GenAI4ELab/papercli-papers huggingface dataset:
A Little More Like This: Text-to-Image Retrieval with Vision-Language Models Using Relevance Feedback
Authors: Bulat Khaertdinov; Mirela Popa; Nava Tintarev
Venue: WACV 2026 (main)

Abstract: Large vision-language models (VLMs) enable intuitive visual search using natural language queries. However, improving their performance often requires fine-tuning and scaling to larger model variants. In this work, we propose a mechanism inspired by traditional text-based search to improve retrieval performance at inference time: relevance feedback. While relevance feedback can serve as an alternative to fine-tuning, its model-agnostic design also enables use with fine-tuned VLMs. Specifically, we introduce and evaluate four feedback strategies for VLM-based retrieval. First, we revise classical pseudo-relevance feedback (PRF), which refines query embeddings based on top-ranked results. To address its limitations, we propose generative relevance feedback (GRF), which uses synthetic captions for query refinement. Furthermore, we introduce an attentive feedback summarizer (AFS), a custom transformer-based model that integrates multimodal fine-grained features from relevant items. Finally, we simulate explicit feedback using ground-truth captions as an upper-bound baseline. Experiments on Flickr30k and COCO with three VLM backbones show that GRF, AFS, and explicit feedback improve retrieval performance by 3-5% in MRR@5 for smaller VLMs, and 1-3% for larger ones, compared to retrieval with no feedback. Moreover, AFS, similarly to explicit feedback, mitigates query drift and is more robust than GRF in iterative, multi-turn retrieval settings. Our findings demonstrate that relevance feedback can consistently enhance retrieval across VLMs and open up opportunities for interactive and adaptive visual search.
CONSTANT: Towards High-Quality One-Shot Handwriting Generation with Patch Contrastive Enhancement and Style-Aware Quantization
Authors: Anh-Duy Le; Van-Linh Pham; Thanh-Nam Vo; Xuan Toan Mai; Tuan-Anh Tran
Venue: WACV 2026 (main)

Abstract: One-shot styled handwriting image generation, despite achieving impressive results in recent years, remains challenging due to the difficulty in capturing the intricate and diverse characteristics of human handwriting by using solely a single reference image. Existing methods still struggle to generate visually appealing and realistic handwritten images and adapt to complex, unseen writer styles, struggling to isolate invariant style features (e.g., slant, stroke width, curvature) while ignoring irrelevant noise. To tackle this problem, we introduce Patch Contrastive Enhancement and Style-Aware Quantization via Denoising Diffusion (CONSTANT), a novel one-shot handwriting generation via diffusion model. CONSTANT leverages three key innovations: 1) a Style-Aware Quantization (SAQ) module that models style as discrete visual tokens capturing distinct concepts; 2) a contrastive objective to ensure these tokens are well-separated and meaningful in the embedding style space; 3) a latent patch-based contrastive (LLatentPCE) objective help improving quality and local structures by aligning multiscale spatial patches of generated and real features in latent space. Extensive experiments and analysis on benchmark datasets from multiple languages, including English, Chinese, and our proposed ViHTGen dataset for Vietnamese, demonstrate the superiority of adapting to new reference styles and producing highly detailed images of our method over state-of-the-art approaches. Code is available at GitHub https://github.com/duylebkHCM/CONSTANT
QAL : A Loss for Recall-Precision Balance in 3D Reconstruction
Authors: Pranay Meshram; Yash Turkar; Kartikeya Singh; Praveen Raj Masilamani; Charuvahan Adhivarahan; Karthik Dantu
Venue: WACV 2026 (main)

Abstract: Volumetric learning underpins many 3D vision tasks such as completion, reconstruction, and mesh generation, yet training objectives still rely on Chamfer Distance (CD) or Earth Mover's Distance (EMD), which fail to balance recall and precision. We propose Quality-Aware Loss (QAL), a drop-in replacement for CD/EMD that combines a coverage-weighted nearest-neighbor term with an uncovered-ground-truth attraction term, explicitly decoupling recall and precision into tunable components. Across diverse pipelines, QAL achieves consistent coverage gains, improving by an average of +4.3 pts over CD and +2.8 pts over the best alternatives. These improvements reliably recover thin structures and under-represented regions that CD/EMD overlook. Extensive ablations confirm stable performance across hyperparameters and output resolutions, while full retraining on PCN and ShapeNet demonstrates generalization across datasets and backbones. Moreover, QAL-trained completions yield higher grasp scores under GraspNet evaluation, showing that improved coverage translates directly into more reliable robotic manipulation. QAL thus offers a principled, interpretable, and practical objective for robust 3D vision and safety-critical robotics pipelines.
PADM: A Physics-aware Diffusion Model for Attenuation Correction
Authors: Trung Kien Pham; Hoang Minh Vu; Anh Duc Chu; Dac Thai Nguyen; Trung Thanh Nguyen; Thao Nguyen Truong; Mai Hong Son; Thanh Trung Nguyen; Phi Le Nguyen
Venue: WACV 2026 (main)

Abstract: Attenuation artifacts remain a significant challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often compromising diagnostic accuracy and reducing clinical interpretability. While hybrid SPECT/CT systems mitigate these artifacts through CT-derived attenuation maps, their high cost, limited accessibility, and added radiation exposure hinder widespread clinical adoption. In this study, we propose a novel CT-free solution to attenuation correction in cardiac SPECT. Specifically, we introduce Physics-aware Attenuation Correction Diffusion Model (PADM), a diffusion-based generative method that incorporates explicit physics priors via a teacher-student distillation mechanism. This approach enables attenuation artifact correction using only Non-Attenuation-Corrected (NAC) input, while still benefiting from physics-informed supervision during training. To support this work, we also present CardiAC, a comprehensive dataset comprising 424 patient studies with paired NAC and Attenuation-Corrected (AC) reconstructions, alongside high-resolution CT-based attenuation maps. Extensive experiments demonstrate that PADM outperforms state-of-the-art generative models, delivering superior reconstruction fidelity across both quantitative metrics and visual assessment. Both the CardiAC dataset and the PADM codebase are publicly released to advance research in physics-informed generative modeling for cardiac imaging.
FB-4D: Spatial-Temporal Coherent Dynamic 3D Content Generation with Feature Banks
Authors: Jinwei Li; Huan-Ang Gao; Wenyi Li; Haohan Chi; Chenyu Liu; Chenxi Du; Yiqian Liu; Mingju Gao; Guiyu Zhang; Zongzheng Zhang; Li Yi; Yao Yao; Jingwei Zhao; Hongyang Li; Yikai Wang; Hao Zhao
Venue: WACV 2026 (main)

Abstract: With the rapid advancements in diffusion models and 3D generation techniques, dynamic 3D content generation has become a crucial research area. However, achieving high-fidelity 4D (dynamic 3D) generation with strong spatial-temporal consistency remains a challenging task. Inspired by recent findings that pretrained diffusion features capture rich correspondences, we propose FB-4D, a novel 4D generation framework that integrates a Feature Bank mechanism to enhance both spatial and temporal consistency in generated frames. In FB-4D, we store features extracted from previous frames and fuse them into the process of generating subsequent frames, ensuring consistent characteristics across both time and multiple views. To ensure a compact representation, the Feature Bank is updated by a proposed dynamic merging mechanism. Leveraging this Feature Bank, we demonstrate for the first time that generating additional reference sequences through multiple autoregressive iterations can continuously improve generation performance. Experimental results show that FB-4D significantly outperforms existing methods in terms of rendering quality, spatial-temporal consistency, and robustness. It surpasses all multi-view generation tuning-free approaches by a large margin and achieves performance on par with training-based methods. Our code and data will be publicly available to support future research.
MEGA-PCC: A Mamba-based Efficient Approach for Joint Geometry and Attribute Point Cloud Compression
Authors: Kai-Hsiang Hsieh; Monyneath Yim; Wen-Hsiao Peng; Jui-Chiu Chiang
Venue: WACV 2026 (main)

Abstract: Joint compression of point cloud geometry and attributes is essential for efficient 3D data representation. Existing methods often rely on recoloring procedures and manually tuned bitrate allocation between geometry and attribute compressions in inference, which hinder end-to-end optimization and add system complexity. To overcome these limitations, we propose MEGA-PCC, a fully end-to-end, learning-based framework featuring two specialized models for joint compression. The main compression model employs a shared encoder that embeds both geometry and attribute information into a unified latent space, followed by dual decoders that sequentially reconstruct geometry and then attributes. Complementing this, the Mamba-based Entropy Model (MEM) enhances entropy coding by capturing spatial and channel-wise correlations to improve probability estimation. Both models are built on the Mamba architecture to effectively model long-range dependencies and rich contextual features. By eliminating the need for recoloring and heuristic bitrate tuning, MEGA-PCC enables data-driven bitrate allocation during training and simplifies the overall pipeline. Extensive experiments demonstrate that MEGA-PCC achieves superior rate-distortion performance and runtime efficiency compared to both traditional and learning-based baselines, offering a powerful solution for AI-driven point cloud compression.
CalibBEV: LiDAR-Camera Calibration via BEV Alignment
Authors: Filippo D'Addeo; Lorenzo Cipelli; Adriano Cardace; Emanuele Ghelfi; Andrea Zinelli; Massimo Bertozzi
Venue: WACV 2026 (main)

Abstract: We present CalibBEV, a novel Bird's Eye View (BEV) alignment approach for LiDAR-camera calibration. Our method unifies LiDAR and camera data into a shared 3D spatial representation, enabling accurate and robust cross-modal calibration. CalibBEV extracts sensor-wise BEV features from each modality using domain-specific architectures and estimates the calibration matrix through a two-step alignment process. First, we perform an implicit alignment by regressing a coarse calibration matrix directly from the BEV features. To ease this alignment, we enforce semantic consistency between BEV representations across modalities using a contrastive loss inspired by CLIP, guiding both networks toward a unified feature space. In the second step, we leverage our BEV formulation to explicitly align the features of one modality with the other, refining the initial coarse estimate into a final, more accurate calibration matrix. CalibBEV significantly outperforms prior point-to-pixel matching methods, achieving state-of-the-art calibration accuracy. On the KITTI and nuScenes benchmarks, our method reduces the Relative Rotation Error (RRE) by 51% and 68%, and the Relative Translation Error (RTE) by 80% and 91%, respectively, compared to previous methods.
Interaction-via-Actions: Cattle Interaction Detection with Joint Learning of Action-Interaction Latent Space
Authors: Ren Nakagawa; Yang Yang; Risa Shinoda; Hiroaki Santo; Kenji Oyama; Fumio Okura; Takenao Ohkawa
Venue: WACV 2026 (main)

Abstract: This paper introduces a method and application for automatic detection of behavioral interaction between grazing cattle from a single image, which is essential for smart livestock management in the cattle industry, such as for detecting estrus. Although the interaction detection for humans has been actively studied, a non-trivial challenge lies in cattle interaction detection, i.e., the lack of a comprehensive behavioral dataset including interaction, since the interactions of grazing cattle are rare events. We, therefore, propose CattleAct, a data-efficient method for interaction detection by decomposing interactions into the combinations of actions by individual cattle. Specifically, we first learn an action latent space from a large-scale cattle action dataset, then embed rare interactions via the fine-tuning of the pre-trained latent space using contrastive learning, constructing a unified latent space of action and interactions. On top of the proposed method, we develop a practical working system integrating video and GPS inputs. Experiments in a commercial-scale pasture show the accurate interaction detection by our method compared to the baselines.
RobustGait: Robustness Analysis for Appearance Based Gait Recognition
Authors: Reeshoon Sayera; Akash Kumar; Sirshapan Mitra; Prudvi Kamtam; Yogesh S Rawat
Venue: WACV 2026 (main)

Abstract: Appearance-based gait recognition has achieved strong performance on controlled datasets, yet systematic evaluation of its robustness to real-world corruptions and silhouette variability remains lacking. We present RobustGait, a framework for fine-grained robustness evaluation of appearance-based gait recognition systems. RobustGait evaluation spans four dimensions: the type of perturbation (digital, environmental, temporal, occlusion), the silhouette extraction method (segmentation and parsing networks), the architectural capacities of gait recognition models, and various deployment scenarios. The benchmark introduces 15 corruption types at 5 severity levels across CASIA-B, CCPG, and SUSTech1K, with in-the-wild validation on MEVID, and evaluates six state-of-the-art gait systems. We came across several exciting insights. First, applying noise at the RGB level better reflects real-world degradation, and reveal how distortions propagate through silhouette extraction to the downstream gait recognition systems. Second, gait accuracy is highly sensitive to silhouette extractor biases, revealing an overlooked source of benchmark bias. Third, robustness is dependent on both the type of perturbation and the architectural design. Finally, we explore robustness-enhancing strategies, showing that noise-aware training and knowledge distillation improve performance and move toward deployment-ready systems.
DualRes: Production-ready Dynamic Object Detection
Authors: Jibril El Hassani; Thomas Verelst
Venue: WACV 2026 (main)

Abstract: Dynamic Neural Networks (DNNs) have emerged as a promising solution to improve the computational efficiency of deep neural networks by adaptively adjusting inference complexity based on input characteristics. Despite their advantages, the deployment of dynamic networks in real-world applications remains challenging because most methods are hard to adapt for practical use cases such as object detection, in combination with the lacking support of inference infrastructure.In this work, we present a dynamic neural network architecture specifically designed for object detection. Using our method, we build a variety of Pareto-optimal models for object detection on COCO for models in the 7-10 GFLOPs range.Additionally, to measure the routing efficacy, we introduce an evaluation metric that facilitates standardized benchmarking across different dynamic network approaches. Finally, we introduce an evaluation of a deployment pipeline utilizing the ONNX format, thus building a DNN that shows speedup in a realistic deployment scenario. Experimental results demonstrate the performance and practical viability of our approach for efficient object detection in resource-constrained scenarios.
DMS2F-HAD: A Dual-branch Mamba-based Spatial-Spectral Fusion Network for Hyperspectral Anomaly Detection
Authors: Aayushma Pant; Lakpa Tamang; Tsz-Kwan Lee; Sunil Aryal
Venue: WACV 2026 (main)

Abstract: Hyperspectral anomaly detection (HAD) aims to identify rare and irregular targets in high-dimensional hyperspectral images (HSIs), which are often noisy and unlabeled data. Existing deep learning methods either fail to capture long-range spectral dependencies (e.g., convolutional neural networks) or suffer from high computational cost (e.g., Transformers). To address these challenges, we propose DMS2F-HAD, a novel dual-branch Mamba-based model. Our architecture utilizes Mamba's linear-time modeling to efficiently learn distinct spatial and spectral features in specialized branches, which are then integrated by dynamic gated fusion mechanism to enhance anomaly localization. Across fourteen benchmark HSI datasets, our proposed DMS2F-HAD not only achieves a state-of-the-art average AUC of 98.78% but also demonstrates superior efficiency with an inference speed 4.6x faster than comparable deep learning methods. The results highlight DMS2F-HAD's strong generalization and scalability, positioning it as a strong candidate for practical HAD applications. The source code and models are available at https://github.com/Ayushma00/DMS2F-HAD.
FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation
Authors: M. Yashwanth; Sampath Koti; Arunabh Singh; Shyam Marjit; Anirban Chakraborty
Venue: WACV 2026 (main)

Abstract: We address the Federated source-Free Domain Adaptation (FFreeDA) problem, with clients holding unlabelled data with significant inter-client domain gaps. The FFreeDA setup constrains the FL frameworks to employ only a pre-trained server model as the setup restricts access to the source dataset during the training rounds. Often, this source domain dataset has a distinct distribution to the clients' domains. To address the challenges posed by the FFreeDA setup, adaptation of the Source-Free Domain Adaptation (SFDA) methods to FL struggles with client-drift in real-world scenarios due to extreme data heterogeneity caused by the aforementioned domain gaps, resulting in unreliable pseudo-labels. In this paper, we introduce FedSCAl, an FL framework leveraging our proposed Server-Client Alignment (SCAl) mechanism to regularize client updates by aligning the clients' and server model's predictions. We observe an improvement in the clients' pseudo-labeling accuracy post alignment, as the SCAl mechanism helps to mitigate the client-drift. Further, we present extensive experiments on benchmark vision datasets showcasing how FedSCAl consistently outperforms state-of-the-art FL methods in the FFreeDA setup for classification tasks.
Meta-YOLO: Metadata-Guided Real-Time Object Detector in Aerial Imagery
Authors: Deukryeol Yoon; Seonghak Kim; Young Hwa Sung; Jinho Jung
Venue: WACV 2026 (main)

Abstract: Aerial object detection is constrained by tiny targets, large scale variation, and strict real-time limits. It supports traffic monitoring, disaster response, and infrastructure inspection. Yet current detectors often ignore available platform metadata and process frames in isolation. This omission prevents receptive fields from adapting to scale variation and reduces accuracy. We propose Meta-YOLO to exploit platform metadata for scale-aware aerial object detection in real time. Meta-YOLO injects normalized telemetry into spatial sampling to guide feature extraction. It modulates deformable convolution offsets using a spatial metadata map aligned with the image. This links visual features with platform state and enables receptive fields to adapt to object scale. Built on YOLOX, Meta-YOLO adds two modules: feature modulation and offset correction. Evaluated on 327K aerial frames with metadata, Meta-YOLO achieves up to +8.7 AP gains over YOLOX in lightweight regimes and consistently outperforms other recent detectors. It preserves real-time throughput with negligible overhead and improves accuracy without extra visual processing.
SAIL: Self-supervised Learning of Lighting-Invariant Representations from Real Images with Latent Diffusion
Authors: Hala Djeghim; Céline Loscos; Désiré Sidibé
Venue: WACV 2026 (main)

Abstract: Intrinsic image decomposition aims at separating an image into its underlying albedo and shading components, isolating the base color from lighting effects to enable downstream applications such as virtual relighting and scene editing.Despite the rise and success of learning-based approaches, intrinsic image decomposition from real-world images remains a significantly challenging task due to the scarcity of labeled ground-truth data.Most existing solutions rely on synthetic data as supervised setups, limiting their ability to generalize to real-world scenes. Self-supervised methods, on the other hand, often produce albedo-like maps that contain reflections and lack consistency under different lighting conditions.To address this, we propose SAIL, an approach designed to estimate illumination-invariant representations from single-view real-world images to specifically target plausible relighting. We repurpose the prior knowledge of a latent diffusion model for unconditioned scene relighting as a surrogate objective for learning light-invariant estimates. To achieve this, we introduce a novel intrinsic image decomposition fully formulated in the latent space.To guide the training of our latent diffusion model, we introduce regularization terms that constrain both the lighting-dependent and -independent components of our latent image decomposition.Through our experiments, we demonstrate that SAIL produces stable albedo-like representations under varying lighting conditions and generalizes to multiple scenes, using only unlabeled multi-illumination data available online.
Zero-Shot Video Deraining with Video Diffusion Models
Authors: Tuomas Varanka; Juan Luis Gonzalez; Hyeongwoo Kim; Pablo Garrido; Xu Yao
Venue: WACV 2026 (main)

Abstract: Existing video deraining methods are often trained on paired datasets, either synthetic, which limits their ability to generalize to real-world rain, or captured by static cameras, which restricts their effectiveness in dynamic scenes with background and camera motion. Furthermore, recent works in fine-tuning diffusion models have shown promising results, but the fine-tuning tends to weaken the generative prior, limiting generalization to unseen cases. In this paper, we introduce the first zero-shot video deraining method for complex dynamic scenes that does not require synthetic data nor model fine-tuning, by leveraging a pretrained text-to-video diffusion model that demonstrates strong generalization capabilities. By inverting an input video into the latent space of diffusion models, its reconstruction process can be intervened and pushed away from the model's concept of rain using negative prompting. At the core of our approach is an attention switching mechanism that we found is crucial for maintaining dynamic backgrounds as well as structural consistency between the input and the derained video, mitigating artifacts introduced by naive negative prompting. Our approach is validated through extensive experiments on real-world rain datasets, demonstrating substantial improvements over prior methods and showcasing robust generalization without the need for supervised training.