pwc-archive/papers-with-abstracts huggingface dataset:
Users Favor LLM-Generated Content -- Until They Know It's AI
Authors: Petr Parshakov, Iuliia Naidenova, Sofia Paklina, Nikita Matkin, Cornel Nesseler
Date: 2025-02-23 00:00:00

In this paper, we investigate how individuals evaluate human and large langue models generated responses to popular questions when the source of the content is either concealed or disclosed. Through a controlled field experiment, participants were presented with a set of questions, each accompanied by a response generated by either a human or an AI. In a randomized design, half of the participants were informed of the response's origin while the other half remained unaware. Our findings indicate that, overall, participants tend to prefer AI-generated responses. However, when the AI origin is revealed, this preference diminishes significantly, suggesting that evaluative judgments are influenced by the disclosure of the response's provenance rather than solely by its quality. These results underscore a bias against AI-generated content, highlighting the societal challenge of improving the perception of AI work in contexts where quality assessments should be paramount.
A Stable and Strategy-Proof Controlled School Choice Mechanism with Integrated and Flexible Rules
Authors: Minoru Kitahara, Yasunori Okumura
Date: 2025-03-23 00:00:00
Tasks: scoring rule

We examine a controlled school choice model where students are categorized into different types, and the distribution of these types within a school influences its priority structure. This study provides a general framework that integrates existing controlled school choice models, including those utilizing reserve rules, quota rules, and bonus-point rules. Specifically, we introduce an adjusted scoring rule that unifies these rules. By achieving a matching that satisfies the stability defined in this framework, matching authorities can effectively manage the trade-offs inherent in controlled school choice markets. Moreover, the priority order for a school is represented as a weak order with each given assignment, meaning that ties are allowed. Our mechanism ensures a stable matching and satisfies strategy-proofness. In particular, when priority orders are restricted to linear orders with each given assignment, our mechanism guarantees student-optimal stability.
Shapley-Scarf Markets with Objective Indifferences
Authors: Will Sandholtz, Andrew Tai
Date: 2025-03-23 00:00:00
Tasks: Object

In many object allocation problems, some of the objects may effectively be indistinguishable from each other, such as with dorm rooms or school seats. In such cases, it is reasonable to assume that agents are indifferent between identical copies of the same object. We call this setting ``objective indifferences.'' Top trading cycles (TTC) with fixed tie-breaking has been suggested and used in practice to deal with indifferences in object allocation problems. Under general indifferences, TTC with fixed tie-breaking is not Pareto efficient nor group strategy-proof. Furthermore, it may not select the core, even when it exists. Under objective indifferences, agents are always and only indifferent between copies of the same object. In this setting, TTC with fixed tie-breaking maintains Pareto efficiency, group strategy-proofness, and core selection. In fact, we present domain characterization results which together show that objective indifferences is the most general setting where TTC with fixed tie-breaking maintains these important properties.
Non-Bayesian Learning in Misspecified Models
Authors: Sebastian Bervoets, Mathieu Faure, Ludovic Renou
Date: 2025-03-23 00:00:00

Deviations from Bayesian updating are traditionally categorized as biases, errors, or fallacies, thus implying their inherent ``sub-optimality.'' We offer a more nuanced view. We demonstrate that, in learning problems with misspecified models, non-Bayesian updating can outperform Bayesian updating.
Coordinated Shirking in Technology Adoption
Authors: Nicholas H. Tenev
Date: 2025-03-22 00:00:00

This paper studies a model of technology adoption: a manager tries to induce a group of workers to exert costly effort to vet a new technology before they choose whether to use it. The manager finds it too costly to simultaneously replace large groups of unproductive workers, so they shirk when coordination is possible. Widely applicable technology expands productive possibilities but also provides an opportunity for coordinated shirking, and can thus lead to widespread production failure. Furthermore, even workers who learn that they are using flawed technology may continue to do so. Applications include mortgage securitization in the financial crisis of 2008, and the adoption of generative artificial intelligence.
Joint Spectrogram Separation and TDOA Estimation using Optimal Transport
Authors: Linda Fabiani, Sebastian J. Schlecht, Isabel Haasler, Filip Elvander
Date: 2025-03-24 00:00:00
Tasks: blind source separation, Speech Enhancement

Separating sources is a common challenge in applications such as speech enhancement and telecommunications, where distinguishing between overlapping sounds helps reduce interference and improve signal quality. Additionally, in multichannel systems, correct calibration and synchronization are essential to separate and locate source signals accurately. This work introduces a method for blind source separation and estimation of the Time Difference of Arrival (TDOA) of signals in the time-frequency domain. Our proposed method effectively separates signal mixtures into their original source spectrograms while simultaneously estimating the relative delays between receivers, using Optimal Transport (OT) theory. By exploiting the structure of the OT problem, we combine the separation and delay estimation processes into a unified framework, optimizing the system through a block coordinate descent algorithm. We analyze the performance of the OT-based estimator under various noise conditions and compare it with conventional TDOA and source separation methods. Numerical simulation results demonstrate that our proposed approach can achieve a significant level of accuracy across diverse noise scenarios for physical speech signals in both TDOA and source separation tasks.
Target Speaker Selection for Neural Network Beamforming in Multi-Speaker Scenarios
Authors: Luan Vinícius Fiorio, Bruno Defraene, Johan David, Alex Young, Frans Widdershoven, Wim van Houtum, Ronald M. Aarts
Date: 2025-03-24 00:00:00

We propose a speaker selection mechanism (SSM) for the training of an end-to-end beamforming neural network, based on recent findings that a listener usually looks to the target speaker with a certain undershot angle. The mechanism allows the neural network model to learn toward which speaker to focus, during training, in a multi-speaker scenario, based on the position of listener and speakers. However, only audio information is necessary during inference. We perform acoustic simulations demonstrating the feasibility and performance when the SSM is employed in training. The results show significant increase in speech intelligibility, quality, and distortion metrics when compared to the minimum variance distortionless filter and the same neural network model trained without SSM. The success of the proposed method is a significant step forward toward the solution of the cocktail party problem.
Unsupervised Variational Acoustic Clustering
Authors: Luan Vinícius Fiorio, Bruno Defraene, Johan David, Frans Widdershoven, Wim van Houtum, Ronald M. Aarts
Date: 2025-03-24 00:00:00
Tasks: Clustering, Variational Inference

We propose an unsupervised variational acoustic clustering model for clustering audio data in the time-frequency domain. The model leverages variational inference, extended to an autoencoder framework, with a Gaussian mixture model as a prior for the latent space. Specifically designed for audio applications, we introduce a convolutional-recurrent variational autoencoder optimized for efficient time-frequency processing. Our experimental results considering a spoken digits dataset demonstrate a significant improvement in accuracy and clustering performance compared to traditional methods, showcasing the model's enhanced ability to capture complex audio patterns.
Learning to segment anatomy and lesions from disparately labeled sources in brain MRI
Authors: Meva Himmetoglu, Ilja Ciernik, Ender Konukoglu
Date: 2025-03-24 00:00:00
Tasks: Anatomy, Lesion Segmentation, Meta-Learning

Segmenting healthy tissue structures alongside lesions in brain Magnetic Resonance Images (MRI) remains a challenge for today's algorithms due to lesion-caused disruption of the anatomy and lack of jointly labeled training datasets, where both healthy tissues and lesions are labeled on the same images. In this paper, we propose a method that is robust to lesion-caused disruptions and can be trained from disparately labeled training sets, i.e., without requiring jointly labeled samples, to automatically segment both. In contrast to prior work, we decouple healthy tissue and lesion segmentation in two paths to leverage multi-sequence acquisitions and merge information with an attention mechanism. During inference, an image-specific adaptation reduces adverse influences of lesion regions on healthy tissue predictions. During training, the adaptation is taken into account through meta-learning and co-training is used to learn from disparately labeled training images. Our model shows an improved performance on several anatomical structures and lesions on a publicly available brain glioblastoma dataset compared to the state-of-the-art segmentation methods.
Dual-domain Multi-path Self-supervised Diffusion Model for Accelerated MRI Reconstruction
Authors: Yuxuan Zhang, Jinkui Hao, Bo Zhou
Date: 2025-03-24 00:00:00
Tasks: Diagnostic, MRI Reconstruction

Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient comfort. Recent advancements in deep learning, particularly diffusion models, have improved accelerated MRI reconstruction. However, existing diffusion models' training often relies on fully sampled data, models incur high computational costs, and often lack uncertainty estimation, limiting their clinical applicability. To overcome these challenges, we propose a novel framework, called Dual-domain Multi-path Self-supervised Diffusion Model (DMSM), that integrates a self-supervised dual-domain diffusion model training scheme, a lightweight hybrid attention network for the reconstruction diffusion model, and a multi-path inference strategy, to enhance reconstruction accuracy, efficiency, and explainability. Unlike traditional diffusion-based models, DMSM eliminates the dependency on training from fully sampled data, making it more practical for real-world clinical settings. We evaluated DMSM on two human MRI datasets, demonstrating that it achieves favorable performance over several supervised and self-supervised baselines, particularly in preserving fine anatomical structures and suppressing artifacts under high acceleration factors. Additionally, our model generates uncertainty maps that correlate reasonably well with reconstruction errors, offering valuable clinically interpretable guidance and potentially enhancing diagnostic confidence.
GS-Marker: Generalizable and Robust Watermarking for 3D Gaussian Splatting
Authors: Lijiang Li, Jinglu Wang, Xiang Ming, Yan Lu
Date: 2025-03-24 00:00:00
Tasks: 3DGS, Decoder

In the Generative AI era, safeguarding 3D models has become increasingly urgent. While invisible watermarking is well-established for 2D images with encoder-decoder frameworks, generalizable and robust solutions for 3D remain elusive. The main difficulty arises from the renderer between the 3D encoder and 2D decoder, which disrupts direct gradient flow and complicates training. Existing 3D methods typically rely on per-scene iterative optimization, resulting in time inefficiency and limited generalization. In this work, we propose a single-pass watermarking approach for 3D Gaussian Splatting (3DGS), a well-known yet underexplored representation for watermarking. We identify two major challenges: (1) ensuring effective training generalized across diverse 3D models, and (2) reliably extracting watermarks from free-view renderings, even under distortions. Our framework, named GS-Marker, incorporates a 3D encoder to embed messages, distortion layers to enhance resilience against various distortions, and a 2D decoder to extract watermarks from renderings. A key innovation is the Adaptive Marker Control mechanism that adaptively perturbs the initially optimized 3DGS, escaping local minima and improving both training stability and convergence. Extensive experiments show that GS-Marker outperforms per-scene training approaches in terms of decoding accuracy and model fidelity, while also significantly reducing computation time.
Rethinking Glaucoma Calibration: Voting-Based Binocular and Metadata Integration
Authors: Taejin Jeong, Joohyeok Kim, Jaehoon Joo, Yeonwoo Jung, Hyeonmin Kim, Seong Jae Hwang
Date: 2025-03-24 00:00:00
Tasks: Diagnostic

Glaucoma is an incurable ophthalmic disease that damages the optic nerve, leads to vision loss, and ranks among the leading causes of blindness worldwide. Diagnosing glaucoma typically involves fundus photography, optical coherence tomography (OCT), and visual field testing. However, the high cost of OCT often leads to reliance on fundus photography and visual field testing, both of which exhibit inherent inter-observer variability. This stems from glaucoma being a multifaceted disease that influenced by various factors. As a result, glaucoma diagnosis is highly subjective, emphasizing the necessity of calibration, which aligns predicted probabilities with actual disease likelihood. Proper calibration is essential to prevent overdiagnosis or misdiagnosis, which are critical concerns for high-risk diseases. Although AI has significantly improved diagnostic accuracy, overconfidence in models have worsen calibration performance. Recent study has begun focusing on calibration for glaucoma. Nevertheless, previous study has not fully considered glaucoma's systemic nature and the high subjectivity in its diagnostic process. To overcome these limitations, we propose V-ViT (Voting-based ViT), a novel framework that enhances calibration by incorporating disease-specific characteristics. V-ViT integrates binocular data and metadata, reflecting the multi-faceted nature of glaucoma diagnosis. Additionally, we introduce a MC dropout-based Voting System to address high subjectivity. Our approach achieves state-of-the-art performance across all metrics, including accuracy, demonstrating that our proposed methods are effective in addressing calibration issues. We validate our method using a custom dataset including binocular data.
Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model
Authors: Leheng Zhang, Weiyi You, Kexuan Shi, Shuhang Gu
Date: 2025-03-24 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Super-Resolution, Super-Resolution

Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a lengthy Markov chain, diffusion-based methods possess remarkable modeling capacity, enabling them to achieve outstanding performance in real-world scenarios. Unlike previous methods that focus on modifying the noise schedule or sampling process to enhance performance, our approach emphasizes the improved utilization of LR information. We find that different regions of the LR image can be viewed as corresponding to different timesteps in a diffusion process, where flat areas are closer to the target HR distribution but edge and texture regions are farther away. In these flat areas, applying a slight noise is more advantageous for the reconstruction. We associate this characteristic with uncertainty and propose to apply uncertainty estimate to guide region-specific noise level control, a technique we refer to as Uncertainty-guided Noise Weighting. Pixels with lower uncertainty (i.e., flat regions) receive reduced noise to preserve more LR information, therefore improving performance. Furthermore, we modify the network architecture of previous methods to develop our Uncertainty-guided Perturbation Super-Resolution (UPSR) model. Extensive experimental results demonstrate that, despite reduced model size and training overhead, the proposed UWSR method outperforms current state-of-the-art methods across various datasets, both quantitatively and qualitatively.
4DGC: Rate-Aware 4D Gaussian Compression for Efficient Streamable Free-Viewpoint Video
Authors: Qiang Hu, Zihan Zheng, Houqiang Zhong, Sihua Fu, Li Song, XiaoyunZhang, Guangtao Zhai, Yanfeng Wang
Date: 2025-03-24 00:00:00
Proceeding: CVPR 2025 1
Tasks: 3DGS, Quantization

3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences. However, the vast number of Gaussians and their associated attributes poses significant challenges for storage and transmission. Existing methods typically handle dynamic 3DGS representation and compression separately, neglecting motion information and the rate-distortion (RD) trade-off during training, leading to performance degradation and increased model redundancy. To address this gap, we propose 4DGC, a novel rate-aware 4D Gaussian compression framework that significantly reduces storage size while maintaining superior RD performance for FVV. Specifically, 4DGC introduces a motion-aware dynamic Gaussian representation that utilizes a compact motion grid combined with sparse compensated Gaussians to exploit inter-frame similarities. This representation effectively handles large motions, preserving quality and reducing temporal redundancy. Furthermore, we present an end-to-end compression scheme that employs differentiable quantization and a tiny implicit entropy model to compress the motion grid and compensated Gaussians efficiently. The entire framework is jointly optimized using a rate-distortion trade-off. Extensive experiments demonstrate that 4DGC supports variable bitrates and consistently outperforms existing methods in RD performance across multiple datasets.
ZECO: ZeroFusion Guided 3D MRI Conditional Generation
Authors: Feiran Wang, Bin Duan, Jiachen Tao, Nikhil Sharma, Dawen Cai, Yan Yan
Date: 2025-03-24 00:00:00
Tasks: Diagnostic, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segmentation model training demands specialized expertise and significant time investment, leading to a small dataset scale in clinical practice. In this paper, we present ZECO, a ZeroFusion guided 3D MRI conditional generation framework that extracts, compresses, and generates high-fidelity MRI images with corresponding 3D segmentation masks to mitigate data scarcity. To effectively capture inter-slice relationships within volumes, we introduce a Spatial Transformation Module that encodes MRI images into a compact latent space for the diffusion process. Moving beyond unconditional generation, our novel ZeroFusion method progressively maps 3D masks to MRI images in latent space, enabling robust training on limited datasets while avoiding overfitting. ZECO outperforms state-of-the-art models in both quantitative and qualitative evaluations on Brain MRI datasets across various modalities, showcasing its exceptional capability in synthesizing high-quality MRI images conditioned on segmentation masks.
Efficient Deep Learning Approaches for Processing Ultra-Widefield Retinal Imaging
Authors: Siwon Kim, Wooyung Yun, Jeongbin Oh, Soomok Lee
Date: 2025-03-23 00:00:00
Tasks: Data Augmentation

Deep learning has emerged as the predominant solution for classifying medical images. We intend to apply these developments to the ultra-widefield (UWF) retinal imaging dataset. Since UWF images can accurately diagnose various retina diseases, it is very important to clas sify them accurately and prevent them with early treatment. However, processing images manually is time-consuming and labor-intensive, and there are two challenges to automating this process. First, high perfor mance usually requires high computational resources. Artificial intelli gence medical technology is better suited for places with limited medical resources, but using high-performance processing units in such environ ments is challenging. Second, the problem of the accuracy of colour fun dus photography (CFP) methods. In general, the UWF method provides more information for retinal diagnosis than the CFP method, but most of the research has been conducted based on the CFP method. Thus, we demonstrate that these problems can be efficiently addressed in low performance units using methods such as strategic data augmentation and model ensembles, which balance performance and computational re sources while utilizing UWF images.
Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms
Authors: Nachuan Ma, Zhengfei Song, Qiang Hu, Chuang-Wei Liu, Yu Han, Yanting Zhang, Rui Fan, Lihua Xie
Date: 2025-03-23 00:00:00
Tasks: Computational Efficiency, Deep Learning

In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. Also, we create a dataset called UDTIRI-Crack, comprising $2,500$ high-quality images from seven public annotated sources, as the first extensive online benchmark in this field. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. In addition, the feasibility of foundation models and large language models (LLMs) for road crack detection is explored. Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The released benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.