GenAI4ELab/papercli-papers huggingface dataset:
RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
Authors: Mustafa Munir; Md Mostafijur Rahman; Radu Marculescu
Venue: WACV 2025 (main)

Abstract: Vision transformers (ViTs) have dominated computer vision in recent years. However ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently Vision GNN (ViG) and CNN hybrid models have also been proposed for mobile vision tasks. However all of these methods remain slower compared to pure CNN-based models. In this work we propose Multi-Level Dilated Convolutions to devise a purely CNN-based mobile backbone. Using Multi-Level Dilated Convolutions allows for a larger theoretical receptive field than standard convolutions. Different levels of dilation also allow for interactions between the short-range and long-range features in an image. Experiments show that our proposed model outperforms state-of-the-art (SOTA) mobile CNN ViT ViG and hybrid architectures in terms of accuracy and/or speed on image classification object detection instance segmentation and semantic segmentation. Our fastest model RapidNet-Ti achieves 76.3% top-1 accuracy on ImageNet-1K with 0.9 ms inference latency on an iPhone 13 mini NPU which is faster and more accurate than MobileNetV2x1.4 (74.7% top-1 with 1.0 ms latency). Our work shows that pure CNN architectures can beat SOTA hybrid and ViT models in terms of accuracy and speed when designed properly.
Uncertainty-Based Data-Wise Label Smoothing for Calibrating Multiple Instance Learning in Histopathology Image Classification
Authors: Hyeongmin Park; Sungrae Hong; Chanjae Song; Jongwoo Kim; Mun Yong Yi
Venue: WACV 2025 (main)

Abstract: Deep neural networks (DNNs) have transformed biomedical image analysis particularly in histopathology with Whole Slide Images (WSIs) classification. However training DNNs requires large annotated datasets which is challenging due to the high heterogeneity and high resolution of WSIs. Multiple Instance Learning (MIL) has become a popular method for weakly supervised classification in this context training with only slide-level labels. Despite the advancements ensuring the reliability of model performance is crucial in safety-critical domains including healthcare. Deep learning models in real-world decision-making systems must accurately predict probability estimates to reflect the true likelihood of correctness known as confidence calibration. This study introduces a novel calibration framework UDLS which uses data-wise label smoothing based on predictive uncertainty to improve the calibration of MIL frameworks. This approach involves augmenting WSIs with PatchFeatureDropout computing predictive uncertainty estimates for original data and applying these estimates to each sample for label smoothing during model training. Experimental results on benchmark histopathology datasets show noticeable improvements in both calibration and classification performance highlighting UDLS's potential for enhancing the reliability of predictions from deep learning models in clinical settings.
Active Event Alignment for Monocular Distance Estimation
Authors: Nan Cai; Pia Bideau
Venue: WACV 2025 (main)

Abstract: Event cameras provide a natural and data efficient representation of visual information motivating novel computational strategies towards extracting visual information. Inspired by the biological vision system we propose a behavior driven approach for object-wise distance estimation from event camera data. This behavior-driven method mimics how biological systems like the human eye stabilize their view based on object distance: distant objects require minimal compensatory rotation to stay in focus while nearby objects demand greater adjustments to maintain alignment. This adaptive strategy leverages natural stabilization behaviors to estimate relative distances effectively. Unlike traditional vision algorithms that estimate depth across the entire image our approach targets local depth estimation within a specific region of interest. By aligning events within a small region we estimate the angular velocity required to stabilize the image motion. We demonstrate that under certain assumptions the compensatory rotational flow is inversely proportional to the object's distance. The proposed approach achieves new state-of-the-art accuracy in distance estimation - a performance gain of 16% on EVIMO2. EVIMO2 event sequences comprise complex camera motion and substantial variance in depth of static real world scenes. Code: https://github.com/pbideau/AAEDepth.
ACE: Action Concept Enhancement of Video-Language Models in Procedural Videos
Authors: Reza Ghoddoosian; Nakul Agarwal; Isht Dwivedi; Behzad Dariush
Venue: WACV 2025 (main)

Abstract: Vision-language models (VLMs) are capable of recognizing unseen actions. However existing VLMs lack intrinsic understanding of procedural action concepts. Hence they overfit to fixed labels and are not invariant to unseen action synonyms. To address this we propose a simple fine-tuning technique Action Concept Enhancement (ACE) to improve the robustness and concept understanding of VLMs in procedural action classification. ACE continually incorporates augmented action synonyms and negatives in an auxiliary classification loss by stochastically replacing fixed labels during training. This creates new combinations of action labels over the course of fine-tuning and prevents overfitting to fixed action representations. We show the enhanced concept understanding of our VLM by visualizing the alignment of encoded embeddings of unseen action synonyms in the embedding space. Our experiments on the ATA IKEA and GTEA datasets demonstrate the efficacy of ACE in domains of cooking and assembly leading to significant improvements in zero-shot action classification while maintaining competitive performance on seen actions.
Psych-Occlusion: using Visual Psychophysics for Aerial Detection of Occluded Persons during Search and Rescue
Authors: Arturo Miguel Russell Bernal; Jane Cleland-Huang; Walter Scheirer
Venue: WACV 2025 (main)

Abstract: The success of Emergency Response (ER) scenarios such as search and rescue is often dependent upon the prompt location of a lost or injured person. With the increasing use of small Unmanned Aerial Systems (sUAS) as "eyes in the sky" during ER scenarios efficient detection of persons from aerial views plays a crucial role in achieving a successful mission outcome. Fatigue of human operators during prolonged ER missions coupled with limited human resources highlights the need for sUAS equipped with Computer Vision (CV) capabilities to aid in finding the person from aerial views. However the performance of CV models onboard sUAS substantially degrades under real-life rigorous conditions of a typical ER scenario where person search is hampered by occlusion and low target resolution. To address these challenges we extracted images from the NOMAD dataset and performed a crowdsource experiment to collect behavioural measurements when humans were asked to "find the person in the picture". We exemplify the use of our behavioral dataset Psych-ER by using its human accuracy data to adapt the loss function of a detection model. We tested our loss adaptation on a RetinaNet model evaluated on NOMAD against increasing distance and occlusion with our psychophysical loss adaptation showing improvements over the baseline at higher distances across different levels of occlusion without degrading performance at closer distances. To the best of our knowledge our work is the first human-guided approach to address the location task of a detection model while addressing real-world challenges of aerial search and rescue. All datasets and code can be found at: https://github.com/ArtRuss/NOMAD.
Ordinal Multiple-Instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
Authors: Kaito Shiku; Kazuya Nishimura; Daiki Suehiro; Kiyohito Tanaka; Ryoma Bise
Venue: WACV 2025 (main)

Abstract: Patient-level diagnosis of severity in ulcerative colitis (UC) is common in clinical practice where the most severe score for a patient is typically recorded as the diagnosis result. However previous UC classification methods (i.e. image-level estimation) mainly assumed the input was a single image. Thus these methods can not utilize severity labels recorded in real clinical settings. In this paper we propose a patient-level severity estimation method by a transformer with selective aggregator tokens where a severity label is estimated from multiple images taken from a patient similar to a clinical setting. Our method can effectively aggregate features of severe parts from a set of images captured in each patient and it facilitates improving the discriminative ability between adjacent severity classes. Experiments demonstrate the effectiveness of the proposed method on two datasets compared with the state-of-the-art MIL methods. Moreover we evaluated our method using real clinical data and confirmed that our method outperformed the previous image-level methods. The code is publicly available at https://github.com/Shiku-Kaito/Ordinal-Multiple-instance-Learning-for-Ulcerative-Colitis-Severity-Estimation.
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalizatio
Authors: Aleksandr Matsun; Numan Saeed; Fadillah Adamsyah Maani; Mohammad Yaqub
Venue: WACV 2025 (main)

Abstract: Medical data often exhibits distribution shifts leading to performance degradation of deep learning models trained using standard supervised learning pipelines. Domain Generalization (DG) addresses this challenge with Single-Domain Generalization (SDG) being notably relevant due to the privacy and logistical constraints often inherent in medical data. Existing disentanglement-based SDG methods heavily rely on structural information from segmentation masks but classification labels do not offer similarly dense information. This work introduces a novel SDG method for medical image classification utilizing channel-wise contrastive disentanglement. The method is further refined with reconstruction-based style regularization to ensure distinct style and structural feature representations are extracted. We evaluate our method on the complex tasks of multicenter histopathology image classification and Diabetic Retinopathy (DR) grading in fundus images benchmarking it against state-of-the-art (SOTA) SDG baselines. Our results demonstrate that our method consistently outperforms the SOTA independently on the choice of the source domain while exhibiting greater performance stability. This study underscores the importance and challenges of exploring SDG frameworks for classification tasks. The code is publicly available at https://github.com/BioMedIA-MBZUAI/ConDiSR
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
Authors: Arya Bangun; Zhuo Cao; Alessio Quercia; Hanno Scharr; Elisabeth Pfaehler
Venue: WACV 2025 (main)

Abstract: Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data i.e. k-space data. This emphasizes the need for efficient solutions that can handle limited input while maintaining high-quality imaging. In contrast to previous methods only using 2D we propose a 3D MRI reconstruction method that leverages a regularized 3D diffusion model combined with optimization method. By incorporating diffusion-based priors our method improves image quality reduces noise and enhances the overall fidelity of 3D MRI reconstructions. We conduct comprehensive experiments analysis on clinical and plant science MRI datasets. To evaluate the algorithm effectiveness for under-sampled k-space data we also demonstrate its reconstruction performance with several undersampling patterns as well as with in- and out-of-distribution pre-trained data. In experiments we show that our method improves upon tested competitors
DreaMo: Articulated 3D Reconstruction from a Single Casual Video
Authors: Tao Tu; Ming-Feng Li; Chieh Hubert Lin; Yen-Chi Cheng; Min Sun; Ming-Hsuan Yang
Venue: WACV 2025 (main)

Abstract: Articulated 3D reconstruction has valuable applications in various domains yet it remains costly and demands intensive work from domain experts. Recent advancements in template-free learning methods show promising results with monocular videos. Nevertheless these approaches necessitate a comprehensive coverage of all viewpoints of the subject in the input video thus limiting their applicability to casually captured videos from online sources. In this work we study articulated 3D shape reconstruction from a single and casually captured Internet video where the subject's view coverage is incomplete. We propose DreaMo that jointly performs shape reconstruction while solving the challenging low-coverage regions with view-conditioned diffusion prior and several tailored regularizations. In addition we introduce a skeleton generation strategy to create human-interpretable skeletons from the learned neural bones and skinning weights without any predefined skeleton structures. We conduct our study on a self-collected internet video collection characterized by incomplete view coverage. DreaMo shows promising quality in novel-view rendering detailed articulated shape reconstruction and skeleton generation. Extensive qualitative and quantitative studies validate the efficacy of each proposed component and show existing methods are unable to solve correct geometry due to the incomplete view coverage.
A Regional-Level Resource-Saving Model for Winter Road Surface Snow Detection in Extreme Weathers
Authors: Xinhao Zhou; Tong Wang; Zhaodong Liu; Hao Wei; Guangyuan Pan
Venue: WACV 2025 (main)

Abstract: Achieving timely and accurate snow detection on road surfaces in extreme weather conditions is vital for both transportation and computer vision applications. However conventional object detection models particularly those designed for small targets fall short in addressing the challenge that is posed by special regional-level multi-scale recognition task. To this end an end-to-end precise and swift road surface snow detection architecture termed the Resource-Saving Snow Detect Model (RSSD) that includes a multidimensional directional attention mechanism is proposed. In this model we designed three dedicated modules namely Multi-dimensional Bidirectional Attention Module (MDBA) Split-EMA-Convolution (SEC) and Equal Split Convolution (ESC) to address the essential feature extraction and fusion tasks in snow detection. MDBA is able to promote lateral interaction and comprehensive feature fusion across scales while SEC can not only enhance feature extraction for regional awareness but also reduces computational load making it efficient under minimal computational power consumption. ESC preserves feature height fusion while significantly reducing computational costs thereby enhancing the real-time detection capability of the model. In experimental evaluations conducted with data collected by in-vehicle cameras from various roads in the United States and Canada the results demonstrate higher detection accuracy and speed compared to the latest Transformer-based real-time object detection methods and other exiting methods in the literature. Furthermore we validated the model's performance and data sensitivity through semi-supervised learning with 50000 unlabeled images. This research holds significant implications for winter road traffic and provides valuable insights for similar computer vision tasks.
EDMB: Edge Detector with Mamba
Authors: Yachuan Li; Xavier Soria Poma; Yun Bai; Qian Xiao; Chaozhi Yang; Guanlin Li; Zongmin Li
Venue: WACV 2025 (main)

Abstract: Transformer-based models have made significant progress in edge detection but their high computational cost is prohibitive. Recently vision Mamba have shown excellent ability in efficiently capturing long-range dependencies. Drawing inspiration from this we propose a novel edge detector with Mamba termed EDMB to efficiently generate high-quality multi-granularity edges. In EDMB Mamba is combined with a global-local architecture therefore it can focus on both global information and fine-grained cues. The fine-grained cues play a crucial role in edge detection but are usually ignored by ordinary Mamba. We design a novel decoder to construct learnable Gaussian distributions by fusing global features and fine-grained features. And the multi-grained edges are generated by sampling from the distributions. In order to make multi-granularity edges applicable to single-label data we introduce Evidence Lower Bound loss to supervise the learning of the distributions. On the multi-label dataset BSDS500 our proposed EDMB achieves competitive single-granularity ODS 0.837 and multi-granularity ODS 0.851 without multi-scale test or extra PASCAL-VOC data. Remarkably EDMB can be extended to single-label datasets such as NYUDv2 and BIPED. The source code is available at https://github.com/Li-yachuan/EDMB.
USWformer: Efficient Sparse Wavelet Transformer for Underwater Image Enhancement
Authors: Priyanka Mishra; Nancy Mehta; Santosh Kumar Vipparthi; Subrahmanyam Murala
Venue: WACV 2025 (main)

Abstract: Transformer-based methods have shown great promise in underwater image enhancement (UIE) tasks due to their capability to model long-range dependencies which are vital for reconstructing clear images. While numerous effective attention mechanisms have been devised to handle the computational requirements of transformers they frequently incorporate redundant information and noisy interactions from irrelevant regions. Additionally the current methods focusing solely on the raw pixel space constrains the exploration of the underwater image frequency dynamics thus hindering the models from fully leveraging their potential for producing high-quality images. To address these challenges we propose USWformer an efficient UIE Sparse Wavelet Transformer Network (1.19 M parameters) to eliminate the redundant features in both the spatial and frequency domains. The USWformer consists of two fundamental components: a Sparse Wavelet Self-Attention (SWSA) block and a Multi-scale Wavelet Feed-Forward Network (MWFN). The SWSA block selectively preserves essential attention scores from the keys corresponding to each query adjusting the feature details. MWFN further diminishes the feature redundancy in the aggregated features thereby improving the enhancement of the underwater images. We assess the efficacy of our approach across benchmark datasets comprising synthetic and real-world underwater images showcasing its superiority via thorough ablation studies and comparative analyses.
Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models
Authors: Danush Kumar Venkatesh; Dominik Rivoir; Micha Pfeiffer; Fiona Kolbinger; Stefanie Speidel
Venue: WACV 2025 (main)

Abstract: In computer-assisted surgery automatically recognizing anatomical organs is crucial for understanding the surgical scene and providing intraoperative assistance. While machine learning models can identify such structures their deployment is hindered by the need for labeled diverse surgical datasets with anatomical annotations. Labeling multiple classes (i.e. organs) in a surgical scene is time-intensive requiring medical experts. Although synthetically generated images can enhance segmentation performance maintaining both organ structure and texture during generation is challenging. We introduce a multi-stage approach using diffusion models to generate multi-class surgical datasets with annotations. Our framework improves anatomy awareness by training organ specific models with an inpainting objective guided by binary segmentation masks. The organs are generated with an inference pipeline using pre-trained ControlNet to maintain the organ structure. The synthetic multi-class datasets are constructed through an image composition step ensuring structural and textural consistency. This versatile approach allows the generation of multi-class datasets from real binary datasets and simulated surgical masks. We thoroughly evaluate the generated datasets on image quality and downstream segmentation achieving a 15% improvement in segmentation scores when combined with real images.
Identity Curvature Laplace Approximation for Improved Out-of-Distribution Detection
Authors: Maksim Zhdanov; Stanislav Dereka; Sergey Kolesnikov
Venue: WACV 2025 (main)

Abstract: Uncertainty estimation is crucial in safety-critical applications where robust out-of-distribution (OOD) detection is essential. Traditional Bayesian methods though effective are often hindered by high computational demands. As an alternative Laplace approximation offers a more practical and efficient approach to uncertainty estimation. In this paper we introduce the Identity Curvature Laplace Approximation (ICLA) a novel method that challenges the conventional posterior covariance formulation by using identity curvature and optimizing prior precision. This innovative design significantly enhances OOD detection performance on well-known datasets such as CIFAR-10 CIFAR-100 and ImageNet while maintaining calibration scores. We attribute this improvement to the alignment issues between typical feature embeddings and curvature as measured by the Fisher information matrix. Our findings are further supported by demonstrating that incorporating Fisher penalty or sharpness-aware minimization techniques can greatly enhance the uncertainty estimation capabilities of standard Laplace approximation.
CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring
Authors: Hao Fu; Naman Patel; Prashanth Krishnamurthy; Farshad khorrami
Venue: WACV 2025 (main)

Abstract: Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models. Recent advances in vision language foundation models have made them capable of detecting OOD samples without requiring in-distribution (ID) images. However these zero-shot methods often underperform as they do not adequately consider ID class likelihoods in their detection confidence scoring. Hence we introduce CLIPScope a zero-shot OOD detection approach that normalizes the confidence score of a sample by class likelihoods akin to a Bayesian posterior update. Furthermore CLIPScope incorporates a novel strategy to mine OOD classes from a large lexical database. It selects class labels that are farthest and nearest to ID classes in terms of CLIP embedding distance to maximize coverage of OOD samples. We conduct extensive ablation studies and empirical evaluations demonstrating state of the art performance of CLIPScope across various OOD detection benchmarks.