GenAI4ELab/papercli-papers huggingface dataset:
DMRN: A Dynamical Multi-Order Response Network for the Robust Lung Airway Segmentation
Authors: Sheng Zhang; Jinge Wu; Junzhi Ning; Guang Yang
Venue: WACV 2025 (main)

Abstract: Automated airway segmentation in CT images is crucial for lung diseases' diagnosis. However manual annotation scarcity hinders supervised learning efficacy while unlimited intensities and sample imbalance lead to discontinuity and false-negative issues. To address these challenges we propose a novel airway segmentation model named Dynamical Multi-order Response Network (DMRN) integrating the unsupervised and supervised learning in parallel to alleviate the label scarcity of airway. In the unsupervised branch (1) we propose several novel strategies of Dynamic Mask-Ratio (DMR) to enable the model to perceive context information of varying sizes mimicking the laws of human learning vividly; (2) we present a novel target of Multi-Order Normalized Responses (MONR) exploiting the distinct order exponential operation of raw images and oriented gradients to enhance the textural representations of bronchioles. For the supervised branch we directly predict the final full segmentation map by the large-ratio cube-masked input instead of full input. Ultimately we verify the method performance and robustness by training on normal lung disease datasets while testing on lung cancer COVID-19 and Lung fibrosis datasets. All experimental results have proved that our method exceeds state-of-the-art methods significantly. Code will be released in the future.
Self-Supervised Learning with Spectral Low-Rank Prior for Hyperspectral Image Reconstruction
Authors: Zijun He; Lishun Wang; Ziyi Meng; Xin Yuan
Venue: WACV 2025 (main)

Abstract: Hyperspectral image (HSI) reconstruction from coded measurement is significant for acquiring images with higher spectral resolution than traditional RGB images. Current advanced neural networks have already shown impressive performance in some datasets like CAVE and KAIST. However these networks rely on a large amount of simulated ground truth measurement pairs. Unfortunately in some scenarios it is hard to obtain a sufficient high-quality HSI training set resulting in low generalization ability. Although iterative algorithms show good generalization ability they are limited by slow speed and low reconstruction quality. To address this challenge in this paper we propose a self-supervised learning framework which can train and fine-tune networks using measurements without ground truth. Besides we propose the spectral low-rank loss function that enables networks to learn the signal model of HSI. Finally we train and fine-tune a representative deep unfolding network GAP-net using our proposed framework. Extensive simulation and real data results show that the proposed self-supervised framework is capable of achieving results competitive with those of supervised networks. Code is available at https://github.com/zjhe02/CASSI-SSL.
Improving Deep Detector Robustness via Detection-Related Discriminant Maximization and Reorganization
Authors: Jung Im Choi; Qizhen Lan; Qing Tian
Venue: WACV 2025 (main)

Abstract: Deep visual detectors are known to be vulnerable to adversarial attacks raising concerns about their real-world applications (e.g. self-driving perception). We argue that this vulnerability arises from the spurious dependency of final detections on irrelevant/loophole latent dimensions. The greater the number of such dimensions the higher the likelihood of the detector being compromised by adversarial attacks making it more susceptible to input perturbations. To enhance detection robustness we propose Detection-related Discriminant Maximization and Reorganization (DDMR) condensing the detection utility to a compressed number of relevant dimensions while deactivating the influence of irrelevant ones. This approach also alleviates the misalignment issue between the two task domains in visual detection and consequently their gradients. This enables the generation of more potent adversarial attacks and defenses for visual detectors within the adversarial training framework. Extensive experiments conducted with four cutting-edge visual detectors on the KITTI and COCO datasets showcase the efficacy of the proposed approach in improving the adversarial robustness of deep visual detectors against both white-box and black-box attacks. For example on the KITTI dataset our method demonstrates an increase in robustness of up to 12.4% and 28.0% without and with adversarial training respectively.
MAISI: Medical AI for Synthetic Imaging
Authors: Pengfei Guo; Can Zhao; Dong Yang; Ziyue Xu; Vishwesh Nath; Yucheng Tang; Benjamin Simon; Mason Belue; Stephanie Harmon; Baris Turkbey; Daguang Xu
Venue: WACV 2025 (main)

Abstract: Medical imaging analysis faces challenges such as data scarcity high annotation costs and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI) an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet MAISI can process organ segmentation including 127 anatomical structures as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.
Attribute Diffusion: Diffusion Driven Diverse Attribute Editing
Authors: Rishubh Parihar; Prasanna Balaji; Raghav Magazine; Sarthak Vora; Varun Jampani; Venkatesh Babu Radhakrishnan
Venue: WACV 2025 (main)

Abstract: Image attribute editing is a widely researched area fueled by the recent advancements in deep generative models. Existing methods treat semantic attributes as binary and do not allow the user to generate multiple variations of the attribute edits. This limits the applications of editing methods in the real world e.g. exploring multiple eyeglass variations on an e-commerce platform. In this work we present a technique to generate a collection of diverse attribute edits and a principled way to explore them. Generation and controlled exploration of attribute variations is challenging as it requires fine control over the attribute styles while preserving other attributes and the identity of the subject. Capitalizing on the attribute disentanglement property of the latent spaces of pretrained GANs we represent the attribute edits in this space. Next we train a diffusion model to model these latent directions of edits. We propose a coarse-to-fine sampling strategy to explore these variations in a controlled manner. Extensive experiments on various datasets establish the effectiveness and generalization of the proposed approach for the generation and controlled exploration of diverse attribute edits. Code is available at - rishubhpar.github.io/attributediffusion
Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach
Authors: Wenjun Huang; Yang Ni; Arghavan Rezvani Dehaghani; SungHeon Evan Jeong; Hanning Chen; Yezi Liu; Fei Wen; Mohsen Imani
Venue: WACV 2025 (main)

Abstract: Human pose estimation (HPE) is crucial for various applications. However deploying HPE algorithms in surveillance contexts raises significant privacy concerns due to the potential leakage of sensitive personal information (SPI) such as facial features and ethnicity. Existing privacy-enhancing methods often compromise either privacy or performance or they require costly additional modalities. We propose a novel privacy-enhancing system that generates privacy-enhanced portraits while maintaining high HPE performance. Our key innovations include the reversible recovery of SPI for authorized personnel and the preservation of contextual information. By jointly optimizing a privacy-enhancing module a privacy recovery module and a pose estimator our system ensures robust privacy protection efficient SPI recovery and high-performance HPE. Experimental results demonstrate the system's robust performance in privacy enhancement SPI recovery and HPE. The code associated with this study can be found at this URL.
DreamBlend: Advancing Personalized Fine-Tuning of Text-to-Image Diffusion Models
Authors: Shwetha Ram; Tal Neiman; Qianli Feng; Andrew M Stuart; Son Tran; Trishul A Chilimbi
Venue: WACV 2025 (main)

Abstract: Given a small number of images of a subject personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subject in novel contexts conditioned on text prompts. In doing so a trade-off is made between prompt fidelity subject fidelity and diversity. As the pre-trained model is fine-tuned earlier checkpoints synthesize images with low subject fidelity but high prompt fidelity and diversity. In contrast later checkpoints generate images with low prompt fidelity and diversity but high subject fidelity. This inherent trade-off limits the prompt fidelity subject fidelity and diversity of generated images. In this work we propose DreamBlend to combine the prompt fidelity from earlier checkpoints and the subject fidelity from later checkpoints during inference. We perform a cross attention guided image synthesis from a later checkpoint guided by an image generated by an earlier checkpoint for the same prompt. This enables generation of images with better subject fidelity prompt fidelity and diversity on challenging prompts outperforming state-of-the-art fine-tuning methods.
MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications
Authors: Gasser Elazab; Torben Gräber; Michael Unterreiner; Olaf Hellwich
Venue: WACV 2025 (main)

Abstract: Self-supervised monocular depth estimation (MDE) has gained popularity for obtaining depth predictions directly from videos. However these methods often produce scale-invariant results unless additional training signals are provided. Addressing this challenge we introduce a novel self-supervised metric-scaled MDE model that requires only monocular video data and the camera's mounting position both of which are readily available in modern vehicles. Our approach leverages planar-parallax geometry to reconstruct scene structure. The full pipeline consists of three main networks a multi-frame network a single-frame network and a pose network. The multi-frame network processes sequential frames to estimate the structure of the static scene using planar-parallax geometry and the camera mounting position. Based on this reconstruction it acts as a teacher distilling knowledge such as scale information masked drivable area metric-scale depth for the static scene and dynamic object mask to the single-frame network. It also aids the pose network in predicting a metric-scaled relative pose between two subsequent images. Our method achieved state-of-the-art results for the driving benchmark KITTI for metric-scaled depth prediction. Notably it is one of the first methods to produce self-supervised metric-scaled depth prediction for the challenging Cityscapes dataset demonstrating its effectiveness and versatility. Project page: https://mono-pp.github.io/
Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities
Authors: Felix Wagner; Wentian Xu; Pramit Saha; Ziyun Liang; Daniel Whitehouse; David Menon; Virginia Newcombe; Natalie Voets; J. Alison Noble; Konstantinos Kamnitsas
Venue: WACV 2025 (main)

Abstract: Segmentation models for brain lesions in MRI are commonly developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities nor can they segment other types of diseases. Moreover this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally the confidentiality of patient data often prevents central data aggregation necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate simple and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients training with random modality drop and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model that achieves very promising results in segmenting all disease types seen during training. Importantly it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate for the first time the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain
A Generic Vehicle-to-Sensor Calibration Framework
Authors: Sumin Hu; Youngmin Yoo; Jeeseong Kim; Changsoo Lim; Doohyun Cho; Bongnam Kang
Venue: WACV 2025 (main)

Abstract: In autonomous driving systems online vehicle-to-sensor (v2s) calibration is a critical component for ensuring safe perception-based control. Since sensor pose may shift during the life-time of a vehicle online calibration is essential to maintain safe driving conditions. To this end this paper introduces Epipoles as a 3D Directional Compass (E3DC) a sensor-agnostic v2s online calibration method. Leveraging the nonholonomic nature of vehicles a hand-eye constraint between the vehicle and the sensor naturally emerges. Consequently we require only the sensor's data to determine the v2s extrinsic rotation. More specifically since we only require sensor odometry estimates to perform v2s calibration E3DC can leverage off-the-shelf odometry estimation pipelines. This offers vast flexibility and wide applicability as the odometry estimation pipeline can be tailored to the specific sensor type and driving environment. We demonstrate that our method is robust and achieves state-of-the-art performance on both the KITTI dataset and a new dataset which will be made publicly available. To the best of our knowledge this is the first v2s calibration dataset for autonomous driving scenarios.
Learning Deep Illumination-Robust Features from Multispectral Filter Array Images
Authors: Anis Amziane
Venue: WACV 2025 (main)

Abstract: Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA) capture multiple spectral bands in a single shot resulting in a raw mosaic image where each pixel holds only one channel value. The fully-defined MS image is estimated from the raw one through demosaicing which inevitably introduces spatio-spectral artifacts. Moreover training on fully-defined MS images can be computationally intensive particularly with deep neural networks (DNNs) and may result in features lacking discrimination power due to suboptimal learning of spatio-spectral interactions. Furthermore outdoor MS image acquisition occurs under varying lighting conditions leading to illumination-dependent features. This paper presents an original approach to learn discriminant and illumination-robust features directly from raw images. It involves: raw spectral constancy to mitigate the impact of illumination MSFA-preserving transformations suited for raw image augmentation to train DNNs on diverse raw textures and raw-mixing to capture discriminant spatio-spectral interactions in raw images. Experiments on MS image classification show that our approach outperforms both handcrafted and recent deep learning-based methods while also requiring significantly less computational effort. The source code is available at https://github.com/AnisAmziane/RawTexture.
Point-GN: A Non-Parametric Network using Gaussian Positional Encoding for Point Cloud Classification
Authors: Marzieh Mohammadi; Amir Salarpour
Venue: WACV 2025 (main)

Abstract: This paper introduces Point-GN a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters Point-GN leverages non-learnable components-specifically Farthest Point Sampling (FPS) k-Nearest Neighbors (k-NN) and Gaussian Positional Encoding (GPE)-to extract both local and global geometric features. This design eliminates the need for additional training while maintaining high performance making Point-GN particularly suited for real-time resource-constrained applications. We evaluate Point-GN on two benchmark datasets ModelNet40 and ScanObjectNN achieving classification accuracies of 85.29% and 85.89% respectively while significantly reducing computational complexity. Point-GN outperforms existing non-parametric methods and matches the performance of fully trained models all with zero learnable parameters. Our results demonstrate that Point-GN is a promising solution for 3D point cloud classification in practical real-time environments.
Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects
Authors: Kyungmin Jo; Jaegul Choo
Venue: WACV 2025 (main)

Abstract: The emergence of diffusion models has enabled the generation of diverse high-quality images solely from text prompting subsequent efforts to enhance the controllability of these models. Despite the improvement in controllability pose control remains limited to specific objects (e.g. humans) or poses (e.g. frontal view) due to the fact that pose is generally controlled via camera parameters (e.g. rotation angle) or keypoints (e.g. eyes nose). Specifically camera parameters-conditional pose control models generate unrealistic images depending on the object owing to the small size of 3D datasets for training. Also keypoint-based approaches encounter challenges in acquiring reliable keypoints for various objects (e.g. church) or poses (e.g. back view). To address these limitations we propose depth-based pose control as depth maps are easily obtainable from a single depth estimation model regardless of objects and poses unlike camera parameters and keypoints. However depth-based pose control confronts issues of shape dependency as depth maps influence not only the pose but also the shape of the generated images. To tackle this issue we propose Skip-and-Play (SnP) designed via analysis of the impact of three components of depth-conditional ControlNet on the pose and the shape of the generated images. To be specific based on the analysis we selectively skip parts of the components to mitigate shape dependency on the depth map while preserving the pose. Through various experiments we demonstrate the superiority of SnP over baselines and showcase the ability of SnP to generate images of diverse objects and poses. Remarkably SnP exhibits the ability to generate images even when the objects in the condition (e.g. a horse) and the prompt (e.g. a hedgehog) differ from each other.
Generating Visual Explanations from Deep Networks using Implicit Neural Representations
Authors: Michal Byra; Henrik Skibbe
Venue: WACV 2025 (main)

Abstract: Explaining deep learning models in a way that humans can easily understand is essential for responsible artificial intelligence applications. Attribution methods constitute an important area of explainable deep learning. The attribution problem involves finding parts of the network's input that are the most responsible for the model's output. In this work we demonstrate that implicit neural representations (INRs) constitute a good framework for generating visual explanations. Firstly we utilize coordinate-based implicit networks to reformulate and extend the extremal perturbations technique and generate attribution masks. Experimental results confirm the usefulness of our method. For instance by proper conditioning of the implicit network we obtain attribution masks that are well-behaved with respect to the imposed area constraints. Secondly we present an iterative INR-based method that can be used to generate multiple non-overlapping attribution masks for the same image. We depict that a deep learning model may associate the image label with both the appearance of the object of interest as well as with areas and textures usually accompanying the object. Our study demonstrates that implicit networks are well-suited for the generation of attribution masks and can provide interesting insights about the performance of deep learning models.
Label Calibration in Source Free Domain Adaptation
Authors: Shivangi Rai; Rini Smita Thakur; Kunal Jangid; Vinod K Kurmi
Venue: WACV 2025 (main)

Abstract: Source-free domain adaptation (SFDA) utilizes a pre-trained source model with unlabeled target data. Self-supervised SFDA techniques generate pseudolabels from the pre-trained source model but these pseudolabels often contain noise due to domain discrepancies between the source and target domains. Traditional self-supervised SFDA techniques rely on deterministic model predictions using the softmax function leading to unreliable pseudolabels. In this work we propose to introduce predictive uncertainty and softmax calibration for pseudolabel refinement using evidential deep learning. The Dirichlet prior is placed over the output of the target network to capture uncertainty using evidence with a single forward pass. Furthermore softmax calibration solves the translation invariance problem to assist in learning with noisy labels. We incorporate a combination of evidential deep learning loss and information maximization loss with calibrated softmax in both prior and non-prior target knowledge SFDA settings. Extensive experimental analysis shows that our method outperforms other state-of-the-art methods on benchmark datasets. The code is available at https://visdomlab.github.io/EKS/.
Debiasify: Self-Distillation for Unsupervised Bias Mitigation
Authors: Nourhan Bayasi; Jamil Fayyad; Ghassan Hamarneh; Rafeef Garbi; Homayoun Najjaran
Venue: WACV 2025 (main)

Abstract: Simplicity bias is a critical challenge in neural networks since it often leads to favoring simpler solutions and learning unintended decision rules captured by spurious correlations causing models to be biased and diminishing their generalizability. While existing solutions rely on human supervision obtaining annotations of the different bias attributes is often impractical. To tackle this we present Debiasify a novel self-distillation approach that works without any prior information about the nature of biases. Our method leverages a new distillation loss to distill knowledge within a network; from a deep layer where complex highly-predictive features reside to a shallow layer where simpler yet attribute-conditioned features are found in an unsupervised manner. In this way Debiasify learns robust debiased representations that generalize well across various biases and datasets enhancing worst-group performance and overall accuracy. Extensive experiments on computer vision and medical imaging benchmarks show the efficacy of our method significantly outperforming the previous unsupervised debiasing methods (e.g. a 10.13% improvement in worst-group accuracy on Wavy Hair classification in CelebA) while achieving comparable or superior performance to supervised methods. Our code is publicly available at the following link:Debiasify.