pwc-archive/papers-with-abstracts huggingface dataset:
Decoupled Dynamics Framework with Neural Fields for 3D Spatio-temporal Prediction of Vehicle Collisions
Authors: SangHyuk Kim, Minsik Seo, Namwoo Kang
Date: 2025-03-25 00:00:00

This study proposes a neural framework that predicts 3D vehicle collision dynamics by independently modeling global rigid-body motion and local structural deformation. Unlike approaches directly predicting absolute displacement, this method explicitly separates the vehicle's overall translation and rotation from its structural deformation. Two specialized networks form the core of the framework: a quaternion-based Rigid Net for rigid motion and a coordinate-based Deformation Net for local deformation. By independently handling fundamentally distinct physical phenomena, the proposed architecture achieves accurate predictions without requiring separate supervision for each component. The model, trained on only 10% of available simulation data, significantly outperforms baseline models, including single multi-layer perceptron (MLP) and deep operator networks (DeepONet), with prediction errors reduced by up to 83%. Extensive validation demonstrates strong generalization to collision conditions outside the training range, accurately predicting responses even under severe impacts involving extreme velocities and large impact angles. Furthermore, the framework successfully reconstructs high-resolution deformation details from low-resolution inputs without increased computational effort. Consequently, the proposed approach provides an effective, computationally efficient method for rapid and reliable assessment of vehicle safety across complex collision scenarios, substantially reducing the required simulation data and time while preserving prediction fidelity.
DeCAP: Context-Adaptive Prompt Generation for Debiasing Zero-shot Question Answering in Large Language Models
Authors: Suyoung Bae, YunSeok Choi, Jee-Hyong Lee
Date: 2025-03-25 00:00:00
Tasks: Fairness, Question Answering

While Large Language Models (LLMs) excel in zero-shot Question Answering (QA), they tend to expose biases in their internal knowledge when faced with socially sensitive questions, leading to a degradation in performance. Existing zero-shot methods are efficient but fail to consider context and prevent bias propagation in the answers. To address this, we propose DeCAP, a method for debiasing LLMs using Context-Adaptive Prompt Generation. DeCAP leverages a Question Ambiguity Detection to take appropriate debiasing actions based on the context and a Neutral Answer Guidance Generation to suppress the LLMs make objective judgments about the context, minimizing the propagation of bias from their internal knowledge. Our various experiments across eight LLMs show that DeCAP achieves state-of-the-art zero-shot debiased QA performance. This demonstrates DeCAP's efficacy in enhancing the fairness and accuracy of LLMs in diverse QA settings.
PPL: Pairwise Prototype Learning for Masked Face Recognition
Authors: Kim, Minsoo; Nam, Gi Pyo; Hong, Yu-Jin; Kim, Ig-Jae
Date: 2022-11-21 00:00:00
Proceeding: 33rd British Machine Vision Conference 2022 2022 11
Tasks: Face Recognition, Fine-Grained Image Recognition

The occlusion caused by a facemask emerged as a new challenge in face recognition as the pandemic of COVID-19 made wearing a facemask an everyday practice. The recognition performance of the previous approaches degraded in recognising a face with a facemask since the models were trained to extract features from the overall face and not many face samples with a facemask were available. Although the previously proposed method of using both face data without facemasks and those with synthesised facemasks for training improved the recognition performance, a certain drop in the recognition performance for faces without facemasks was observed. Thus, we propose an approach that can achieve robust recognition of faces with facemasks without compromising that of faces without facemasks. This study broke free from using a single prototype and designed Pairwise Prototype Learning (PPL) which separated the prototype depending on the facemask condition of the face data. Models trained with the proposed PPL method outperformed those trained with previously suggested methods in recognising both faces with and without facemasks. On top of presenting a new MFW+ dataset for masked face recognition benchmark, our study found a simple and intuitive way to improve recognition performance on all benchmarks, overcoming the limitation of using a single prototype in face recognition for faces with facemasks. All codes of PPL are available at https://github.com/kim1102/PPL-MFR.
Spiideo SoccerNet SynLoc: Single Frame World Coordinate Athlete Detection and Localization with Synthetic Data
Authors: Håkan Ardö, Mikael Nilsson, Anthony Cioppa, Floriane Magera, Silvio Giancola, Haochen Liu, Bernard Ghanem, Marc Van Droogenbroeck
Date: 2025-02-27 00:00:00
Proceeding: VISAPP 2025 2
Tasks: 3D Object Detection, Sports Analytics

Currently, most research and public datasets for video sports analytics are base on detecting players as bounding boxes in broadcast videos. Going from there to precise locations on the pitch is however hard. Modern solutions are making dedicated static cameras covering the entire pitch more readily accessible, and they are now used more and more even in lower tiers. To promote research that can take benefits of such cameras and produce more precise pitch locations, we introduce the Spiideo SoccerNet SynLoc dataset. It consists of synthetic athletes rendered on top of images from real world installation of such cameras. We also introduce a new task of detecting the players in the world pitch coordinate system and a new metric based solely on real world physical properties where the representation in the image is irrelevant. The dataset and code are publicly available at https://github. com/Spiideo/sskit.
Feature selection strategies for optimized heart disease diagnosis using ML and DL models
Authors: Bilal Ahmad, Jinfu Chen, HaiBao Chen
Date: 2025-03-20 00:00:00
Tasks: Decision Making, Diagnostic, Disease Prediction, feature selection

Heart disease remains one of the leading causes of morbidity and mortality worldwide, necessitating the development of effective diagnostic tools to enable early diagnosis and clinical decision-making. This study evaluates the impact of feature selection techniques Mutual Information (MI), Analysis of Variance (ANOVA), and Chi-Square on the predictive performance of various machine learning (ML) and deep learning (DL) models using a dataset of clinical indicators for heart disease. Eleven ML/DL models were assessed using metrics such as precision, recall, AUC score, F1-score, and accuracy. Results indicate that MI outperformed other methods, particularly for advanced models like neural networks, achieving the highest accuracy of 82.3% and recall score of 0.94. Logistic regression (accuracy 82.1%) and random forest (accuracy 80.99%) also demonstrated improved performance with MI. Simpler models such as Naive Bayes and decision trees achieved comparable results with ANOVA and Chi-Square, yielding accuracies of 76.45% and 75.99%, respectively, making them computationally efficient alternatives. Conversely, k Nearest Neighbors (KNN) and Support Vector Machines (SVM) exhibited lower performance, with accuracies ranging between 51.52% and 54.43%, regardless of the feature selection method. This study provides a comprehensive comparison of feature selection methods for heart disease prediction, demonstrating the critical role of feature selection in optimizing model performance. The results offer practical guidance for selecting appropriate feature selection techniques based on the chosen classification algorithm, contributing to the development of more accurate and efficient diagnostic tools for enhanced clinical decision-making in cardiology.
Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection
Authors: Andrii Yermakov, Jan Cech, Jiri Matas
Date: 2025-03-25 00:00:00
Tasks: DeepFake Detection, Face Swapping, Metric Learning, parameter-efficient fine-tuning

This paper tackles the challenge of detecting partially manipulated facial deepfakes, which involve subtle alterations to specific facial features while retaining the overall context, posing a greater detection difficulty than fully synthetic faces. We leverage the Contrastive Language-Image Pre-training (CLIP) model, specifically its ViT-L/14 visual encoder, to develop a generalizable detection method that performs robustly across diverse datasets and unknown forgery techniques with minimal modifications to the original model. The proposed approach utilizes parameter-efficient fine-tuning (PEFT) techniques, such as LN-tuning, to adjust a small subset of the model's parameters, preserving CLIP's pre-trained knowledge and reducing overfitting. A tailored preprocessing pipeline optimizes the method for facial images, while regularization strategies, including L2 normalization and metric learning on a hyperspherical manifold, enhance generalization. Trained on the FaceForensics++ dataset and evaluated in a cross-dataset fashion on Celeb-DF-v2, DFDC, FFIW, and others, the proposed method achieves competitive detection accuracy comparable to or outperforming much more complex state-of-the-art techniques. This work highlights the efficacy of CLIP's visual encoder in facial deepfake detection and establishes a simple, powerful baseline for future research, advancing the field of generalizable deepfake detection. The code is available at: https://github.com/yermandy/deepfake-detection
G-DexGrasp: Generalizable Dexterous Grasping Synthesis Via Part-Aware Prior Retrieval and Prior-Assisted Generation
Authors: Juntao Jian, Xiuping Liu, Zixuan Chen, Manyi Li, Jian Liu, Ruizhen Hu
Date: 2025-03-25 00:00:00
Tasks: Retrieval-augmented Generation

Recent advances in dexterous grasping synthesis have demonstrated significant progress in producing reasonable and plausible grasps for many task purposes. But it remains challenging to generalize to unseen object categories and diverse task instructions. In this paper, we propose G-DexGrasp, a retrieval-augmented generation approach that can produce high-quality dexterous hand configurations for unseen object categories and language-based task instructions. The key is to retrieve generalizable grasping priors, including the fine-grained contact part and the affordance-related distribution of relevant grasping instances, for the following synthesis pipeline. Specifically, the fine-grained contact part and affordance act as generalizable guidance to infer reasonable grasping configurations for unseen objects with a generative model, while the relevant grasping distribution plays as regularization to guarantee the plausibility of synthesized grasps during the subsequent refinement optimization. Our comparison experiments validate the effectiveness of our key designs for generalization and demonstrate the remarkable performance against the existing approaches. Project page: https://g-dexgrasp.github.io/
Single Image Denoising via a New Lightweight Learning-Based Model
Authors: Sadjad Rezvani, Fatemeh Soleymani Siahkar, Yasin Rezvani, Abdorreza Alavi Gharahbagh, Vahid Abolghasemi
Date: 2024-08-28 00:00:00
Proceeding: IEEE Access 2024 8
Tasks: Denoising, Image Denoising, Zero-Shot Learning

Restoring a high-quality image from a noisy version poses a significant challenge in computer vision, particularly in today’s context where high-resolution and large-sized images are prevalent. As such, fast and efficient techniques are required to address noise reduction in such images effectively. Deep CNN-based image-denoising algorithms have gained popularity due to the rapid growth of deep learning and convolutional neural networks (CNNs). However, many existing deep learning models require paired clean/noisy images for training, limiting their utility in real-world denoising scenarios. In this paper, we propose a fast residual denoising framework (FRDF) designed based on zero-shot learning to address this issue. The FRDF first employs a novel downsampling technique to generate six different images from the noisy input, which are then fed into a lightweight residual network with 23K parameters. The network effectively utilizes a hybrid loss function, including residual, regularization, and guidance losses, to produce high-quality denoised images. Our innovative downsampling approach incorporates zero-shot learning principles, enabling our framework to generalize to unseen noise types and adapt to diverse noise conditions without needing labelled data. Extensive experiments conducted on synthetic and real images confirm the superiority of our proposed approach over existing dataset-free methods. Extensive experiments conducted on synthetic and real images show that our method achieves up to 2 dB improvements in PSNR on the McMaster and Kodak24 datasets. This renders our approach applicable in scenarios with limited data availability and computational resources.
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
Authors: Farzad Beizaee, Gregory A. Lodygensky, Christian Desrosiers, Jose Dolz
Date: 2025-03-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Recent advances in diffusion models have spurred research into their application for Reconstruction-based unsupervised anomaly detection. However, these methods may struggle with maintaining structural integrity and recovering the anomaly-free content of abnormal regions, especially in multi-class scenarios. Furthermore, diffusion models are inherently designed to generate images from pure noise and struggle to selectively alter anomalous regions of an image while preserving normal ones. This leads to potential degradation of normal regions during reconstruction, hampering the effectiveness of anomaly detection. This paper introduces a reformulation of the standard diffusion model geared toward selective region alteration, allowing the accurate identification of anomalies. By modeling anomalies as noise in the latent space, our proposed \textbf{Deviation correction diffusion} (\Ours) model preserves the normal regions and encourages transformations exclusively on anomalous areas. This selective approach enhances the reconstruction quality, facilitating effective unsupervised detection and localization of anomaly regions. Comprehensive evaluations demonstrate the superiority of our method in accurately identifying and localizing anomalies in complex images, with pixel-level AUPRC improvements of 11-14\% over state-of-the-art models on well known anomaly detection datasets. The code is available at https://github.com/farzad-bz/DeCo-Diff
Surg-3M: A Dataset and Foundation Model for Perception in Surgical Settings
Authors: Chengan Che, Chao Wang, Tom Vercauteren, Sophia Tsoka, Luis C. Garcia-Peraza-Herrera
Date: 2025-03-25 00:00:00
Tasks: 4k, Action Recognition, Surgical phase recognition

Advancements in computer-assisted surgical procedures heavily rely on accurate visual data interpretation from camera systems used during surgeries. Traditional open-access datasets focusing on surgical procedures are often limited by their small size, typically consisting of fewer than 100 videos with less than 100K images. To address these constraints, a new dataset called Surg-3M has been compiled using a novel aggregation pipeline that collects high-resolution videos from online sources. Featuring an extensive collection of over 4K surgical videos and more than 3 million high-quality images from multiple procedure types, Surg-3M offers a comprehensive resource surpassing existing alternatives in size and scope, including two novel tasks. To demonstrate the effectiveness of this dataset, we present SurgFM, a self-supervised foundation model pretrained on Surg-3M that achieves impressive results in downstream tasks such as surgical phase recognition, action recognition, and tool presence detection. Combining key components from ConvNeXt, DINO, and an innovative augmented distillation method, SurgFM exhibits exceptional performance compared to specialist architectures across various benchmarks. Our experimental results show that SurgFM outperforms state-of-the-art models in multiple downstream tasks, including significant gains in surgical phase recognition (+8.9pp, +4.7pp, and +3.9pp of Jaccard in AutoLaparo, M2CAI16, and Cholec80), action recognition (+3.1pp of mAP in CholecT50) and tool presence detection (+4.6pp of mAP in Cholec80). Moreover, even when using only half of the data, SurgFM outperforms state-of-the-art models in AutoLaparo and achieves state-of-the-art performance in Cholec80. Both Surg-3M and SurgFM have significant potential to accelerate progress towards developing autonomous robotic surgery systems.
Computer institute management system project report
Authors: Kamal Acharya
Date: 2025-03-10 00:00:00
Proceeding: ResearchGate 2025 3
Tasks: Management

The deliverable product is named as “COMPUTER INSTITUTE MANAGEMENT SYSTEM”. The scope of automation of COMPUTER INSTITUTE MANAGEMENT SYSTEM is to provide record of all students, courses, fees, payrolls etc. At an institute about their training or courses at the click of a button rather than maintaining files of papers.“COMPUTER INSTITUTE MANAGEMENT SYSTEM” will automate the work of administrative staff which before this software was made was done manually. With the help of “COMPUTER INSTITUTE MANAGEMENT SYSTEM” administrative people like receptionist or center manager can get record of students at the click of a button. Record of students contains their fee status, exam result status (optional) etc.
GraphCheck: Multi-Path Fact-Checking with Entity-Relationship Graphs
Authors: Hyewon Jeon, Jay-Yoon Lee
Date: 2025-02-28 00:00:00
Tasks: Fact Checking

Automated fact-checking aims to assess the truthfulness of text based on relevant evidence, yet verifying complex claims requiring multi-hop reasoning remains a significant challenge. We propose GraphCheck, a novel framework that converts claims into entity-relationship graphs for comprehensive verification. By identifying relation between explicit entities and latent entities across multiple paths, GraphCheck enhances the adaptability and robustness of verification. Furthermore, we introduce DP-GraphCheck, a two-stage variant that improves performance by incorporating direct prompting as an initial filtering step. Experiments on the HOVER and EX-FEVER datasets show that our approach outperforms existing methods, particularly in multi-hop reasoning tasks. Furthermore, our two-stage framework generalizes well to other fact-checking pipelines, demonstrating its versatility.
CoLLM: A Large Language Model for Composed Image Retrieval
Authors: Chuong Huynh, Jinyu Yang, Ashish Tawari, Mubarak Shah, Son Tran, Raffay Hamid, Trishul Chilimbi, Abhinav Shrivastava
Date: 2025-03-25 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Retrieval, Language Modeling, Language Modelling, Large Language Model, Retrieval, Triplet, Zero-Shot Composed Image Retrieval (ZS-CIR)

Composed Image Retrieval (CIR) is a complex task that aims to retrieve images based on a multimodal query. Typical training data consists of triplets containing a reference image, a textual description of desired modifications, and the target image, which are expensive and time-consuming to acquire. The scarcity of CIR datasets has led to zero-shot approaches utilizing synthetic triplets or leveraging vision-language models (VLMs) with ubiquitous web-crawled image-caption pairs. However, these methods have significant limitations: synthetic triplets suffer from limited scale, lack of diversity, and unnatural modification text, while image-caption pairs hinder joint embedding learning of the multimodal query due to the absence of triplet data. Moreover, existing approaches struggle with complex and nuanced modification texts that demand sophisticated fusion and understanding of vision and language modalities. We present CoLLM, a one-stop framework that effectively addresses these limitations. Our approach generates triplets on-the-fly from image-caption pairs, enabling supervised training without manual annotation. We leverage Large Language Models (LLMs) to generate joint embeddings of reference images and modification texts, facilitating deeper multimodal fusion. Additionally, we introduce Multi-Text CIR (MTCIR), a large-scale dataset comprising 3.4M samples, and refine existing CIR benchmarks (CIRR and Fashion-IQ) to enhance evaluation reliability. Experimental results demonstrate that CoLLM achieves state-of-the-art performance across multiple CIR benchmarks and settings. MTCIR yields competitive results, with up to 15% performance improvement. Our refined benchmarks provide more reliable evaluation metrics for CIR models, contributing to the advancement of this important field.
DIV-FF: Dynamic Image-Video Feature Fields For Environment Understanding in Egocentric Videos
Authors: Lorenzo Mur-Labadia, Josechu Guerrero, Ruben Martinez-Cantin
Date: 2025-03-11 00:00:00
Proceeding: CVPR 2025 1
Tasks: Scene Understanding

Environment understanding in egocentric videos is an important step for applications like robotics, augmented reality and assistive technologies. These videos are characterized by dynamic interactions and a strong dependence on the wearer engagement with the environment. Traditional approaches often focus on isolated clips or fail to integrate rich semantic and geometric information, limiting scene comprehension. We introduce Dynamic Image-Video Feature Fields (DIV FF), a framework that decomposes the egocentric scene into persistent, dynamic, and actor based components while integrating both image and video language features. Our model enables detailed segmentation, captures affordances, understands the surroundings and maintains consistent understanding over time. DIV-FF outperforms state-of-the-art methods, particularly in dynamically evolving scenarios, demonstrating its potential to advance long term, spatio temporal scene understanding.
FedMM-X: A Trustworthy and Interpretable Framework for Federated Multi-Modal Learning in Dynamic Environments
Authors: Sree Bhargavi Balija
Date: 2025-03-25 00:00:00
Tasks: Federated Learning, Out-of-Distribution Generalization

As artificial intelligence systems increasingly operate in Real-world environments, the integration of multi-modal data sources such as vision, language, and audio presents both unprecedented opportunities and critical challenges for achieving trustworthy intelligence. In this paper, we propose a novel framework that unifies federated learning with explainable multi-modal reasoning to ensure trustworthiness in decentralized, dynamic settings. Our approach, called FedMM-X (Federated Multi-Modal Explainable Intelligence), leverages cross-modal consistency checks, client-level interpretability mechanisms, and dynamic trust calibration to address challenges posed by data heterogeneity, modality imbalance, and out-of-distribution generalization. Through rigorous evaluation across federated multi-modal benchmarks involving vision-language tasks, we demonstrate improved performance in both accuracy and interpretability while reducing vulnerabilities to adversarial and spurious correlations. Further, we introduce a novel trust score aggregation method to quantify global model reliability under dynamic client participation. Our findings pave the way toward developing robust, interpretable, and socially responsible AI systems in Real-world environments.