GenAI4ELab/papercli-papers huggingface dataset:
Face Anonymization Made Simple
Authors: Han-Wei Kung; Tuomas Varanka; Sanjay Saha; Terence Sim; Nicu Sebe
Venue: WACV 2025 (main)

Abstract: Current face anonymization techniques often depend on identity loss calculated by face recognition models which can be inaccurate and unreliable. Additionally many methods require supplementary data such as facial landmarks and masks to guide the synthesis process. In contrast our approach uses diffusion models with only a reconstruction loss eliminating the need for facial landmarks or masks while still producing images with intricate fine-grained details. We validated our results on two public benchmarks through both quantitative and qualitative evaluations. Our model achieves state-of-the-art performance in three key areas: identity anonymization facial attribute preservation and image quality. Beyond its primary function of anonymization our model can also perform face swapping tasks by incorporating an additional facial image as input demonstrating its versatility and potential for diverse applications. Our code and models are available at https://github.com/hanweikung/face_anon_simple.
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation
Authors: Michele De Vita; Vasileios Belagiannis
Venue: WACV 2025 (main)

Abstract: Despite the remarkable progress in generative modelling current diffusion models lack a quantitative approach to assess image quality. To address this limitation we propose to estimate the pixel-wise aleatoric uncertainty during the sampling phase of diffusion models and utilise the uncertainty to improve the sample generation quality. The uncertainty is computed as the variance of the denoising scores with a perturbation scheme that is specifically designed for diffusion models. We then show that the aleatoric uncertainty estimates are related to the second-order derivative of the diffusion noise distribution. We evaluate our uncertainty estimation algorithm and the uncertainty-guided sampling on the ImageNet and CIFAR- 10 datasets. In our comparisons with the related work we demonstrate promising results in filtering out low quality samples. Furthermore we show that our guided approach leads to better sample generation in terms of FID scores
SIGNN - Star Identification using Graph Neural Networks
Authors: Floyd Hepburn-Dickins; Mark W. Jones; Mike Edwards; Jay Paul Morgan; Steve Bell
Venue: WACV 2025 (main)

Abstract: As a solution for the lost-in-space star identification problem we present Star Identification using Graph Neural Network (SIGNN) a novel approach using Graph Attention Networks. By representing the celestial sphere as a graph data structure created from the ESA's Hipparcos catalogue we are able to accurately capture the rich information and relationships within local star fields. Graph learning techniques allow our model to aggregate information and learn the relative importance of the nodes and structure within each stars local neighbourhood to it's identification. This approach combined with our parametric data-generation and noise simulation allows us to train a highly robust model capable of accurate star identification even under intensive noise outperforming existing methods. Code and generation techniques will be available on https://github.com/FloydHepburn/SIGNN.
Controlling Human Shape and Pose in Text-to-Image Diffusion Models via Domain Adaptation
Authors: Benito Buchheim; Max Reimann; Jürgen Döllner
Venue: WACV 2025 (main)

Abstract: We present a methodology for conditional control of human shape and pose in pretrained text-to-image diffusion models using a 3D human parametric model (SMPL). Fine-tuning these diffusion models to adhere to new conditions requires large datasets and high-quality annotations which can be more cost-effectively acquired through synthetic data generation rather than real-world data. However the domain gap and low scene diversity of synthetic data can compromise the pretrained model's visual fidelity. We propose a domain-adaptation technique that maintains image quality by isolating synthetically trained conditional information in the classifier-free guidance vector and composing it with another control network to adapt the generated images to the input domain. To achieve SMPL control we fine-tune a ControlNet-based architecture on the synthetic SURREAL dataset of rendered humans and apply our domain adaptation at generation time. Experiments demonstrate that our model achieves greater shape and pose diversity than the 2d pose-based ControlNet while maintaining the visual fidelity and improving stability proving its usefulness for downstream tasks such as human animation. Our code is available at https://ivpg.github.io/humanLDM.
Zero-Shot Class Unlearning in CLIP with Synthetic Samples
Authors: Alexey Kravets; Vinay Namboodiri
Venue: WACV 2025 (main)

Abstract: Machine unlearning is a crucial area of research. It is driven by the need to remove sensitive information from models to safeguard individuals' right to be forgotten under rigorous regulations such as GDPR. In this work we focus on unlearning within CLIP a dual vision-language encoder model trained on a massive dataset of image-text pairs using contrastive loss. To achieve forgetting we expand the application of Lipschitz regularization to the multimodal context of CLIP. Specifically we smooth both visual and textual embeddings associated with the class intended to be forgotten relative to the perturbation introduced to the samples from that class. Additionally importantly we remove the necessity for real forgetting data by generating synthetic samples via gradient ascent maximizing the target class. Our forgetting procedure is iterative where we track accuracy on a synthetic forget set and stop when accuracy falls below a chosen threshold. We employ a selective layers update strategy based on their average absolute gradient value to mitigate over-forgetting. We validate our approach on several standard datasets and provide thorough ablation analysis and comparisons with previous work.
XPose: Towards Extreme Low Light Hand Pose Estimation
Authors: Green Rosh; Meghana Shankar; Prateek Kukreja; Anmol Namdev; Pawan Prasad B H
Venue: WACV 2025 (main)

Abstract: Recent advances in deep learning have enabled considerable strides in hand pose estimation in well-lit conditions. However to the best of our knowledge there is no existing method for hand pose estimation from RGB images captured in low light conditions. This task is highly challenging due to the overwhelming amount of noise which plague image capture in low-light conditions (<1 lux). In this paper we propose XPose the first method for extreme low light hand pose estimation from RGB images. We also introduce the first dataset for low light hand pose estimation consisting of  120k images along with accurate hand pose labels. Our dataset consists of images captured in low light and well-lit conditions from multiple viewpoints. We propose an innovative deep learning based methodology for monocular low-light hand pose estimation using guidance from well-lit and multi-view images available in our dataset during training time. We show that our method using the proposed LLPose dataset significantly outperforms existing methods for hand pose estimation both qualitatively and quantitatively in low light conditions.
HDPNet: Hourglass Vision Transformer with Dual-Path Feature Pyramid for Camouflaged Object Detection
Authors: Jinpeng He; Biyuan Liu; Huaixin Chen
Venue: WACV 2025 (main)

Abstract: Existing camouflaged object detection methods often struggle with detecting small objects and fine object boundaries. To alleviate these issues we propose a novel hourglass vision Transformer with Dual-path Feature Pyramid (HDPNet). Specifically we construct an hourglass Transformer encoder that effectively captures the global semantic cues while extracting detailed feature maps at various scales preserving the spatial details and fine-grained boundaries of the camouflaged object. To ensure the preservation of essential cues of hourglass features we introduce a dual-path feature pyramid decoder (DPFD). This decoder performs coarse-to-fine feature fusion laterally mitigating the dilution of essential feature cues caused by the semantic gaps. In addition to further facilitate the local feature modeling in the encoder to mine the correlation between local features and global semantic cues from the camouflaged region we design a feature interaction enhancement module (FIEM). This module adopts a symmetric structure enables detailed appearance features and global semantic features to complement each other enhancing the model's ability to capture a wide range of fine-grained details. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms 25 existing methods across three challenging COD benchmark datasets particularly excelling in the detection of small objects and fine boundaries. The code is available at https://github.com/LittleGrey-hjp/HDPNet.
SEM-Net: Efficient Pixel Modelling for Image Inpainting with Spatially Enhanced SSM
Authors: Shuang Chen; Haozheng Zhang; Amir Atapour-Abarghouei; Hubert P. H. Shum
Venue: WACV 2025 (main)

Abstract: Image inpainting aims to repair a partially damaged image based on the information from known regions of the images. Achieving semantically plausible inpainting results is particularly challenging because it requires the reconstructed regions to exhibit similar patterns to the semantically consistent regions. This requires a model with a strong capacity to capture long-range dependencies. Existing models struggle in this regard due to the slow growth of receptive field for Convolutional Neural Networks (CNNs) based methods and patch-level interactions in Transformer-based methods which are ineffective for capturing long-range dependencies. Motivated by this we propose SEM-Net a novel visual State Space model (SSM) vision network modelling corrupted images at the pixel level while capturing long-range dependencies (LRDs) in state space achieving a linear computational complexity. To address the inherent lack of spatial awareness in SSM we introduce the Snake Mamba Block (SMB) and Spatially-Enhanced Feedforward Network. These innovations enable SEM-Net to outperform state-of-the-art inpainting methods on two distinct datasets showing significant improvements in capturing LRDs and enhancement in spatial consistency. Additionally SEM-Net achieves state-of-the-art performance on motion deblurring demonstrating its generalizability. Our source code is available:https://github.com/ChrisChen1023/SEM-Net.
LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
Authors: Marco P. E. Apolinario; Arani Roy; Kaushik Roy
Venue: WACV 2025 (main)

Abstract: Training deep neural networks (DNNs) using traditional backpropagation (BP) presents challenges in terms of computational complexity and energy consumption particularly for on-device learning where computational resources are limited. Various alternatives to BP including random feedback alignment forward-forward and local classifiers have been explored to address these challenges. These methods have their advantages but they can encounter difficulties when dealing with intricate visual tasks or demand considerable computational resources. In this paper we propose a novel Local Learning rule inspired by neural activity Synchronization phenomena (LLS) observed in the brain. LLS utilizes fixed periodic basis vectors to synchronize neuron activity within each layer enabling efficient training without the need for additional trainable parameters. We demonstrate the effectiveness of LLS and its variations LLS-M and LLS-MxM on multiple image classification datasets achieving accuracy comparable to BP with reduced computational complexity and minimal additional parameters. Specifically LLS achieves comparable performance with up to 300x fewer multiply-accumulate (MAC) operations and half the memory requirements of BP. Furthermore the performance of LLS on the Visual Wake Word (VWW) dataset highlights its suitability for on-device learning tasks making it a promising candidate for edge hardware implementations.
A Multi-Task Supervised Compression Model for Split Computing
Authors: Yoshitomo Matsubara; Matteo Mendula; Marco Levorato
Venue: WACV 2025 (main)

Abstract: Split computing ( split learning) is a promising approach to deep learning models for resource-constrained edge computing systems where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through channels with limited communication capacity. State-of-the-art work on split computing presents methods for single tasks such as image classification object detection or semantic segmentation. The application of existing methods to multi-task problems degrades model accuracy and/or significantly increase runtime latency. In this study we propose Ladon the first multi-task-head supervised compression model for multi-task split computing. Experimental results show that the multi-task supervised compression model either outperformed or rivaled strong lightweight baseline models in terms of predictive performance for ILSVRC 2012 COCO 2017 and PASCAL VOC 2012 datasets while learning compressed representations at its early layers. Furthermore our models reduced end-to-end latency (by up to 95.4%) and energy consumption of mobile devices (by up to 88.2%) in multi-task split computing scenarios.
Advancing Chart Question Answering with Robust Chart Component Recognition
Authors: Hanwen Zheng; Sijia Wang; Chris Thomas; Lifu Huang
Venue: WACV 2025 (main)

Abstract: Chart comprehension presents significant challenges for machine learning models due to the diverse and intricate shapes of charts. Existing multimodal methods often overlook these visual features or fail to integrate them effectively for Chart Question Answering. To address this we introduce ChartFormer a unified framework that enhances chart component recognition by accurately identifying and classifying components such as bars lines pies titles legends and axes. Additionally we propose a novel Question-guided Deformable Co-Attention (QDCAt) mechanism which fuses chart features encoded by ChartFormer with the given question leveraging the question's guidance to ground the correct answer. Extensive experiments demonstrate a 3.2% improvement in mAP over the baselines for chart component recognition. For ChartQA and OpenCQA tasks our approach achieves improvements of 15.4% in accuracy and 0.8 in BLEU score respectively underscoring the robustness of our solution for detailed visual data interpretation across various applications. The source code and dataset are publicly available at https://github.com/VT-NLP/chartQA.
Towards Accurate Unified Anomaly Segmentation
Authors: Wenxin Ma; Qingsong Yao; Xiang Zhang; Zhelong Huang; Zihang Jiang; S.Kevin Zhou
Venue: WACV 2025 (main)

Abstract: Unsupervised anomaly detection (UAD) from images strives to model normal data distributions creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme challenges persist in accurately segmenting anomalies for further monitoring. Moreover this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets respectively surpassing previous methods significantly. The codes are shared at https://github.com/Mwxinnn/UniAS.
TRUST: Time-Domain Residual Unsupervised Stability Technique for Improved Heart Rate Estimation
Authors: Shahzad Ahmad; Sania Bano; Sukalpa Chanda; Santosh Kumar Vipparthi; Subrahmanyam Murala
Venue: WACV 2025 (main)

Abstract: Camera-based estimation of vital signs is a promising method for non-contact health monitoring which analyzes minute changes in video data. However the creation of accurate models for this task is challenging due to the scarcity of datasets that possess synchronized vital sign recordings. Our research enhances an existing non-contrastive unsupervised learning technique for extracting rPPG signals which does not necessitate ground-truth signals during the training process. We have incorporated new time-domain loss functions and added a feature stabilization block to improve the model's stability and accuracy in detecting low-level features. Additionally we have devised a metric to evaluate the feature instability in the model's final layer. Our experiments on four public datasets demonstrate that our method surpasses the performance of current state-of-the-art methods. These advancements make our approach a significant breakthrough in the development of scalable deep-learning models for camera-based heart-rate estimation.
Attention-Guided Masked Autoencoders for Learning Image Representations
Authors: Leon Sick; Dominik Engel; Pedro Hermosilla; Timo Ropinski
Venue: WACV 2025 (main)

Abstract: Masked autoencoders (MAEs) have established themselves as a powerful pre-training method for computer vision tasks. While vanilla MAEs put equal emphasis on reconstructing the individual parts of the image we propose to inform the reconstruction process through an attention-guided loss function. By leveraging advances in unsupervised object discovery we obtain an attention map of the scene which we employ in the loss function to put increased emphasis on reconstructing relevant objects. Thus we incentivize the model to learn improved representations of the scene for a variety of tasks. Our evaluations show that our pre-trained models produce off-the-shelf representations more effective than the vanilla MAE for such tasks demonstrated by improved linear probing and k-NN classification results on several benchmarks while at the same time making ViTs more robust against varying backgrounds and changes in texture.
Enhancing Vision-Language Few-Shot Adaptation with Negative Learning
Authors: Ce Zhang; Simon Stepputtis; Katia Sycara; Yaqi Xie
Venue: WACV 2025 (main)

Abstract: Large-scale pre-trained Vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability allowing them to adapt to downstream tasks in a data-efficient manner. However when only a few labeled samples are available adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work we propose a Simple yet effective Negative Learning approach SimNL to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of representative positive features defining "what is a  CLASS " SimNL discovers a complementary set of negative features that define "what is not a  CLASS " providing additional insights that supplement the positive features to enhance task-specific recognition capability. Further we identify that current adaptation approaches are particularly vulnerable to potential noise in the few-shot sample set. To mitigate this issue we introduce a plug-and-play few-shot instance reweighting technique to suppress noisy outliers and amplify clean samples for more stable adaptation. Our extensive experimental results across 15 datasets validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks while achieving competitive computational efficiency. Code is available at https://github.com/zhangce01/SimNL.
MVAD: A Multiple Visual Artifact Detector for Video Streaming
Authors: Chen Feng; Duolikun Danier; Fan Zhang; Alex Mackin; Andrew Collins; David Bull
Venue: WACV 2025 (main)

Abstract: Visual artifacts are often introduced into streamed video content due to prevailing conditions during content production and delivery. Since these can degrade the quality of the user's experience it is important to automatically and accurately detect them in order to enable effective quality measurement and enhancement. Existing detection methods often focus on a single type of artifact and/or determine the presence of an artifact through thresholding objective quality indices. Such approaches have been reported to offer inconsistent prediction performance and are also impractical for real-world applications where multiple artifacts co-exist and interact. In this paper we propose a Multiple Visual Artifact Detector MVAD for video streaming which for the first time is able to detect multiple artifacts using a single framework that is not reliant on video quality assessment models. Our approach employs a new Artifact-aware Dynamic Feature Extractor (ADFE) to obtain artifact-relevant spatial features within each frame for multiple artifact types. The extracted features are further processed by a Recurrent Memory Vision Transformer (RMViT) module which captures both short-term and long-term temporal information within the input video. The proposed network architecture is optimized in an end-to-end manner based on a new large and diverse training database that is generated by simulating the video streaming pipeline and based on Adversarial Data Augmentation. This model has been evaluated on two video artifact databases Maxwell and BVI-Artifact and achieves consistent and improved prediction results for ten target visual artifacts when compared to seven existing single and multiple artifact detectors. The source code and training database will be available at https://chenfeng-bristol.github.io/MVAD/.
Physiology-Aware PolySnake for Coronary Vessel Segmentation
Authors: Yizhe Ruan; Lin Gu; Yusuke Kurose; Junichi Iho; Youji Tokunaga; Makoto Horie; Yusaku Hayashi; Keisuke Nishizawa; Yasushi Koyama; Tatsuya Harada
Venue: WACV 2025 (main)

Abstract: Coronary artery disease (CAD) is a significant health risk that requires early detection for effective treatment. While recent advances in deep learning have shown promise in automating CAD detection from coronary computed tomography angiography (CCTA) images the accurate segmentation of coronary vessels remains a challenge particularly due to the imbalanced presence of plaque in unhealthy vessels. This paper introduces a physiology-aware approach to coronary vessel segmentation that addresses these challenges. Our proposed pipeline consists of three main components. First a hybrid UNeXt architecture is designed to segment artery boundaries and predict initial boundary contours by leveraging 3D spatial relations among adjacent slices. Second we introduce multi-class circular convolution for iterative contour deformation which generates well-connected contour pairs of the artery wall's inner and outer boundaries through iterative refinement. Finally we propose a focal smooth L1 loss function to handle the implicit class imbalance caused by plaque in unhealthy vessels and to enhance the robustness of the physiology-aware polysnake network by explicitly limiting the accuracy of initial contours. Extensive evaluations demonstrate that our methods significantly improve model performance achieving state-of-the-art results in coronary vessel segmentation.