GenAI4ELab/papercli-papers huggingface dataset:
MedROV: Towards Real-Time Open-Vocabulary Detection Across Diverse Medical Imaging Modalities
Authors: Tooba Tehreem Sheikh; Jean Lahoud; Rao Muhammad Anwer; Fahad Shahbaz Khan; Salman Khan; Hisham Cholakkal
Venue: WACV 2026 (main)

Abstract: Traditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detection (OVOD) addresses this limitation but remains underexplored in medical imaging due to dataset scarcity and weak text-image alignment. To bridge this gap, we introduce MedROV, the first Real-time Open Vocabulary detection model for medical imaging. To enable open-vocabulary learning, we curate a large-scale dataset, Omnis, with 600K detection samples across nine imaging modalities and introduce a pseudo-labeling strategy to handle missing annotations from multi-source datasets. Additionally, we enhance generalization by incorporating knowledge from a large pre-trained foundation model. By leveraging contrastive learning and cross-modal representations, MedROV effectively detects both known and novel structures. Experimental results demonstrate that MedROV outperforms the previous state-of-the-art foundation model for medical image detection with an average absolute improvement of 40 mAP50, and surpasses closed-set detectors by more than 3 mAP50, while running at 70 FPS, setting a new benchmark in medical detection. Our source code, dataset, and trained model are available at https://github.com/toobatehreem/MedROV.
ConsensusXAI: A Framework to Examine Class-wise Agreement in Medical Imaging
Authors: Abbas Haider; David Wright; Ruth Hogg; Hui Wang; Tunde Peto; Richard Gault
Venue: WACV 2026 (main)

Abstract: Explainable AI (XAI) is essential for trust and transparencyin deep learning, especially in medical imaging.Existing local explanation methods provide per-instance insightsbut fail to show whether similar explanations holdacross samples of the same class. This limits global interpretabilityand demands time-consuming manual reviewby clinicians to trust models in practice. We introduce theConsensus Alignment Score (CAS), a novel metric thatquantifies consistency of explanations at the class level.We also present ConsensusXAI, an open-source, modelandmethod-agnostic framework that evaluates explanationagreement quantitatively (via CAS) and qualitatively(through consensus heatmaps) per class. Unlike priorbenchmarks, ConsensusXAI uses a latent-space clusteringapproach, Latent Consensus, to identify dominant explanationpatterns, exposing biases and inconsistencies towardscertain classes. Evaluated across four benchmark datasetsand two imaging modalities, our method consistently revealsmeaningful class-level insights, outperforming traditionalmetrics like SSIM and IoU, and enabling faster, moreconfident clinical adoption of AI models.
Towards Unconstrained Cross-View Pose Estimation
Authors: Alexander Wollam; Kyle Ashley; Maxim Shugaev; Oliver Arend; Ilya Semenov; Hadis Dashtestani; Sumved Ravi; Nathan Jacobs
Venue: WACV 2026 (main)

Abstract: Cross-view pose estimation entails predicting the relative 3 Degrees-of-Freedom (3DoF) pose of an image within an aerial view. Existing work focuses on imagery in controlled settings featuring highly constrained parameters. In contrast, a wide variety of camera parameterizations are seen in-the-wild across tasks where such estimation is useful. To address this gap, we propose a method capable of performing cross-view pose estimation in these less constrained scenarios with ground-view images of unknown FoV, pitch, roll, and projection type (panoramic or rectilinear). Namely, our method avoids common assumptions--such as gravity/horizon alignment needed for geometric-based projections--and purely relies on a transformer to learn the cross-view relationships in a data-driven manner, paired with prediction modules to enable continuous querying of the pose search space. Evaluations of our approach demonstrates it's ability to perform competitively with the state-of-the-art over the VIGOR benchmark, while maintaining performance in those harder less constrained scenarios. This supports our work as the first generalized approach to this task that is capable of operating with less-constrained imagery.
Towards Egocentric 3D Hand Pose Estimation in Unseen Domains
Authors: Wiktor Mucha; Michael Wray; Martin Kampel
Venue: WACV 2026 (main)

Abstract: We present V-HPOT, a novel approach for improving the zero-shot performance of 3D hand pose estimation from egocentric images across diverse, unseen domains. State-of-the-art methods demonstrate strong performance when trained and tested within the same domain. However, they struggle to generalise to new environments due to limited training data and depth perception -- overfitting to specific camera intrinsics. Our method addresses this by estimating keypoint z-coordinates in a virtual camera space, normalised by focal length and image size, enabling camera-agnostic depth prediction. We further leverage this invariance to camera intrinsics to propose a self-supervised test-time optimisation strategy that refines the model's depth perception during inference. This is achieved by applying a 3D consistency loss between predicted and in-space scale-transformed hand poses, allowing the model to adapt to target domain characteristics without requiring ground truth annotations. V-HPOT significantly improves 3D hand pose estimation performance in zero-shot scenarios, achieving a 71% reduction in mean pose error on the H2O dataset and a 41% reduction on the AssemblyHands dataset. Compared to state-of-the-art methods, V-HPOT outperforms all single-stage approaches across all datasets and competes closely with two-stage methods, despite needing x3.5 to x14 less data. The code is planned to be released under https://github.com/tobereleased.
Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation
Authors: Difei Gu; Yunhe Gao; Mu Zhou; Dimitris Metaxas
Venue: WACV 2026 (main)

Abstract: Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image features with clinical knowledge. Yet major vision-language models (VLMs) treat images as holistic entities and overlook fine-grained image details that are vital for disease diagnosis. Clinicians analyze images by utilizing their prior medical knowledge and identify anatomical structures as important region of interests (ROIs). Inspired from this human-centric workflow, we introduce Anatomy-VLM, a fine-grained, vision-language model that incorporates multi-scale information. First, we design a model encoder to localize key anatomical features from entire medical images. Second, these regions are enriched with structured knowledge for contextually-aware interpretation. Finally, the model encoder aligns multi-scale medical information to generate clinically-interpretable disease prediction. Anatomy-VLM achieves outstanding performance on both in- and out-of-distribution datasets. We also validate the performance of Anatomy-VLM on downstream image segmentation tasks, suggesting that its fine-grained alignment captures anatomical and pathology-related knowledge. Furthermore, the Anatomy-VLM's encoder facilitates zero-shot anatomy-wise interpretation, providing its strong expert-level clinical interpretation capabilities.
FocalComm: Hard Instance-Aware Multi-Agent Perception
Authors: Dereje Shenkut; Vijayakumar Bhagavatula
Venue: WACV 2026 (main)

Abstract: Multi-agent collaborative perception (CP) is a promising paradigm for improving autonomous driving safety, particularly for vulnerable road users like pedestrians, through robust 3D perception. However, existing CP approaches often optimize for vehicle detection performance metrics, underperforming on smaller, safety-critical objects such as pedestrians, where detection failures can be catastrophic. Furthermore, previous CP methods rely on full feature exchange rather than communicating only salient features that help reduce false negatives. To this end, we present FocalComm, a novel collaborative perception framework that focuses on exchanging hard-instance-oriented features among connected collaborative agents. FocalComm consists of two key novel designs: (1) a learnable progressive hard instance mining (HIM) module to extract hard instance-oriented features per agent, and (2) a query-based feature-level (intermediate) fusion technique that dynamically weights these identified features during collaboration. We show that FocalComm outperforms state-of-the-art collaborative perception methods on two challenging real-world datasets (V2X-Real and DAIR-V2X) across both vehicle-centric and infrastructure-centric collaborative setups. FocalComm also shows strong performance gains in pedestrian detection in V2X-Real.
SeaClips: A Video Dataset for Maritime Object Detection.
Authors: Franziska Denk; Christian Rankl; Shaban Almouahed; David Moser; Robert Sablatnig
Venue: WACV 2026 (main)

Abstract: Maritime computer vision is a requirement for autonomous surface vehicles and can improve maritime safety if a high level of robustness is achieved. As deep learning dominates the computer vision community, domain-specific datasets are required to obtain well-generalizing and reliable models. However, maritime datasets, especially those containing videos and temporally dense annotations, are still small compared to other domains, such as autonomous driving or generic computer vision datasets. This paper introduces SeaClips, a new maritime video dataset containing 74 videos with an average duration of 14 seconds and with 31k frames in total. Videos were recorded under varying conditions, with three cameras mounted on shore and on boats. SeaClips provides frame-by-frame annotations, encompassing 129k bounding boxes of seven categories, containing vessel and non-vessel classes. SeaClips contributes to a broader coverage of maritime scenarios, leading to more robust computer vision models. Baseline results on the dataset are established by evaluating six image-based models and three models using temporal context, ranging from lightweight models to heavy transformer architectures. It is found that the different scales and shapes at which objects appear in SeaClips pose a challenge to state-of-the-art detectors. SeaClips is accessible for research on maritime obstacle detection at: https://huggingface.co/datasets/SEA-AI/SeaClips.
Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control
Authors: Jason Armitage; Rico Sennrich
Venue: WACV 2026 (main)

Abstract: Cross-modal systems trained on 2D visual inputs are presented with a dimensional shift when processing 3D scenes. An in-scene camera bridges the dimensionality gap but requires learning a control module. We introduce a new method that improves multivariate mutual information estimates by regret minimisation with derivative-free optimisation. Our algorithm enables off-the-shelf cross-modal systems trained on 2D visual inputs to adapt online to object occlusions and differentiate features. The pairing of expressive measures and value-based optimisation assists control of an in-scene camera to learn directly from the noisy outputs of vision-language models. The resulting pipeline improves performance in cross-modal tasks on multi-object 3D scenes without resorting to pretraining or finetuning.
AGENet: Adaptive Edge-aware Geodesic Distance Learning for Few-Shot Medical Image Segmentation
Authors: Ziyuan Gao
Venue: WACV 2026 (main)

Abstract: Medical image segmentation requires large annotated datasets, creating a significant bottleneck for clinical applications. While few-shot segmentation methods can learn from minimal examples, existing approaches demonstrate suboptimal performance in precise boundary delineation for medical images, particularly when anatomically similar regions appear without sufficient spatial context. We propose AGENet (Adaptive Geodesic Edge-aware Network), a novel framework that incorporates spatial relationships through edge-aware geodesic distance learning. Our key insight is that medical structures follow predictable geometric patterns that can guide prototype extraction even with limited training data. Unlike methods relying on complex architectural components or heavy neural networks, our approach leverages computationally lightweight geometric modeling. The framework combines three main components: (1) An edge-aware geodesic distance learning module that respects anatomical boundaries through iterative Fast Marching refinement, (2) adaptive prototype extraction that captures both global structure and local boundary details via spatially-weighted aggregation, and (3) adaptive parameter learning that automatically adjusts to different organ characteristics. Extensive experiments across diverse medical imaging datasets demonstrate improvements over state-of-the-art methods. Notably, our method reduces boundary errors compared to existing approaches while maintaining computational efficiency, making it highly suitable for clinical applications requiring precise segmentation with limited annotated data.
GaussianHeadTalk: Wobble-Free 3D Talking Heads with Audio Driven Gaussian Splatting
Authors: Madhav Agarwal; Mingtian Zhang; Laura Sevilla-Lara; Steven McDonagh
Venue: WACV 2026 (main)

Abstract: Speech-driven talking heads have recently emerged and enable interactive avatars. However, real-world applications are limited, as current methods achieve high visual fidelity but slow or fast yet temporally unstable. Diffusion methods provide realistic image generation, yet struggle with one-shot settings. Gaussian Splatting approaches are real-time, yet inaccuracies in facial tracking, or inconsistent Gaussian mappings, lead to unstable outputs and video artifacts that are detrimental to realistic use cases. We address this problem by mapping Gaussian Splatting using 3D Morphable Models to generate person-specific avatars. We introduce transformer-based prediction of model parameters, directly from audio, to drive temporal consistency. From monocular video and independent audio speech inputs, our method enables generation of real-time talking head videos where we report competitive quantitative and qualitative performance.
SVD-Det: A Lightweight Framework for Video Forgery Detection Using Semantic and Visual Defect Cues
Authors: Tsung-Shan Yang; Tianyu Zhang; Feng Qian; Bing Yan; C.-C. Jay Kuo
Venue: WACV 2026 (main)

Abstract: With the rapid proliferation of AI-generated content (AIGC) on multimedia platforms, efficient and reliable video forgery detection has become increasingly important. Existing approaches often rely on either visual artifacts or semantic inconsistencies, but suffer from high computational costs, limiting their deployment at scale. In this work, we propose SVD-Det, a lightweight and efficient pipeline that leverages both semantic and Visual Defect cues to detect forged videos. SVD-Det fuses spatiotemporal representations from raw RGB frames and compression-induced distortions using a 3D-Swin Transformer, and augments semantic understanding via CLIP-based embeddings. To integrate these heterogeneous modalities, we introduce Domain-Query Attention (DoQA), a novel attention mechanism that hierarchically aggregates spatial and temporal features. Experiments across seven video generation domains demonstrate that SVD-Det not only achieves state-of-the-art detection performance but also reduces model size and inference time by over 97% and 98%, respectively, compared to LMM-based baselines. Our results highlight the practicality and robustness of SVD-Det for scalable AIGC detection in real-world scenarios.
milliMamba: Specular-Aware Human Pose Estimation via Dual mmWave Radar with Multi-Frame Mamba Fusion
Authors: Niraj Prakash Kini; Shiau-Rung Tsai; Guan-Hsun Lin; Wen-Hsiao Peng; Ching-Wen Ma; Jenq-Neng Hwang
Venue: WACV 2026 (main)

Abstract: Millimeter-wave radar offers a privacy-preserving and lighting-invariant alternative to RGB sensors for Human Pose Estimation (HPE) task. However, the radar signals are often sparse due to specular reflection, making the extraction of robust features from radar signals highly challenging. To address this, we present milliMamba, a radar-based 2D human pose estimation framework that jointly models spatio-temporal dependencies across both the feature extraction and decoding stages. Specifically, given the high dimensionality of radar inputs, we adopt a Cross-View Fusion Mamba encoder to efficiently extract spatio-temporal features from longer sequences with linear complexity. A Spatio-Temporal-Cross Attention decoder then predicts joint coordinates across multiple frames. Together, this spatio-temporal modeling pipeline enables the model to leverage contextual cues from neighboring frames and joints to infer missing joints caused by specular reflections. To reinforce motion smoothness, we incorporate a velocity loss alongside the standard keypoint loss during training. Experiments on the TransHuPR and HuPR datasets demonstrate that our method achieves significant performance improvements, exceeding the baselines by 11.0 AP and 14.6 AP, respectively, while maintaining reasonable complexity. Our code will be released upon publication.
One-Cycle Structured Pruning via Stability-Driven Subnetwork Search
Authors: Deepak Ghimire; Dayoung Kil; Seonghwan Jeong; Jaesik Park; Seong-heum Kim
Venue: WACV 2026 (main)

Abstract: Existing structured pruning typically involves multi-stage training procedures that often demand heavy computation. Pruning at initialization, which aims to address this limitation, reduces training costs but struggles with performance. To address these challenges, we propose an efficient one-cycle structured pruning framework that integrates pre-training, pruning, and fine-tuning into a single training cycle without compromising model performance, referred to as the `one-cycle approach'. The core idea is to search for the optimal sub-network during the early stages of network training, guided by norm-based group saliency criteria and structured sparsity regularization. We introduce a novel pruning indicator that identifies the stable pruning epoch by measuring the similarity between evolving pruning sub-networks across consecutive training epochs. Additionally, group sparsity regularization helps accelerate the pruning process, thereby speeding up overall training. Extensive experiments on the CIFAR-10/100 and ImageNet datasets using VGGNet, ResNet, and MobileNet architectures demonstrate that our method achieves state-of-the-art accuracy while being one of the most efficient pruning frameworks in terms of training time. Our code is available at https://github.com/ghimiredhikura/OCSPruner.
Hestia: Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction
Authors: Cheng-You Lu; Zhuoli Zhuang; Nguyen Thanh Trung Le; Da Xiao; Yu-Cheng Chang; Thomas Do; Srinath Sridhar; Chin-Teng Lin
Venue: WACV 2026 (main)

Abstract: Advances in 3D reconstruction and novel view synthesis have enabled efficient and photorealistic rendering. However, images for reconstruction are still either largely manual or constrained by simple preplanned trajectories. To address this issue, recent works propose generalizable next-best-view planners that do not require online learning. Nevertheless, robustness and performance remain limited across various shapes. Hence, this study introduces Voxel-Face-Aware Hierarchical Next-Best-View Acquisition for Efficient 3D Reconstruction (Hestia), which addresses the shortcomings of the reinforcement learning-based generalizable approaches for five-degree-of-freedom viewpoint prediction. Hestia systematically improves the planners through four components: a more diverse dataset to promote robustness, a hierarchical structure to manage the high-dimensional continuous action search space, a close-greedy strategy to mitigate spurious correlations, and a face-aware design to avoid overlooking geometry. Experimental results show that Hestia achieves non-marginal improvements, with at least a 4% gain in coverage ratio, while reducing Chamfer Distance by 50% and maintaining real-time inference. In addition, Hestia outperforms prior methods by at least 12% in coverage ratio with a 5-image budget and remains robust to object placement variations. Finally, we demonstrate that Hestia, as a next-best-view planner, is feasible for the real-world application. Our project page is https://johnnylu305.github.io/hestia web.
MIX-based Foreground and Background Patch Augmentation Guided by Physics and Material Properties for X-ray Detection
Authors: Xintong Liu; Dongliang Chang; Yujun Tong; Zhanyu Ma
Venue: WACV 2026 (main)

Abstract: The performance of deep learning-based models for X-ray prohibited item detection heavily relies on large-scale, diverse datasets, which are often unavailable. While data augmentation offers a promising solution, prevalent methods ignore the fundamental principles of X-ray imaging, leading to artifacts such as distorted material properties and unnatural thickness perturbations. To bridge this gap, we present MIX, a physics-grounded data augmentation pipeline. The core idea of MIX is to manipulate image attributes in a way that reflects real-world physical variations. Our contributions are twofold: (1) To address material ambiguity, MIX modulates foreground pseudo-colors by directly manipulating hue and saturation, informed by the relationship between color and effective atomic number. This forces the model to learn more robust material representations. (2) To simulate variations in object density and thickness, MIX introduces a novel thickness perturbation technique based on X-ray attenuation principles. This significantly improves the model's adaptability to geometric changes. Our proposed method seamlessly integrates with existing detectors and yields substantial performance gains across multiple benchmarks. Our work not only provides an effective augmentation solution but also highlights the critical need for domain-specific approaches in X-ray computer vision.
SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Authors: Andreas Lolos; Stergios Christodoulidis; Aris L. Moustakas; Jose Dolz; Maria Vakalopoulou
Venue: WACV 2026 (main)

Abstract: Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This is usually the case in digital pathology, which consists of gigapixel sized images. While deterministic attention-based MIL approaches achieve strong bag-level performance, they often overlook the uncertainty inherent in instance relevance. In this paper, we address the lack of uncertainty quantification in instance-level attention scores by introducing SGPMIL, a new probabilistic attention-based MIL framework grounded in Sparse Gaussian Processes (SGP). By learning a posterior distribution over attention scores, SGPMIL enables principled uncertainty estimation, resulting in more reliable and calibrated instance relevance maps. Our approach not only preserves competitive bag-level performance but also significantly improves the quality and interpretability of instance-level predictions under uncertainty. SGPMIL extends prior work by introducing feature scaling in the SGP predictive mean function, leading to faster training, improved efficiency, and enhanced instance-level performance. Extensive experiments on multiple well-established digital pathology datasets highlight the effectiveness of our approach across both bag- and instance-level evaluations. Our code is available at https://github.com/mandlos/SGPMIL.