pwc-archive/papers-with-abstracts huggingface dataset:
Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach
Authors: Shuo Wang, Tong Ren, Nan Cheng, Rong Wang, Li Zhang
Date: 2025-05-16 00:00:00
Tasks: 3D Reconstruction, Mixed Reality

Background and Objective: Precise preoperative planning and effective physician training for coronary interventions are increasingly important. Despite advances in medical imaging technologies, transforming static or limited dynamic imaging data into comprehensive dynamic cardiac models remains challenging. Existing training systems lack accurate simulation of cardiac physiological dynamics. This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA, integrating digital twin technology, computer vision, and physical model manufacturing to provide precise, personalized tools for interventional cardiology. Methods: Using 4D-CTA data from a 60-year-old female with three-vessel coronary stenosis, we segmented cardiac chambers and coronary arteries, constructed dynamic models, and implemented skeletal skinning weight computation to simulate vessel deformation across 20 cardiac phases. Transparent vascular physical models were manufactured using medical-grade silicone. We developed cardiac output analysis and virtual angiography systems, implemented guidewire 3D reconstruction using binocular stereo vision, and evaluated the system through angiography validation and CABG training applications. Results: Morphological consistency between virtual and real angiography reached 80.9%. Dice similarity coefficients for guidewire motion ranged from 0.741-0.812, with mean trajectory errors below 1.1 mm. The transparent model demonstrated advantages in CABG training, allowing direct visualization while simulating beating heart challenges. Conclusion: Our patient-specific digital-physical twin approach effectively reproduces both anatomical structures and dynamic characteristics of coronary vasculature, offering a dynamic environment with visual and tactile feedback valuable for education and clinical planning.
VISTA: Enhancing Vision-Text Alignment in MLLMs via Cross-Modal Mutual Information Maximization
Authors: Mingxiao Li, Na Su, Fang Qu, Zhizhou Zhong, Ziyang Chen, Yuan Li, Zhaopeng Tu, Xiaolong Li
Date: 2025-05-16 00:00:00
Tasks: cross-modal alignment, MME

Current multimodal large language models (MLLMs) face a critical challenge in modality alignment, often exhibiting a bias towards textual information at the expense of other modalities like vision. This paper conducts a systematic information-theoretic analysis of the widely used cross-entropy loss in MLLMs, uncovering its implicit alignment objective. Our theoretical investigation reveals that this implicit objective has inherent limitations, leading to a degradation of cross-modal alignment as text sequence length increases, thereby hindering effective multimodal information fusion. To overcome these drawbacks, we propose Vision-Text Alignment (VISTA), a novel approach guided by our theoretical insights. VISTA introduces an explicit alignment objective designed to maximize cross-modal mutual information, preventing the degradation of visual alignment. Notably, VISTA enhances the visual understanding capabilities of existing MLLMs without requiring any additional trainable modules or extra training data, making it both efficient and practical. Our method significantly outperforms baseline models across more than a dozen benchmark datasets, including VQAv2, MMStar, and MME, paving the way for new directions in MLLM modal alignment research.
Towards Cross-modal Retrieval in Chinese Cultural Heritage Documents: Dataset and Solution
Authors: Junyi Yuan, Jian Zhang, Fangyu Wu, Dongming Lu, Huanda Lu, Qiufeng Wang
Date: 2025-05-16 00:00:00
Tasks: Cross-Modal Retrieval, Image to text, Image-to-Text Retrieval, Retrieval, Text Retrieval

China has a long and rich history, encompassing a vast cultural heritage that includes diverse multimodal information, such as silk patterns, Dunhuang murals, and their associated historical narratives. Cross-modal retrieval plays a pivotal role in understanding and interpreting Chinese cultural heritage by bridging visual and textual modalities to enable accurate text-to-image and image-to-text retrieval. However, despite the growing interest in multimodal research, there is a lack of specialized datasets dedicated to Chinese cultural heritage, limiting the development and evaluation of cross-modal learning models in this domain. To address this gap, we propose a multimodal dataset named CulTi, which contains 5,726 image-text pairs extracted from two series of professional documents, respectively related to ancient Chinese silk and Dunhuang murals. Compared to existing general-domain multimodal datasets, CulTi presents a challenge for cross-modal retrieval: the difficulty of local alignment between intricate decorative motifs and specialized textual descriptions. To address this challenge, we propose LACLIP, a training-free local alignment strategy built upon a fine-tuned Chinese-CLIP. LACLIP enhances the alignment of global textual descriptions with local visual regions by computing weighted similarity scores during inference. Experimental results on CulTi demonstrate that LACLIP significantly outperforms existing models in cross-modal retrieval, particularly in handling fine-grained semantic associations within Chinese cultural heritage.
M4-SAR: A Multi-Resolution, Multi-Polarization, Multi-Scene, Multi-Source Dataset and Benchmark for Optical-SAR Fusion Object Detection
Authors: Chao Wang, Wei Lu, Xiang Li, Jian Yang, Lei Luo
Date: 2025-05-16 00:00:00
Tasks: Benchmarking, object-detection, Object Detection

Single-source remote sensing object detection using optical or SAR images struggles in complex environments. Optical images offer rich textural details but are often affected by low-light, cloud-obscured, or low-resolution conditions, reducing the detection performance. SAR images are robust to weather, but suffer from speckle noise and limited semantic expressiveness. Optical and SAR images provide complementary advantages, and fusing them can significantly improve the detection accuracy. However, progress in this field is hindered by the lack of large-scale, standardized datasets. To address these challenges, we propose the first comprehensive dataset for optical-SAR fusion object detection, named Multi-resolution, Multi-polarization, Multi-scene, Multi-source SAR dataset (M4-SAR). It contains 112,184 precisely aligned image pairs and nearly one million labeled instances with arbitrary orientations, spanning six key categories. To enable standardized evaluation, we develop a unified benchmarking toolkit that integrates six state-of-the-art multi-source fusion methods. Furthermore, we propose E2E-OSDet, a novel end-to-end multi-source fusion detection framework that mitigates cross-domain discrepancies and establishes a robust baseline for future studies. Extensive experiments on M4-SAR demonstrate that fusing optical and SAR data can improve $mAP$ by 5.7\% over single-source inputs, with particularly significant gains in complex environments. The dataset and code are publicly available at https://github.com/wchao0601/M4-SAR.
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark
Authors: Yunkang Cao, Yuqi Cheng, Xiaohao Xu, Yiheng Zhang, Yihan Sun, Yuxiang Tan, Yuxin Zhang, Xiaonan Huang, Weiming Shen
Date: 2025-05-16 00:00:00
Tasks: Anomaly Detection, Benchmarking

The practical deployment of Visual Anomaly Detection (VAD) systems is hindered by their sensitivity to real-world imaging variations, particularly the complex interplay between viewpoint and illumination which drastically alters defect visibility. Current benchmarks largely overlook this critical challenge. We introduce Multi-View Multi-Illumination Anomaly Detection (M2AD), a new large-scale benchmark comprising 119,880 high-resolution images designed explicitly to probe VAD robustness under such interacting conditions. By systematically capturing 999 specimens across 10 categories using 12 synchronized views and 10 illumination settings (120 configurations total), M2AD enables rigorous evaluation. We establish two evaluation protocols: M2AD-Synergy tests the ability to fuse information across diverse configurations, and M2AD-Invariant measures single-image robustness against realistic view-illumination effects. Our extensive benchmarking shows that state-of-the-art VAD methods struggle significantly on M2AD, demonstrating the profound challenge posed by view-illumination interplay. This benchmark serves as an essential tool for developing and validating VAD methods capable of overcoming real-world complexities. Our full dataset and test suite will be released at https://hustcyq.github.io/M2AD to facilitate the field.
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning
Authors: Weilai Xiang, Hongyu Yang, Di Huang, Yunhong Wang
Date: 2025-05-16 00:00:00
Tasks: Denoising, Image Generation

While diffusion models have gained prominence in image synthesis, their generative pre-training has been shown to yield discriminative representations, paving the way towards unified visual generation and understanding. However, two key questions remain: 1) Can these representations be leveraged to improve the training of diffusion models themselves, rather than solely benefiting downstream tasks? 2) Can the feature quality be enhanced to rival or even surpass modern self-supervised learners, without compromising generative capability? This work addresses these questions by introducing self-conditioning, a straightforward yet effective mechanism that internally leverages the rich semantics inherent in denoising network to guide its own decoding layers, forming a tighter bottleneck that condenses high-level semantics to improve generation. Results are compelling: our method boosts both generation FID and recognition accuracy with 1% computational overhead and generalizes across diverse diffusion architectures. Crucially, self-conditioning facilitates an effective integration of discriminative techniques, such as contrastive self-distillation, directly into diffusion models without sacrificing generation quality. Extensive experiments on pixel-space and latent-space datasets show that in linear evaluations, our enhanced diffusion models, particularly UViT and DiT, serve as strong representation learners, surpassing various self-supervised models.
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
Authors: Bo Du, Xuekang Zhu, Xiaochen Ma, Chenfan Qu, Kaiwen Feng, Zhe Yang, Chi-Man Pun, Jian Liu, Jizhe Zhou
Date: 2025-05-16 00:00:00
Tasks: All, DeepFake Detection, Face Swapping, Fake Image Detection, Image Manipulation, Image Manipulation Detection, Image Manipulation Localization

The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank. The absence of a unified benchmark results in significant domain silos, where each domain independently constructs its datasets, models, and evaluation protocols without interoperability, preventing cross-domain comparisons and hindering the development of the entire FIDL field. To close the domain silo barrier, we propose ForensicHub, the first unified benchmark & codebase for all-domain fake image detection and localization. Considering drastic variations on dataset, model, and evaluation configurations across all domains, as well as the scarcity of open-sourced baseline models and the lack of individual benchmarks in some domains, ForensicHub: i) proposes a modular and configuration-driven architecture that decomposes forensic pipelines into interchangeable components across datasets, transforms, models, and evaluators, allowing flexible composition across all domains; ii) fully implements 10 baseline models, 6 backbones, 2 new benchmarks for AIGC and Doc, and integrates 2 existing benchmarks of DeepfakeBench and IMDLBenCo through an adapter-based design; iii) conducts indepth analysis based on the ForensicHub, offering 8 key actionable insights into FIDL model architecture, dataset characteristics, and evaluation standards. ForensicHub represents a significant leap forward in breaking the domain silos in the FIDL field and inspiring future breakthroughs.
Towards Robust and Controllable Text-to-Motion via Masked Autoregressive Diffusion
Authors: Zongye Zhang, Bohan Kong, Qingjie Liu, Yunhong Wang
Date: 2025-05-16 00:00:00
Tasks: Motion Generation, Motion Synthesis

Generating 3D human motion from text descriptions remains challenging due to the diverse and complex nature of human motion. While existing methods excel within the training distribution, they often struggle with out-of-distribution motions, limiting their applicability in real-world scenarios. Existing VQVAE-based methods often fail to represent novel motions faithfully using discrete tokens, which hampers their ability to generalize beyond seen data. Meanwhile, diffusion-based methods operating on continuous representations often lack fine-grained control over individual frames. To address these challenges, we propose a robust motion generation framework MoMADiff, which combines masked modeling with diffusion processes to generate motion using frame-level continuous representations. Our model supports flexible user-provided keyframe specification, enabling precise control over both spatial and temporal aspects of motion synthesis. MoMADiff demonstrates strong generalization capability on novel text-to-motion datasets with sparse keyframes as motion prompts. Extensive experiments on two held-out datasets and two standard benchmarks show that our method consistently outperforms state-of-the-art models in motion quality, instruction fidelity, and keyframe adherence.
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?
Authors: An-Lan Wang, Jingqun Tang, Liao Lei, Hao Feng, Qi Liu, Xiang Fei, Jinghui Lu, Han Wang, Weiwei Liu, Hao liu, Yuliang Liu, Xiang Bai, Can Huang
Date: 2025-05-16 00:00:00
Tasks: document understanding

The rapid advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced capabilities in Document Understanding. However, prevailing benchmarks like DocVQA and ChartQA predominantly comprise \textit{scanned or digital} documents, inadequately reflecting the intricate challenges posed by diverse real-world scenarios, such as variable illumination and physical distortions. This paper introduces WildDoc, the inaugural benchmark designed specifically for assessing document understanding in natural environments. WildDoc incorporates a diverse set of manually captured document images reflecting real-world conditions and leverages document sources from established benchmarks to facilitate comprehensive comparisons with digital or scanned documents. Further, to rigorously evaluate model robustness, each document is captured four times under different conditions. Evaluations of state-of-the-art MLLMs on WildDoc expose substantial performance declines and underscore the models' inadequate robustness compared to traditional benchmarks, highlighting the unique challenges posed by real-world document understanding. Our project homepage is available at https://bytedance.github.io/WildDoc.
Rethinking the Mean Teacher Strategy from the Perspective of Self-paced Learning
Authors: Pengchen Zhang, Alan J. X. Guo, Sipin Luo, Zhe Han, Lin Guo
Date: 2025-05-16 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

Semi-supervised medical image segmentation has attracted significant attention due to its potential to reduce manual annotation costs. The mean teacher (MT) strategy, commonly understood as introducing smoothed, temporally lagged consistency regularization, has demonstrated strong performance across various tasks in this field. In this work, we reinterpret the MT strategy on supervised data as a form of self-paced learning, regulated by the output agreement between the temporally lagged teacher model and the ground truth labels. This idea is further extended to incorporate agreement between a temporally lagged model and a cross-architectural model, which offers greater flexibility in regulating the learning pace and enables application to unlabeled data. Specifically, we propose dual teacher-student learning (DTSL), a framework that introduces two groups of teacher-student models with different architectures. The output agreement between the cross-group teacher and student models is used as pseudo-labels, generated via a Jensen-Shannon divergence-based consensus label generator (CLG). Extensive experiments on popular datasets demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches. Ablation studies further validate the effectiveness of the proposed modules.
Classifying Shelf Life Quality of Pineapples by Combining Audio and Visual Features
Authors: Yi-Lu Jiang, Wen-Chang Chang, Ching-Lin Wang, Kung-Liang Hsu, Chih-Yi Chiu
Date: 2025-05-16 00:00:00

Determining the shelf life quality of pineapples using non-destructive methods is a crucial step to reduce waste and increase income. In this paper, a multimodal and multiview classification model was constructed to classify pineapples into four quality levels based on audio and visual characteristics. For research purposes, we compiled and released the PQC500 dataset consisting of 500 pineapples with two modalities: one was tapping pineapples to record sounds by multiple microphones and the other was taking pictures by multiple cameras at different locations, providing multimodal and multi-view audiovisual features. We modified the contrastive audiovisual masked autoencoder to train the cross-modal-based classification model by abundant combinations of audio and visual pairs. In addition, we proposed to sample a compact size of training data for efficient computation. The experiments were evaluated under various data and model configurations, and the results demonstrated that the proposed cross-modal model trained using audio-major sampling can yield 84% accuracy, outperforming the unimodal models of only audio and only visual by 6% and 18%, respectively.
CleanPatrick: A Benchmark for Image Data Cleaning
Authors: Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Elisabeth Victoria Goessinger, Hanna Lindemann, Marie Bargiela, Marie Hofbauer, Omar Badri, Philipp Tschandl, Arash Koochek, Matthew Groh, Alexander A. Navarini, Marc Pouly
Date: 2025-05-16 00:00:00
Tasks: Benchmarking, Label Error Detection, SSIM

Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world relevance. We introduce CleanPatrick, the first large-scale benchmark for data cleaning in the image domain, built upon the publicly available Fitzpatrick17k dermatology dataset. We collect 496,377 binary annotations from 933 medical crowd workers, identify off-topic samples (4%), near-duplicates (21%), and label errors (22%), and employ an aggregation model inspired by item-response theory followed by expert review to derive high-quality ground truth. CleanPatrick formalizes issue detection as a ranking task and adopts typical ranking metrics mirroring real audit workflows. Benchmarking classical anomaly detectors, perceptual hashing, SSIM, Confident Learning, NoiseRank, and SelfClean, we find that, on CleanPatrick, self-supervised representations excel at near-duplicate detection, classical methods achieve competitive off-topic detection under constrained review budgets, and label-error detection remains an open challenge for fine-grained medical classification. By releasing both the dataset and the evaluation framework, CleanPatrick enables a systematic comparison of image-cleaning strategies and paves the way for more reliable data-centric artificial intelligence.
Artifacts of Idiosyncracy in Global Street View Data
Authors: Tim Alpherts, Sennay Ghebreab, Nanne van Noord
Date: 2025-05-16 00:00:00

Street view data is increasingly being used in computer vision applications in recent years. Machine learning datasets are collected for these applications using simple sampling techniques. These datasets are assumed to be a systematic representation of cities, especially when densely sampled. Prior works however, show that there are clear gaps in coverage, with certain cities or regions being covered poorly or not at all. Here we demonstrate that a cities' idiosyncracies, such as city layout, may lead to biases in street view data for 28 cities across the globe, even when they are densely covered. We quantitatively uncover biases in the distribution of coverage of street view data and propose a method for evaluation of such distributions to get better insight in idiosyncracies in a cities' coverage. In addition, we perform a case study of Amsterdam with semi-structured interviews, showing how idiosyncracies of the collection process impact representation of cities and regions and allowing us to address biases at their source.
CUBIC: Concept Embeddings for Unsupervised Bias Identification using VLMs
Authors: David Méndez, Gianpaolo Bontempo, Elisa Ficarra, Roberto Confalonieri, Natalia Díaz-Rodríguez
Date: 2025-05-16 00:00:00

Deep vision models often rely on biases learned from spurious correlations in datasets. To identify these biases, methods that interpret high-level, human-understandable concepts are more effective than those relying primarily on low-level features like heatmaps. A major challenge for these concept-based methods is the lack of image annotations indicating potentially bias-inducing concepts, since creating such annotations requires detailed labeling for each dataset and concept, which is highly labor-intensive. We present CUBIC (Concept embeddings for Unsupervised Bias IdentifiCation), a novel method that automatically discovers interpretable concepts that may bias classifier behavior. Unlike existing approaches, CUBIC does not rely on predefined bias candidates or examples of model failures tied to specific biases, as such information is not always available. Instead, it leverages image-text latent space and linear classifier probes to examine how the latent representation of a superclass label$\unicode{x2014}$shared by all instances in the dataset$\unicode{x2014}$is influenced by the presence of a given concept. By measuring these shifts against the normal vector to the classifier's decision boundary, CUBIC identifies concepts that significantly influence model predictions. Our experiments demonstrate that CUBIC effectively uncovers previously unknown biases using Vision-Language Models (VLMs) without requiring the samples in the dataset where the classifier underperforms or prior knowledge of potential biases.