pwc-archive/papers-with-abstracts huggingface dataset:
Zooming In on Fakes: A Novel Dataset for Localized AI-Generated Image Detection with Forgery Amplification Approach
Authors: Lvpan Cai, Haowei Wang, Jiayi Ji, YanShu ZhouMen, Yiwei Ma, Xiaoshuai Sun, Liujuan Cao, Rongrong Ji
Date: 2025-04-16 00:00:00

The rise of AI-generated image editing tools has made localized forgeries increasingly realistic, posing challenges for visual content integrity. Although recent efforts have explored localized AIGC detection, existing datasets predominantly focus on object-level forgeries while overlooking broader scene edits in regions such as sky or ground. To address these limitations, we introduce \textbf{BR-Gen}, a large-scale dataset of 150,000 locally forged images with diverse scene-aware annotations, which are based on semantic calibration to ensure high-quality samples. BR-Gen is constructed through a fully automated Perception-Creation-Evaluation pipeline to ensure semantic coherence and visual realism. In addition, we further propose \textbf{NFA-ViT}, a Noise-guided Forgery Amplification Vision Transformer that enhances the detection of localized forgeries by amplifying forgery-related features across the entire image. NFA-ViT mines heterogeneous regions in images, \emph{i.e.}, potential edited areas, by noise fingerprints. Subsequently, attention mechanism is introduced to compel the interaction between normal and abnormal features, thereby propagating the generalization traces throughout the entire image, allowing subtle forgeries to influence a broader context and improving overall detection robustness. Extensive experiments demonstrate that BR-Gen constructs entirely new scenarios that are not covered by existing methods. Take a step further, NFA-ViT outperforms existing methods on BR-Gen and generalizes well across current benchmarks. All data and codes are available at https://github.com/clpbc/BR-Gen.
Mitigating Long-tail Distribution in Oracle Bone Inscriptions: Dataset, Model, and Benchmark
Authors: Jinhao Li, Zijian Chen, Runze Jiang, Tingzhu Chen, Changbo Wang, Guangtao Zhai
Date: 2025-04-13 00:00:00
Tasks: Data Augmentation, Denoising

The oracle bone inscription (OBI) recognition plays a significant role in understanding the history and culture of ancient China. However, the existing OBI datasets suffer from a long-tail distribution problem, leading to biased performance of OBI recognition models across majority and minority classes. With recent advancements in generative models, OBI synthesis-based data augmentation has become a promising avenue to expand the sample size of minority classes. Unfortunately, current OBI datasets lack large-scale structure-aligned image pairs for generative model training. To address these problems, we first present the Oracle-P15K, a structure-aligned OBI dataset for OBI generation and denoising, consisting of 14,542 images infused with domain knowledge from OBI experts. Second, we propose a diffusion model-based pseudo OBI generator, called OBIDiff, to achieve realistic and controllable OBI generation. Given a clean glyph image and a target rubbing-style image, it can effectively transfer the noise style of the original rubbing to the glyph image. Extensive experiments on OBI downstream tasks and user preference studies show the effectiveness of the proposed Oracle-P15K dataset and demonstrate that OBIDiff can accurately preserve inherent glyph structures while transferring authentic rubbing styles effectively.
Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval
Authors: Yushuai Sun, Zikun Zhou, Dongmei Jiang, YaoWei Wang, Jun Yu, Guangming Lu, Wenjie Pei
Date: 2025-04-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Retrieval

Asymmetric retrieval is a typical scenario in real-world retrieval systems, where compatible models of varying capacities are deployed on platforms with different resource configurations. Existing methods generally train pre-defined networks or subnetworks with capacities specifically designed for pre-determined platforms, using compatible learning. Nevertheless, these methods suffer from limited flexibility for multi-platform deployment. For example, when introducing a new platform into the retrieval systems, developers have to train an additional model at an appropriate capacity that is compatible with existing models via backward-compatible learning. In this paper, we propose a Prunable Network with self-compatibility, which allows developers to generate compatible subnetworks at any desired capacity through post-training pruning. Thus it allows the creation of a sparse subnetwork matching the resources of the new platform without additional training. Specifically, we optimize both the architecture and weight of subnetworks at different capacities within a dense network in compatible learning. We also design a conflict-aware gradient integration scheme to handle the gradient conflicts between the dense network and subnetworks during compatible learning. Extensive experiments on diverse benchmarks and visual backbones demonstrate the effectiveness of our method. Our code and model are available at https://github.com/Bunny-Black/PrunNet.
A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images
Authors: Daiqi Liu, Fuxin Fan, Andreas Maier
Date: 2025-04-16 00:00:00
Tasks: Image Registration, Segmentation

Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging are used to transfer the surgical plan to the operating room through image registration, enabling quick intraoperative adjustments. Specifically, segmenting pelvic fractures from 2D X-ray imaging can assist in accurately positioning bone fragments and guiding the placement of screws or metal plates. In this study, we propose a novel deep learning-based category and fragment segmentation (CFS) framework for the automatic segmentation of pelvic bone fragments in 2D X-ray images. The framework consists of three consecutive steps: category segmentation, fragment segmentation, and post-processing. Our best model achieves an IoU of 0.91 for anatomical structures and 0.78 for fracture segmentation. Results demonstrate that the CFS framework is effective and accurate.
Mirror: Multimodal Cognitive Reframing Therapy for Rolling with Resistance
Authors: Subin Kim, Hoonrae Kim, Jihyun Lee, Yejin Jeon, Gary Geunbae Lee
Date: 2025-04-16 00:00:00

Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, allowing the AI therapist to better align its responses with the client's negative emotional state. Specifically, we introduce a new synthetic dataset, Multimodal Interactive Rolling with Resistance (Mirror), which is a novel synthetic dataset that pairs client statements with corresponding facial images. Using this dataset, we train baseline Vision-Language Models (VLMs) that can analyze facial cues, infer emotions, and generate empathetic responses to effectively manage resistance. They are then evaluated in terms of both the therapist's counseling skills and the strength of the therapeutic alliance in the presence of client resistance. Our results demonstrate that Mirror significantly enhances the AI therapist's ability to handle resistance, which outperforms existing text-based CBT approaches.
Synthetic Data for Blood Vessel Network Extraction
Authors: Joël Mathys, Andreas Plesner, Jorel Elmiger, Roger Wattenhofer
Date: 2025-04-16 00:00:00
Tasks: Graph Generation, Image Generation, Synthetic Data Generation

Blood vessel networks in the brain play a crucial role in stroke research, where understanding their topology is essential for analyzing blood flow dynamics. However, extracting detailed topological vessel network information from microscopy data remains a significant challenge, mainly due to the scarcity of labeled training data and the need for high topological accuracy. This work combines synthetic data generation with deep learning to automatically extract vessel networks as graphs from volumetric microscopy data. To combat data scarcity, we introduce a comprehensive pipeline for generating large-scale synthetic datasets that mirror the characteristics of real vessel networks. Our three-stage approach progresses from abstract graph generation through vessel mask creation to realistic medical image synthesis, incorporating biological constraints and imaging artifacts at each stage. Using this synthetic data, we develop a two-stage deep learning pipeline of 3D U-Net-based models for node detection and edge prediction. Fine-tuning on real microscopy data shows promising adaptation, improving edge prediction F1 scores from 0.496 to 0.626 by training on merely 5 manually labeled samples. These results suggest that automated vessel network extraction is becoming practically feasible, opening new possibilities for large-scale vascular analysis in stroke research.
Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation
Authors: Zhenhuan Zhou, Yuchen Zhang, Along He, Peng Wang, Xueshuo Xie, Tao Li
Date: 2025-04-16 00:00:00
Tasks: Diagnostic, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians' experience and subjective judgment. Deep learning has made significant advancements in the field of computer-aided diagnosis (CAD) because it can provide more objective and accurate diagnostic results. However, its application in RC treatment is still relatively rare, mainly due to the lack of public datasets in this field. To address this issue, in this paper, we established a First Molar Root Canal segmentation dataset called FMRC-2025. Additionally, to alleviate the workload of manual annotation for dentists and fully leverage the unlabeled data, we designed a Cross-Frequency Collaborative training semi-supervised learning (SSL) Network called CFC-Net. It consists of two components: (1) Cross-Frequency Collaborative Mean Teacher (CFC-MT), which introduces two specialized students (SS) and one comprehensive teacher (CT) for collaborative multi-frequency training. The CT and SS are trained on different frequency components while fully integrating multi-frequency knowledge through cross and full frequency consistency supervisions. (2) Uncertainty-guided Cross-Frequency Mix (UCF-Mix) mechanism enables the network to generate high-confidence pseudo-labels while learning to integrate multi-frequency information and maintaining the structural integrity of the targets. Extensive experiments on FMRC-2025 and three public dental datasets demonstrate that CFC-MT is effective for RC segmentation and can also exhibit strong generalizability on other dental segmentation tasks, outperforming state-of-the-art SSL medical image segmentation methods. Codes and dataset will be released.
ACE: Attentional Concept Erasure in Diffusion Models
Authors: Finn Carter
Date: 2025-04-16 00:00:00
Tasks: Image Generation, Specificity

Large text-to-image diffusion models have demonstrated remarkable image synthesis capabilities, but their indiscriminate training on Internet-scale data has led to learned concepts that enable harmful, copyrighted, or otherwise undesirable content generation. We address the task of concept erasure in diffusion models, i.e., removing a specified concept from a pre-trained model such that prompting the concept (or related synonyms) no longer yields its depiction, while preserving the model's ability to generate other content. We propose a novel method, Attentional Concept Erasure (ACE), that integrates a closed-form attention manipulation with lightweight fine-tuning. Theoretically, we formulate concept erasure as aligning the model's conditional distribution on the target concept with a neutral distribution. Our approach identifies and nullifies concept-specific latent directions in the cross-attention modules via a gated low-rank adaptation, followed by adversarially augmented fine-tuning to ensure thorough erasure of the concept and its synonyms. Empirically, we demonstrate on multiple benchmarks, including object classes, celebrity faces, explicit content, and artistic styles, that ACE achieves state-of-the-art concept removal efficacy and robustness. Compared to prior methods, ACE better balances generality (erasing concept and related terms) and specificity (preserving unrelated content), scales to dozens of concepts, and is efficient, requiring only a few seconds of adaptation per concept. We will release our code to facilitate safer deployment of diffusion models.
Boosting Multi-View Stereo with Depth Foundation Model in the Absence of Real-World Labels
Authors: Jie Zhu, Bo Peng, Zhe Zhang, Bingzheng Liu, Jianjun Lei
Date: 2025-04-16 00:00:00

Learning-based Multi-View Stereo (MVS) methods have made remarkable progress in recent years. However, how to effectively train the network without using real-world labels remains a challenging problem. In this paper, driven by the recent advancements of vision foundation models, a novel method termed DFM-MVS, is proposed to leverage the depth foundation model to generate the effective depth prior, so as to boost MVS in the absence of real-world labels. Specifically, a depth prior-based pseudo-supervised training mechanism is developed to simulate realistic stereo correspondences using the generated depth prior, thereby constructing effective supervision for the MVS network. Besides, a depth prior-guided error correction strategy is presented to leverage the depth prior as guidance to mitigate the error propagation problem inherent in the widely-used coarse-to-fine network structure. Experimental results on DTU and Tanks & Temples datasets demonstrate that the proposed DFM-MVS significantly outperforms existing MVS methods without using real-world labels.
A Visual RAG Pipeline for Few-Shot Fine-Grained Product Classification
Authors: Bianca Lamm, Janis Keuper
Date: 2025-04-16 00:00:00
Tasks: Product Recommendation, RAG, Retrieval-augmented Generation

Despite the rapid evolution of learning and computer vision algorithms, Fine-Grained Classification (FGC) still poses an open problem in many practically relevant applications. In the retail domain, for example, the identification of fast changing and visually highly similar products and their properties are key to automated price-monitoring and product recommendation. This paper presents a novel Visual RAG pipeline that combines the Retrieval Augmented Generation (RAG) approach and Vision Language Models (VLMs) for few-shot FGC. This Visual RAG pipeline extracts product and promotion data in advertisement leaflets from various retailers and simultaneously predicts fine-grained product ids along with price and discount information. Compared to previous approaches, the key characteristic of the Visual RAG pipeline is that it allows the prediction of novel products without re-training, simply by adding a few class samples to the RAG database. Comparing several VLM back-ends like GPT-4o [23], GPT-4o-mini [24], and Gemini 2.0 Flash [10], our approach achieves 86.8% accuracy on a diverse dataset.
Real-World Depth Recovery via Structure Uncertainty Modeling and Inaccurate GT Depth Fitting
Authors: Delong Suzhang, Meng Yang
Date: 2025-04-16 00:00:00
Tasks: Diversity

The low-quality structure in raw depth maps is prevalent in real-world RGB-D datasets, which makes real-world depth recovery a critical task in recent years. However, the lack of paired raw-ground truth (raw-GT) data in the real world poses challenges for generalized depth recovery. Existing methods insufficiently consider the diversity of structure misalignment in raw depth maps, which leads to poor generalization in real-world depth recovery. Notably, random structure misalignments are not limited to raw depth data but also affect GT depth in real-world datasets. In the proposed method, we tackle the generalization problem from both input and output perspectives. For input, we enrich the diversity of structure misalignment in raw depth maps by designing a new raw depth generation pipeline, which helps the network avoid overfitting to a specific condition. Furthermore, a structure uncertainty module is designed to explicitly identify the misaligned structure for input raw depth maps to better generalize in unseen scenarios. Notably the well-trained depth foundation model (DFM) can help the structure uncertainty module estimate the structure uncertainty better. For output, a robust feature alignment module is designed to precisely align with the accurate structure of RGB images avoiding the interference of inaccurate GT depth. Extensive experiments on multiple datasets demonstrate the proposed method achieves competitive accuracy and generalization capabilities across various challenging raw depth maps.
DART: Disease-aware Image-Text Alignment and Self-correcting Re-alignment for Trustworthy Radiology Report Generation
Authors: Sang-Jun Park, Keun-Soo Heo, Dong-Hee Shin, Young-Han Son, Ji-Hye Oh, Tae-Eui Kam
Date: 2025-04-16 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Image to text, Image-to-Text Retrieval, Retrieval, Text Retrieval

The automatic generation of radiology reports has emerged as a promising solution to reduce a time-consuming task and accurately capture critical disease-relevant findings in X-ray images. Previous approaches for radiology report generation have shown impressive performance. However, there remains significant potential to improve accuracy by ensuring that retrieved reports contain disease-relevant findings similar to those in the X-ray images and by refining generated reports. In this study, we propose a Disease-aware image-text Alignment and self-correcting Re-alignment for Trustworthy radiology report generation (DART) framework. In the first stage, we generate initial reports based on image-to-text retrieval with disease-matching, embedding both images and texts in a shared embedding space through contrastive learning. This approach ensures the retrieval of reports with similar disease-relevant findings that closely align with the input X-ray images. In the second stage, we further enhance the initial reports by introducing a self-correction module that re-aligns them with the X-ray images. Our proposed framework achieves state-of-the-art results on two widely used benchmarks, surpassing previous approaches in both report generation and clinical efficacy metrics, thereby enhancing the trustworthiness of radiology reports.
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
Authors: Guanchun Wang, Xiangrong Zhang, Yifei Zhang, Zelin Peng, Tianyang Zhang, Xu Tang, Licheng Jiao
Date: 2025-04-16 00:00:00
Tasks: Anomaly Detection, Mamba, Unsupervised Anomaly Detection

Unsupervised anomaly detection in hyperspectral images (HSI), aiming to detect unknown targets from backgrounds, is challenging for earth surface monitoring. However, current studies are hindered by steep computational costs due to the high-dimensional property of HSI and dense sampling-based training paradigm, constraining their rapid deployment. Our key observation is that, during training, not all samples within the same homogeneous area are indispensable, whereas ingenious sampling can provide a powerful substitute for reducing costs. Motivated by this, we propose an Asymmetrical Consensus State Space Model (ACMamba) to significantly reduce computational costs without compromising accuracy. Specifically, we design an asymmetrical anomaly detection paradigm that utilizes region-level instances as an efficient alternative to dense pixel-level samples. In this paradigm, a low-cost Mamba-based module is introduced to discover global contextual attributes of regions that are essential for HSI reconstruction. Additionally, we develop a consensus learning strategy from the optimization perspective to simultaneously facilitate background reconstruction and anomaly compression, further alleviating the negative impact of anomaly reconstruction. Theoretical analysis and extensive experiments across eight benchmarks verify the superiority of ACMamba, demonstrating a faster speed and stronger performance over the state-of-the-art.
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection
Authors: Qishun Wang, Zhengzheng Tu, Chenglong Li, Bo Jiang
Date: 2025-04-16 00:00:00
Tasks: Graph Learning, Graph Representation Learning, object-detection, Object Detection, Representation Learning, Video Object Detection

RGB-Thermal Video Object Detection (RGBT VOD) can address the limitation of traditional RGB-based VOD in challenging lighting conditions, making it more practical and effective in many applications. However, similar to most RGBT fusion tasks, it still mainly relies on manually aligned multimodal image pairs. In this paper, we propose a novel Multimodal Spatio-temporal Graph learning Network (MSGNet) for alignment-free RGBT VOD problem by leveraging the robust graph representation learning model. Specifically, we first design an Adaptive Partitioning Layer (APL) to estimate the corresponding regions of the Thermal image within the RGB image (high-resolution), achieving a preliminary inexact alignment. Then, we introduce the Spatial Sparse Graph Learning Module (S-SGLM) which employs a sparse information passing mechanism on the estimated inexact alignment to achieve reliable information interaction between different modalities. Moreover, to fully exploit the temporal cues for RGBT VOD problem, we introduce Hybrid Structured Temporal Modeling (HSTM), which involves a Temporal Sparse Graph Learning Module (T-SGLM) and Temporal Star Block (TSB). T-SGLM aims to filter out some redundant information between adjacent frames by employing the sparse aggregation mechanism on the temporal graph. Meanwhile, TSB is dedicated to achieving the complementary learning of local spatial relationships. Extensive comparative experiments conducted on both the aligned dataset VT-VOD50 and the unaligned dataset UVT-VOD2024 demonstrate the effectiveness and superiority of our proposed method. Our project will be made available on our website for free public access.