GenAI4ELab/papercli-papers huggingface dataset:
Zero-LEAD: Source-Free Universal Domain Adaptation for Abdominal Multi-Organ Segmentation
Authors: Ahmed El-Sayed; Marwan Torki
Venue: WACV 2026 (main)

Abstract: Cross-modality medical image segmentation is critical for diagnosis and treatment planning, yet domain shifts and source data restrictions pose significant challenges. This paper introduces Zero-LEAD, the first unified framework for source-free universal domain adaptation (SF-UniDA) in segmentation, addressing all four UDA scenarios, closed-set, partial-set, open-set, and universal-set, without access to source data. Zero-LEAD integrates (1) Label-Efficient Adaptive Decomposition (LEAD) to decompose features into source-known and source-unknown components, and (2) a zero-shot segmentation module leveraging anatomical priors and semantic attributes to segment novel target classes.Extensive experiments across four datasets, Synapse, CHAOS, BTCV, and FLARE22, demonstrate strong performance across all adaptation settings. Zero-LEAD achieves 0.9159 Dice in closed-set (Synapse->CHAOS), 0.8721 Dice in partial-set (BTCV->CHAOS), 0.7801 Dice in open-set (Synapse->BTCV), and 0.7716/0.6866 Dice in universal-set (BTCV\leftrightarrowFLARE22), significantly outperforming state-of-the-art baselines. Ablation studies confirm the complementary contributions of LEAD and zero-shot modules, and qualitative analysis highlights improved boundary precision and robustness under both domain and label shifts.
Cross-Modal Event Encoder: Bridging Image-Text Knowledge to Event Streams
Authors: Sungheon Jeong; Hanning Chen; Sanggeon Yun; Suhyeon Cho; Wenjun Huang; Xiangjian Liu; Mohsen Imani
Venue: WACV 2026 (main)

Abstract: We introduce a robust event-centric encoder that expands the practical reach of event-based data across diverse tasks. To overcome the scarcity of large-scale event datasets, our approach adapts CLIP's representation space to the event domain, preserving zero-shot learning and text alignment while mitigating catastrophic forgetting. By explicitly aligning event and image embeddings, the proposed encoder retains CLIP's core strengths and delivers competitive performance on object recognition as well as zero-shot and few-shot benchmarks. Moreover, it generalizes effectively to event streams derived from video datasets without additional training. Finally, we show that the encoder integrates seamlessly into cross-modal architectures, enabling accurate event-image retrieval and unlocking new applications for the event modality.
Dual-Domain Multimodal Hyperbolic Fusion for Cardiopulmonary Disease Diagnosis in Emergency Care
Authors: Ke Nan; Maggie Samaan; Benjamin Burns; Xia Ning; Yuchi Han; Yuan Xue
Venue: WACV 2026 (main)

Abstract: Differentiating between cardiac and pulmonary diseases in emergency settings presents a significant challenge due to overlapping symptoms like dyspnea and chest pain, where misdiagnosis can lead to inappropriate interventions and increased morbidity. While electrocardiograms (ECGs) and chest X-rays (CXRs) provide complementary diagnostic information, existing multimodal fusion approaches fail to fully capture the complex relationships between these fundamentally different data modalities. To address these limitations, we propose DDMF-Net, a Dual-Domain Multimodal Fusion Network that explicitly unifies multi-domain features--from both frequency and spatial/temporal perspectives--and conducts cross-modality fusion of ECG, CXR signals and clinical parameters in hyperbolic space, thereby enhancing the modeling of complex cardiopulmonary pathophysiology. Our framework contains three innovations: (1) a frequency fusion module that captures complementary spectral patterns across modalities, (2) an inter-domain fusion module that dynamically balances domain-specific features, and (3) a hyperbolic cross-attention module with soft-entailment loss that effectively models hierarchical relationships between low-level imaging/signal data and high-level clinical parameters. Evaluated on four MIMIC datasets, DDMF-Net achieves state-of-the-art performance with over 2.9% improvement in micro-AUC, enabling more accurate differentiation of cardiac and pulmonary conditions in time-sensitive emergency settings. Code is publicly available at https://github.com/ kknankk/DDMF_Net.
Procedure Learning via Regularized Gromov-Wasserstein Optimal Transport
Authors: Syed Ahmed Mahmood; Ali Shah Ali; Umer Ahmed; Fawad Javed Fateh; M. Zeeshan Zia; Quoc-Huy Tran
Venue: WACV 2026 (main)

Abstract: We study the problem of self-supervised procedure learning, which discovers key steps and establishes their order from a set of unlabeled procedural videos. Previous procedure learning methods typically learn frame-to-frame correspondences between videos before determining key steps and their order. However, their performance often suffers from order variations, background/redundant frames, and repeated actions. To overcome these challenges, we propose a self-supervised procedure learning framework, which utilizes a fused Gromov-Wasserstein optimal transport formulation with a structural prior for computing frame-to-frame mapping between videos. However, optimizing exclusively for the above temporal alignment term may lead to degenerate solutions, where all frames are mapped to a small cluster in the embedding space and hence every video is associated with only one key step. To address that limitation, we further integrate a contrastive regularization term, which maps different frames to different points in the embedding space, avoiding the collapse to trivial solutions. Finally, we conduct extensive experiments on large-scale egocentric (i.e., EgoProceL) and third-person (i.e., ProceL and CrossTask) benchmarks to demonstrate superior performance by our approach against previous methods, including OPEL which relies on a traditional Kantorovich optimal transport formulation with an optimality prior.
PDV: Prompt Directional Vectors for Zero-shot Composed Image Retrieval
Authors: Osman Tursun; Sinan Kalkan; Simon Denman; Clinton Fookes
Venue: WACV 2026 (main)

Abstract: Zero-shot Composed Image Retrieval (ZS-CIR) enables image search using a reference image and a text prompt without requiring specialized text-image composition networks trained on large-scale paired data. However, current ZS-CIR approaches suffer from three critical limitations in their reliance on composed text embeddings: static query embedding representations, insufficient utilization of image embeddings, and suboptimal performance when fusing text and image embeddings. To address these challenges, we introduce the Prompt Directional Vector (PDV), a simple yet effective training-free enhancement that captures semantic modifications induced by user prompts. PDV enables three key improvements: (1) Dynamic composed text embeddings where prompt adjustments are controllable via a scaling factor, (2) composed image embeddings through semantic transfer from text prompts to image features, and (3) weighted fusion of composed text and image embeddings that enhances retrieval by balancing visual and semantic similarity. Our approach serves as a plug-and-play enhancement for existing ZS-CIR methods with minimal computational overhead. Extensive experiments across multiple benchmarks demonstrate that PDV consistently improves retrieval performance when integrated with state-of-the-art ZS-CIR approaches, particularly for methods that generate accurate compositional embeddings. The code will be released upon publication.
Any Detector Can Detect Anything
Authors: Thomas E. Huang; Siyuan Li; Martin Danelljan; Henghui Ding; Luc Van Gool; Fisher Yu
Venue: WACV 2026 (main)

Abstract: Visual prompt-based detection enables generalization to arbitrary novel instances in the target image by using one or a few visual templates. Previous methods rely on complex relation or explicit feature matching modules, and their designs are deeply coupled with specific detectors, greatly limiting their applicability. Instead, we propose our `Any Detector can Detect Anything' framework that can enable any detector to detect any object given a single or a few visual templates. Specifically, we design an adapter called Template-Aware Adapter that can be added on top of any existing detector architecture to inject visual template information directly into the detection features. After integration, localization is done on the feature maps as in standard object detectors, effectively transforming any detector into a visual prompt-based detector. Furthermore, we revisit current visual prompt detection benchmarks and correct their unrealistic test assumptions and class splits, which limit the usability of the developed algorithms in the real world. We introduce a set of realistic benchmarks to remedy these issues. We comprehensively evaluate the proposed model on both existing and our new benchmarks, outperforming current state-of-the-art one-shot and few-shot detection methods by a large margin.
Automated Suturing Skill Assessment in Robot-assisted Surgery from Endoscopic Videos using Clinically-guided Evaluation Criteria
Authors: Atharva Sunil Deo; Ujjwal Pasupulety; Nicholas Matsumoto; Jay Moran; Cherine Yang; Jeanine Kim; Rafal Dariusz Kocielnik; Aurash Naser-Tavakolian; Andrew Hung
Venue: WACV 2026 (main)

Abstract: Surgery continues to be perceived as an art, where proficiency is primarily achieved through years of experience. Artificial Intelligence research has yielded insight into the performance of expert surgeons and their associations with patient outcomes. Clinician expertise has led to the development of systematic assessments for fundamental skills (e.g., End-to-End Assessment of Suturing Expertise [EASE]) that contribute to positive outcomes. However, evaluating these skills requires manual expert review of endoscopic videos and is prone to inconsistencies between human raters. In this work, we present AutoEASE, the first end-to-end pipeline to automatically assess suturing performance from raw endoscopic video data using EASE rubrics. Our system utilizes a Mixture of Expert models (MoE) ; Multiscale vision transformers and 3D convolutional neural networks trained on Robot-assisted Radical Prostatectomy videos with over 13000 data points. For a given stitch clip, the MoE pipeline first determines each phase (needle handling, driving, withdrawal) of a continuous stitch and predicts a binary score (fail / ideal) for seven sub-skills based on rubrics defined in EASE. AutoEASE achieves 0.98 AUC while detecting each stitch phase. For EASE score prediction, the complete end-to-end pipeline attains >= 0.77 AUC in sub-skills associated with needle handling and driving. The promising performance of AutoEASE at the individual stitch level demonstrates the feasibility of developing more sophisticated assessment and reporting tools for complete surgical procedures objectively and at scale.
Efficient Text-Guided Convolutional Adapter for the Diffusion Model
Authors: Aryan Das; Koushik Biswas; Swalpa Kumar Roy; Badri Narayana Patro; Vinay Kumar Verma
Venue: WACV 2026 (main)

Abstract: We propose Nexus Adapters, a novel family of text-guided and efficient adapters designed for diffusion-based Structure Preserving Conditional Generation (SPCG). Existing structure-preserving methods typically rely on using a base diffusion model conditioned on a prompt and a separate adapter for structural inputs like sketches or depth maps. However, such approaches are computationally expensive, often requiring adapter sizes comparable to the base model, making training impractical due to the already high cost of diffusion models. Moreover, traditional adapters operate independently of the input prompt, making them suboptimal for capturing the semantic alignment between textual and structural cues. To address these limitations, we introduce Nexus Prime and Nexus Slim, two prompt-aware adapters that jointly leverage both text and structural inputs. These adapters are composed of modular Nexus Blocks that utilize cross-attention mechanisms to enable effective multimodal conditioning. As a result, the adapters can maintain structural fidelity while aligning more closely with the intended textual prompt. Experimental results demonstrate that Nexus Prime significantly improves performance with only 8M additional parameters over the minimal T2I baseline. Additionally, Nexus Slim, a lightweight variant with 18M fewer parameters than T2I, still achieves competitive, state-of-the-art results--validating the efficiency and effectiveness of our proposed design.
Enhancing Object Detection Training via Joint Image-Annotation Generation
Authors: Roy Uziel; Oded Bialer
Venue: WACV 2026 (main)

Abstract: Incorporating generated annotated data into training sets can improve object detection. Prior approaches either condition image generation on annotation layouts, limiting diversity and often causing misalignment, or generate images independently and annotate them afterward, reducing accuracy. We introduce a diffusion model that jointly generates images and annotations, enabling their co-evolution and mutual dependency throughout the process. This design achieves tight image-annotation alignment and produces diverse scenarios beyond the original training set, enhancing object detection performance when used in training.
Direct Visual Grounding by Directing Attention of Visual Tokens
Authors: Parsa Esmaeilkhani; Longin Jan Latecki
Venue: WACV 2026 (main)

Abstract: Vision Language Models (VLMs) mix visual tokens and text tokens. A puzzling issue is the fact that visual tokens most related to the query receive little to no attention in the final layers of the LLM module of VLMs from the answer tokens, where all tokens are treated equally, in particular, visual and language tokens in the LLM attention layers. This fact may result in wrong answers to visual questions, as our experimental results confirm. It appears that the standard next-token prediction (NTP) loss provides an insufficient signal for directing attention to visual tokens. We hypothesize that a more direct supervision of the attention of visual tokens to corresponding language tokens in the LLM module of VLMs will lead to improved performance on visual tasks. To demonstrate that this is indeed the case, we propose a novel loss function that directly supervises the attention of visual tokens. It directly grounds the answer language tokens in images by directing their attention to the relevant visual tokens. This is achieved by aligning the attention distribution of visual tokens to ground truth attention maps with KL divergence. The ground truth attention maps are obtained from task geometry in synthetic cases or from standard grounding annotations (e.g., bounding boxes or point annotations) in real images, and are used inside the LLM for attention supervision without requiring new labels. The obtained KL attention loss (KLAL) when combined with NTP encourages VLMs to attend to relevant visual tokens while generating answer tokens. This results in notable improvements across geometric tasks, pointing, and referring expression comprehension on both synthetic and real-world data, as demonstrated by our experiments. We also introduce a new dataset to evaluate the line tracing abilities of VLMs. Surprisingly, even commercial VLMs do not perform well on this task.
Anatomically-guided Masked Autoencoder Pre-training for Aneurysm Detection
Authors: Alberto M. Ceballos Arroyo; Jisoo Kim; Chu-Hsuan Lin; Lei Qin; Geoffrey S. Young; Huaizu Jiang
Venue: WACV 2026 (main)

Abstract: Intracranial aneurysms are a major cause of morbidity and mortality worldwide, and detecting them manually is a complex, time-consuming task. Albeit automated solutions are desirable, the limited availability of training data makes it difficult to develop such solutions using typical supervised learning frameworks. In this work, we propose a novel pre-training strategy using more widely available unannotated head CT scan data to pre-train a 3D Vision Transformer model prior to fine-tuning for the aneurysm detection task. Specifically, we modify masked auto-encoder (MAE) pre-training in the following ways: we use a factorized self-attention mechanism to make 3D attention computationally viable, we restrict the masked patches to areas near arteries to focus on areas where aneurysms are likely to occur, and we reconstruct not only CT scan intensity values but also artery distance maps, which describe the distance between each voxel and the closest artery, thereby enhancing the backbone's learned representations. Compared with SOTA aneurysm detection models, our approach gains +2-7% absolute Sensitivity at a false positive rate of 0.5. Code and weights will be released.
DreamAnywhere: Object-Centric Panoramic 3D Scene Generation
Authors: Edoardo A. Dominici; Jozef Hladký; Floor Verhoeven; Lukas Radl; Thomas Deixelberger; Stefan Ainetter; Philipp Drescher; Stefan Hauswiesner; Arno Coomans; Giacomo Nazzaro; Konstantinos Vardis; Markus Steinberger
Venue: WACV 2026 (main)

Abstract: Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of this complex task, existing methods often generate environments that are only front-facing, lack visual fidelity, exhibit limited scene understanding, and are typically fine-tuned for either indoor or outdoor settings. In this work, we address these issues and propose DreamAnywhere, a modular system for the fast generation and prototyping of 3D scenes. Our system synthesizes a 360deg panoramic image from text, decomposes it into background and objects, constructs a complete 3D representation through hybrid inpainting, and lifts object masks to detailed 3D objects that are placed in the virtual environment. DreamAnywhere supports immersive navigation and intuitive object-level editing, making it ideal for scene exploration, visual mock-ups, and rapid prototyping -- all with minimal manual modeling. These features make our system particularly suitable for low-budget movie production, enabling quick iteration on scene layout and visual tone without the overhead of traditional 3D workflows. Our modular pipeline is highly customizable as it allows components to be replaced independently. Compared to current state-of-the-art text and image-based 3D scene generation approaches, DreamAnywhere shows significant improvements in coherence in novel view synthesis and achieves competitive image quality, demonstrating its effectiveness across diverse and challenging scenarios. A comprehensive user study demonstrates a clear preference for our method over existing approaches, validating both its technical robustness and practical usefulness.
Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices
Authors: Saeid Ghafouri; Mohsen Fayyaz; Xiangchen Li; Deepu John; Bo Ji; Dimitrios S. Nikolopoulos; Hans Vandierendonck
Venue: WACV 2026 (main)

Abstract: Real-time multi-label video classification on embedded devices is constrained by limited compute and energy budgets. Yet, video streams exhibit structural properties such as label sparsity, temporal continuity, and label co-occurrence that can be leveraged for more efficient inference. We introduce Polymorph, a context-aware framework that activates a minimal set of lightweight Low Rank Adapters (LoRA) per frame. Each adapter specializes in a subset of classes derived from co-occurrence patterns and is implemented as a LoRA weight over a shared backbone. At runtime, Polymorph dynamically selects and composes only the adapters needed to cover the active labels, avoiding full-model switching and weight merging. This modular strategy improves scalability while reducing latency, and energy overhead. Polymorph achieves 40% lower energy consumption and improves mAP by 9 points over strong baselines executing the TAO dataset.
KD360-VoxelBEV: LiDAR and 360-degree Camera Cross Modality Knowledge Distillation for Bird's-Eye-View Segmentation
Authors: Wenke E; Yixin Sun; Jiaxu Liu; Hubert P. H. Shum; Amir Atapour-Abarghouei; Toby P. Breckon
Venue: WACV 2026 (main)

Abstract: We present the first cross-modality distillation framework specifically tailored for single-panoramic-camera Bird's-Eye-View (BEV) segmentation. Our approach leverages a novel LiDAR image representation fused from range, intensity and ambient channels, together with a voxel-aligned view transformer that preserves spatial fidelity while enabling efficient BEV processing. During training, a high-capacity LiDAR and camera fusion Teacher network extracts both rich spatial and semantic features for cross-modality knowledge distillation into a lightweight Student network that relies solely on a single 360-degree panoramic camera image. Extensive experiments on the Dur360BEV dataset demonstrate that our teacher model significantly outperforms existing camera-based BEV segmentation methods, achieving a 25.6% IoU improvement. Meanwhile, the distilled Student network attains competitive performance with an 8.5% IoU gain and state-of-the-art inference speed of 31.2 FPS. Moreover, evaluations on KITTI-360 (two fisheye cameras) confirm that our distillation framework generalises to diverse camera setups, underscoring its feasibility and robustness. This approach reduces sensor complexity and deployment costs while providing a practical solution for efficient, low-cost BEV segmentation in real-world autonomous driving. The code is available at: https://github.com/Tom-E-Durham/KD360-VoxelBEV.
WSSSP-Net: Weakly Supervised Semantic Segmentation Plugin Network for Face Anti-Spoofing
Authors: Krzysztof Galus; Piotr Syga; Piotr Kawa
Venue: WACV 2026 (main)

Abstract: Face anti-spoofing (FAS) is essential for protecting facial-biometric systems from presentation attacks. We propose WSSSP-Net, a Weakly Supervised Semantic Segmentation Plugin Network that integrates a lightweight, attention-based segmentation decoder at multiple depths of any CNN or transformer encoder. Serving only as an auxiliary training-time module, the decoder guides feature learning without increasing inference runtime. Pixel-wise spoof masks are automatically generated via a face-parsing pipeline, removing the need for manual annotations and enabling multiscale spoof-aware feature refinement. In leave-one-out evaluations on leading FAS benchmarks, WSSSP-Net reduces HTER by up to 24.9% and increases AUC by up to 3.2% over state-of-the-art methods. In out-of-distribution tests on a separate dataset, it lowers HTER by up to 18.4%. Across attack classes, it reduces average APCER by up to 12.9% and BPCER by up to 12.4%, achieving all improvements without added inference cost.