill-level
attention structure is an effective and lightweight mechanism for continual MLLMs. Code is available at https://github.com/Creative-zcx/
attention-spectrum-replay
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All authors Chuangxin Zhao, Canran Xiao, Siyuan Ma, Mengyao Lyu, Yanbiao Ma, Jun Xia, Guiguang Ding, Yang Liu
Computer Vision · Cross-listed from cs.GR
VolHuMe: a High-Resolution Large Scale Dataset of Volumetric Human Meshes
Giulia Martinelli, Niccolò Bisagno, Nicola Garau et al. (+2 more)
We introduce VolHuMe, a
dataset of high-quality 4D human scans captured with a state-of-the-art volumetric studio using 64 RGB and 32 depth cameras. VolHuMe contains individual captures of 104 subjects and provides extensive ground truth, including SMPL-X, high-resolution meshes, multi-view RGB/depth images, rigged meshes, point clouds, garment
segmentation, and detailed hand and facial geometry. Unlike prior
datasets that primarily rely on full-body imagery, VolHuMe uses a close-range, high-resolution capture setup that preserves fine-grained body-part details, improving
geometric fidelity and texture resolution. We
benchmark VolHuMe on state-of-the-art methods across 3D and 4D human reconstruction tasks, showcasing the
dataset's quality and exposing the limitations of current evaluation testbeds.
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All authors Giulia Martinelli, Niccolò Bisagno, Nicola Garau, Esa Rahtu, Nicola Conci
Computer Vision
MotionHalluc: Diagnosing Kinematic Hallucinations in Fine-Grained Motion Reasoning
Weile Guo, Shenghong He, Danying Mo et al. (+3 more)
Motion instruction generation in cross-
video comparison aims to produce corrective feedback that describes the differences between a query and a reference motion. However, existing models often generate instructions that exhibit motion
hallucinations, failing to reflect actual kinematic differences between paired
videos. To systematically investigate these
hallucinations, we introduce MotionHalluc, a dedicated
benchmark for evaluating motion
hallucinations in paired-
video comparison. MotionHalluc comprises 1540 fine-grained questions over 553
video pairs, evaluating
hallucinations along three core dimensions: (1)directional
hallucination, (2)attributional
hallucination, and (3)temporal
hallucination. Extensive evaluations of state-of-the-art large
multimodal models demonstrate high susceptibility to these
hallucinations. Furthermore, we provide Perceive-Parse-Verify (PPV) as a training-free measurements extraction and verification baseline that converts candidate instructions into executable measurement queries and supplies kinematic measurements at inference time. Our results show that this simple measurements injection yields an average 10.6% performance gain across models, suggesting that motion
reasoning with explicit quantitative measurements is a key factor in reducing
hallucinations in cross-
video comparison. Our code and
dataset are available at https://github.com/Leocoding0971/MotionHalluc.
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All authors Weile Guo, Shenghong He, Danying Mo, Chengdong Xu, Xuexun Liu, Chao Yu
Computer Vision
Three-Step Hierarchical Transformer for Multi-Pedestrian Trajectory Prediction
Raphaël Delécluse, Hazem Wannous, Laurent Grisoni et al. (+1 more)
Pedestrian trajectory prediction requires modeling temporal dynamics,
multimodal cues, and social interactions in crowded environments. Existing methods often address these factors separately or entangle them in costly
attention blocks, limiting scalability, flexibility, and interpretability. We propose a three-step hierarchical
Transformer that explicitly separates temporal encoding,
multimodal fusion, and scene-level interaction
reasoning. Lightweight GRU summaries enable efficient cross-modal
attention, while social
attention over time–
agent tokens captures inter-pedestrian influences at manageable cost. Experiments on JTA, JRDB, and the Pedestrians and Cyclists in Road Traffic
dataset show state-of-the-art performance on real-world
datasets (JRDB, Urban) and competitive results on JTA. Ablation and qualitative analyses confirm the contribution of each stage and the model's ability to anticipate complex behaviors such as early turning.
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All authors Raphaël Delécluse, Hazem Wannous, Laurent Grisoni, Laurent Guimas