pwc-archive/papers-with-abstracts huggingface dataset:
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data
Authors: Jiajie Li, Brian R Quaranto, Chenhui Xu, Ishan Mishra, Ruiyang Qin, Dancheng Liu, Peter C W Kim, JinJun Xiong
Date: 2025-01-25 00:00:00
Tasks: Action Recognition, Open Set Learning, TAG, Weakly-supervised Learning

We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and object classes, in both surgical images and videos. RASO leverages a novel weakly-supervised learning framework that generates tag-image-text pairs automatically from large-scale unannotated surgical lecture videos, significantly reducing the need for manual annotations. Our scalable data generation pipeline gathers 2,200 surgical procedures and produces 3.6 million tag annotations across 2,066 unique surgical tags. Our experiments show that RASO achieves improvements of 2.9 mAP, 4.5 mAP, 10.6 mAP, and 7.2 mAP on four standard surgical benchmarks, respectively, in zero-shot settings, and surpasses state-of-the-art models in supervised surgical action recognition tasks. Code, model, and demo are available at https://ntlm1686.github.io/raso.
Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images
Authors: Saikat Roy, Mahmoud Mostapha, Radu Miron, Matt Holbrook, Mariappan Nadar
Date: 2025-01-25 00:00:00
Tasks: Super-Resolution

Plug-and-play approaches to solving inverse problems such as restoration and super-resolution have recently benefited from Diffusion-based generative priors for natural as well as medical images. However, solutions often use the standard albeit computationally intensive route of training and inferring with the whole image on the diffusion prior. While patch-based approaches to evaluating diffusion priors in plug-and-play methods have received some interest, they remain an open area of study. In this work, we explore the feasibility of the usage of patches for training and inference of a diffusion prior on MRI images. We explore the minor adaptation necessary for artifact avoidance, the performance and the efficiency of memory usage of patch-based methods as well as the adaptability of whole image training to patch-based evaluation - evaluating across multiple plug-and-play methods, tasks and datasets.
Efficient Point Clouds Upsampling via Flow Matching
Authors: Zhi-Song Liu, Chenhang He, Lei LI
Date: 2025-01-25 00:00:00
Tasks: point cloud upsampling

Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds, overlooking the geometric information inherent in sparse point clouds. To address these inefficiencies, we propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method first employs midpoint interpolation to sparse point clouds, resolving the density mismatch between sparse and dense point clouds. Since point clouds are unordered representations, we introduce a pre-alignment method based on Earth Mover's Distance (EMD) optimization to ensure coherent interpolation between sparse and dense point clouds, which enables a more stable learning path in flow matching. Experiments on synthetic datasets demonstrate that our method delivers superior upsampling quality but with fewer sampling steps. Further experiments on ScanNet and KITTI also show that our approach generalizes well on RGB-D point clouds and LiDAR point clouds, making it more practical for real-world applications.
Mirage in the Eyes: Hallucination Attack on Multi-modal Large Language Models with Only Attention Sink
Authors: Yining Wang, Mi Zhang, Junjie Sun, Chenyue Wang, Min Yang, Hui Xue, Jialing Tao, Ranjie Duan, Jiexi Liu
Date: 2025-01-25 00:00:00
Tasks: Hallucination, Text Generation

Fusing visual understanding into language generation, Multi-modal Large Language Models (MLLMs) are revolutionizing visual-language applications. Yet, these models are often plagued by the hallucination problem, which involves generating inaccurate objects, attributes, and relationships that do not match the visual content. In this work, we delve into the internal attention mechanisms of MLLMs to reveal the underlying causes of hallucination, exposing the inherent vulnerabilities in the instruction-tuning process. We propose a novel hallucination attack against MLLMs that exploits attention sink behaviors to trigger hallucinated content with minimal image-text relevance, posing a significant threat to critical downstream applications. Distinguished from previous adversarial methods that rely on fixed patterns, our approach generates dynamic, effective, and highly transferable visual adversarial inputs, without sacrificing the quality of model responses. Comprehensive experiments on 6 prominent MLLMs demonstrate the efficacy of our attack in compromising black-box MLLMs even with extensive mitigating mechanisms, as well as the promising results against cutting-edge commercial APIs, such as GPT-4o and Gemini 1.5. Our code is available at https://huggingface.co/RachelHGF/Mirage-in-the-Eyes.
Explainable YOLO-Based Dyslexia Detection in Synthetic Handwriting Data
Authors: Nora Fink
Date: 2025-01-25 00:00:00
Tasks: object-detection, Object Detection, Transfer Learning

Dyslexia affects reading and writing skills across many languages. This work describes a new application of YOLO-based object detection to isolate and label handwriting patterns (Normal, Reversal, Corrected) within synthetic images that resemble real words. Individual letters are first collected, preprocessed into 32x32 samples, then assembled into larger synthetic 'words' to simulate realistic handwriting. Our YOLOv11 framework simultaneously localizes each letter and classifies it into one of three categories, reflecting key dyslexia traits. Empirically, we achieve near-perfect performance, with precision, recall, and F1 metrics typically exceeding 0.999. This surpasses earlier single-letter approaches that rely on conventional CNNs or transfer-learning classifiers (for example, MobileNet-based methods in Robaa et al. arXiv:2410.19821). Unlike simpler pipelines that consider each letter in isolation, our solution processes complete word images, resulting in more authentic representations of handwriting. Although relying on synthetic data raises concerns about domain gaps, these experiments highlight the promise of YOLO-based detection for faster and more interpretable dyslexia screening. Future work will expand to real-world handwriting, other languages, and deeper explainability methods to build confidence among educators, clinicians, and families.
TflosYOLO+TFSC: An Accurate and Robust Model for Estimating Flower Count and Flowering Period
Authors: Qianxi Mi, Pengcheng Yuan, Chunlei Ma, Jiedan Chen, Mingzhe Yao
Date: 2025-01-25 00:00:00

Tea flowers play a crucial role in taxonomic research and hybrid breeding for the tea plant. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, we propose TflosYOLO and TFSC model for tea flowering quantifying, which enable estimation of flower count and flowering period. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions in 2 years. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, which is the first model to offer a viable solution for detecting and counting tea flowers. The TflosYOLO model achieved an mAP50 of 0.874, outperforming YOLOv5, YOLOv7 and YOLOv8. Furthermore, TflosYOLO model was tested on 34 datasets encompassing 26 tea accessions, five flowering stages, various lighting conditions, and pruned / unpruned plants, demonstrating high generalization and robustness. The correlation coefficient (R^2) between the predicted and actual flower counts was 0.974. Additionally, the TFSC (Tea Flowering Stage Classification) model, a 7-layer neural network was designed for automatic classification of the flowering period. TFSC model was evaluated on 2 years and achieved an accuracy of 0.738 and 0.899 respectively. Using the TflosYOLO+TFSC model, we monitored the tea flowering dynamics and tracked the changes in flowering stages across various tea accessions. The framework provides crucial support for tea plant breeding programs and phenotypic analysis of germplasm resources.
Pre-trained Model Guided Mixture Knowledge Distillation for Adversarial Federated Learning
Authors: Yu Qiao, Huy Q. Le, Apurba Adhikary, Choong Seon Hong
Date: 2025-01-25 00:00:00
Tasks: Adversarial Robustness, Federated Learning, image-classification, Image Classification, Knowledge Distillation

This paper aims to improve the robustness of a small global model while maintaining clean accuracy under adversarial attacks and non-IID challenges in federated learning. By leveraging the concise knowledge embedded in the class probabilities from a pre-trained model for both clean and adversarial image classification, we propose a Pre-trained Model-guided Adversarial Federated Learning (PM-AFL) training paradigm. This paradigm integrates vanilla mixture and adversarial mixture knowledge distillation to effectively balance accuracy and robustness while promoting local models to learn from diverse data. Specifically, for clean accuracy, we adopt a dual distillation strategy where the class probabilities of randomly paired images and their blended versions are aligned between the teacher model and the local models. For adversarial robustness, we use a similar distillation approach but replace clean samples on the local side with adversarial examples. Moreover, considering the bias between local and global models, we also incorporate a consistency regularization term to ensure that local adversarial predictions stay aligned with their corresponding global clean ones. These strategies collectively enable local models to absorb diverse knowledge from the teacher model while maintaining close alignment with the global model, thereby mitigating overfitting to local optima and enhancing the generalization of the global model. Experiments demonstrate that the PM-AFL-based paradigm outperforms other methods that integrate defense strategies by a notable margin.
Enhancing Fetal Plane Classification Accuracy with Data Augmentation Using Diffusion Models
Authors: Yueying Tian, Elif Ucurum, Xudong Han, Rupert Young, Chris Chatwin, Philip Birch
Date: 2025-01-25 00:00:00
Tasks: Data Augmentation, Medical Diagnosis

Ultrasound imaging is widely used in medical diagnosis, especially for fetal health assessment. However, the availability of high-quality annotated ultrasound images is limited, which restricts the training of machine learning models. In this paper, we investigate the use of diffusion models to generate synthetic ultrasound images to improve the performance on fetal plane classification. We train different classifiers first on synthetic images and then fine-tune them with real images. Extensive experimental results demonstrate that incorporating generated images into training pipelines leads to better classification accuracy than training with real images alone. The findings suggest that generating synthetic data using diffusion models can be a valuable tool in overcoming the challenges of data scarcity in ultrasound medical imaging.
Large-Scale Riemannian Meta-Optimization via Subspace Adaptation
Authors: Peilin Yu, Yuwei Wu, Zhi Gao, Xiaomeng Fan, Yunde Jia
Date: 2025-01-25 00:00:00

Riemannian meta-optimization provides a promising approach to solving non-linear constrained optimization problems, which trains neural networks as optimizers to perform optimization on Riemannian manifolds. However, existing Riemannian meta-optimization methods take up huge memory footprints in large-scale optimization settings, as the learned optimizer can only adapt gradients of a fixed size and thus cannot be shared across different Riemannian parameters. In this paper, we propose an efficient Riemannian meta-optimization method that significantly reduces the memory burden for large-scale optimization via a subspace adaptation scheme. Our method trains neural networks to individually adapt the row and column subspaces of Riemannian gradients, instead of directly adapting the full gradient matrices in existing Riemannian meta-optimization methods. In this case, our learned optimizer can be shared across Riemannian parameters with different sizes. Our method reduces the model memory consumption by six orders of magnitude when optimizing an orthogonal mainstream deep neural network (e.g., ResNet50). Experiments on multiple Riemannian tasks show that our method can not only reduce the memory consumption but also improve the performance of Riemannian meta-optimization.
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection
Authors: Siqi Wang, Yuanze Hu, Xinwang Liu, Siwei Wang, Guangpu Wang, Chuanfu Xu, Jie Liu, Ping Chen
Date: 2025-01-25 00:00:00
Tasks: Anomaly Detection, Image Generation

Industrial image anomaly detection (IAD) is a pivotal topic with huge value. Due to anomaly's nature, real anomalies in a specific modern industrial domain (i.e. domain-specific anomalies) are usually too rare to collect, which severely hinders IAD. Thus, zero-shot anomaly synthesis (ZSAS), which synthesizes pseudo anomaly images without any domain-specific anomaly, emerges as a vital technique for IAD. However, existing solutions are either unable to synthesize authentic pseudo anomalies, or require cumbersome training. Thus, we focus on ZSAS and propose a brand-new paradigm that can realize both authentic and training-free ZSAS. It is based on a chronically-ignored fact: Although domain-specific anomalies are rare, real anomalies from other domains (i.e. cross-domain anomalies) are actually abundant and directly applicable to ZSAS. Specifically, our new ZSAS paradigm makes three-fold contributions: First, we propose a novel method named Cross-domain Anomaly Injection (CAI), which directly exploits cross-domain anomalies to enable highly authentic ZSAS in a training-free manner. Second, to supply CAI with sufficient cross-domain anomalies, we build the first domain-agnostic anomaly dataset within our best knowledge, which provides ZSAS with abundant real anomaly patterns. Third, we propose a CAI-guided Diffusion Mechanism, which further breaks the quantity limit of real anomalies and enable unlimited anomaly synthesis. Our head-to-head comparison with existing ZSAS solutions justifies our paradigm's superior performance for IAD and demonstrates it as an effective and pragmatic ZSAS solution.
A Training-free Synthetic Data Selection Method for Semantic Segmentation
Authors: Hao Tang, Siyue Yu, Jian Pang, Bingfeng Zhang
Date: 2025-01-25 00:00:00
Tasks: Semantic Segmentation

Training semantic segmenter with synthetic data has been attracting great attention due to its easy accessibility and huge quantities. Most previous methods focused on producing large-scale synthetic image-annotation samples and then training the segmenter with all of them. However, such a solution remains a main challenge in that the poor-quality samples are unavoidable, and using them to train the model will damage the training process. In this paper, we propose a training-free Synthetic Data Selection (SDS) strategy with CLIP to select high-quality samples for building a reliable synthetic dataset. Specifically, given massive synthetic image-annotation pairs, we first design a Perturbation-based CLIP Similarity (PCS) to measure the reliability of synthetic image, thus removing samples with low-quality images. Then we propose a class-balance Annotation Similarity Filter (ASF) by comparing the synthetic annotation with the response of CLIP to remove the samples related to low-quality annotations. The experimental results show that using our method significantly reduces the data size by half, while the trained segmenter achieves higher performance. The code is released at https://github.com/tanghao2000/SDS.
Uni-Sign: Toward Unified Sign Language Understanding at Scale
Authors: Zecheng Li, Wengang Zhou, Weichao Zhao, Kepeng Wu, Hezhen Hu, Houqiang Li
Date: 2025-01-25 00:00:00
Tasks: Computational Efficiency, Gloss-free Sign Language Translation, Sign Language Recognition, Sign Language Translation, Transfer Learning

Sign language pre-training has gained increasing attention for its ability to enhance performance across various sign language understanding (SLU) tasks. However, existing methods often suffer from a gap between pre-training and fine-tuning, leading to suboptimal results. To address this, we propose Uni-Sign, a unified pre-training framework that eliminates the gap between pre-training and downstream SLU tasks through a large-scale generative pre-training strategy and a novel fine-tuning paradigm. First, we introduce CSL-News, a large-scale Chinese Sign Language (CSL) dataset containing 1,985 hours of video paired with textual annotations, which enables effective large-scale pre-training. Second, Uni-Sign unifies SLU tasks by treating downstream tasks as a single sign language translation (SLT) task during fine-tuning, ensuring seamless knowledge transfer between pre-training and fine-tuning. Furthermore, we incorporate a prior-guided fusion (PGF) module and a score-aware sampling strategy to efficiently fuse pose and RGB information, addressing keypoint inaccuracies and improving computational efficiency. Extensive experiments across multiple SLU benchmarks demonstrate that Uni-Sign achieves state-of-the-art performance across multiple downstream SLU tasks. Dataset and code are available at github.com/ZechengLi19/Uni-Sign.
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy
Authors: Yangfan He, Jianhui Wang, Yijin Wang, Kun Li, Yan Zhong, Xinyuan Song, Li Sun, Jingyuan Lu, Miao Zhang, Tianyu Shi, Xinhang Yuan, Kuan Lu, Menghao Huo, Keqin Li, Jiaqi Chen
Date: 2025-01-25 00:00:00
Tasks: Image Generation, Language Modeling, Language Modelling, RAG

Today's image generation systems are capable of producing realistic and high-quality images. However, user prompts often contain ambiguities, making it difficult for these systems to interpret users' actual intentions. Consequently, many users must modify their prompts several times to ensure the generated images meet their expectations. While some methods focus on enhancing prompts to make the generated images fit user needs, the model is still hard to understand users' real needs, especially for non-expert users. In this research, we aim to enhance the visual parameter-tuning process, making the model user-friendly for individuals without specialized knowledge and better understand user needs. We propose a human-machine co-adaption strategy using mutual information between the user's prompts and the pictures under modification as the optimizing target to make the system better adapt to user needs. We find that an improved model can reduce the necessity for multiple rounds of adjustments. We also collect multi-round dialogue datasets with prompts and images pairs and user intent. Various experiments demonstrate the effectiveness of the proposed method in our proposed dataset. Our annotation tools and several examples of our dataset are available at https://zenodo.org/records/14876029 for easier review. We will make open source our full dataset and code.
SpikSSD: Better Extraction and Fusion for Object Detection with Spiking Neuron Networks
Authors: Yimeng Fan, Changsong Liu, Mingyang Li, Wei zhang
Date: 2025-01-25 00:00:00
Tasks: object-detection, Object Detection

As the third generation of neural networks, Spiking Neural Networks (SNNs) have gained widespread attention due to their low energy consumption and biological interpretability. Recently, SNNs have made considerable advancements in computer vision. However, efficiently conducting feature extraction and fusion under the spiking characteristics of SNNs for object detection remains a pressing challenge. To address this problem, we propose the SpikSSD, a novel Spiking Single Shot Multibox Detector. Specifically, we design a full-spiking backbone network, MDS-ResNet, which effectively adjusts the membrane synaptic input distribution at each layer, achieving better spiking feature extraction. Additionally, for spiking feature fusion, we introduce the Spiking Bi-direction Fusion Module (SBFM), which for the first time realizes bi-direction fusion of spiking features, enhancing the multi-scale detection capability of the model. Experimental results show that SpikSSD achieves 40.8% mAP on the GEN1 dataset, 76.3% and 52.4% mAP@0.5 on VOC 2007 and COCO 2017 datasets respectively with the lowest firing rate, outperforming existing SNN-based approaches at ultralow energy consumption. This work sets a new benchmark for future research in SNN-based object detection. Our code is publicly available in https://github.com/yimeng-fan/SpikSSD.
Exploring Primitive Visual Measurement Understanding and the Role of Output Format in Learning in Vision-Language Models
Authors: Ankit Yadav, Lingqiao Liu, Yuankai Qi
Date: 2025-01-25 00:00:00
Tasks: Attribute, Sentence

This work investigates the capabilities of current vision-language models (VLMs) in visual understanding and attribute measurement of primitive shapes using a benchmark focused on controlled 2D shape configurations with variations in spatial positioning, occlusion, rotation, size, and shape attributes such as type, quadrant, center-coordinates, rotation, occlusion status, and color as shown in Figure 1 and supplementary Figures S3-S81. We fine-tune state-of-the-art VLMs (2B-8B parameters) using Low-Rank Adaptation (LoRA) and validate them on multiple out-of-domain (OD) scenarios from our proposed benchmark. Our findings reveal that coherent sentence-based outputs outperform tuple formats, particularly in OD scenarios with large domain gaps. Additionally, we demonstrate that scaling numeric tokens during loss computation enhances numerical approximation capabilities, further improving performance on spatial and measurement tasks. These results highlight the importance of output format design, loss scaling strategies, and robust generalization techniques in enhancing the training and fine-tuning of VLMs, particularly for tasks requiring precise spatial approximations and strong OD generalization.
TranStable: Towards Robust Pixel-level Online Video Stabilization by Jointing Transformer and CNN
Authors: zhizhen li, tianyi zhuo, Yifei Cao, Jizhe Yu, Yu Liu
Date: 2025-01-25 00:00:00
Tasks: Video Stabilization

Video stabilization often struggles with distortion and excessive cropping. This paper proposes a novel end-to-end framework, named TranStable, to address these challenges, comprising a genera tor and a discriminator. We establish TransformerUNet (TUNet) as the generator to utilize the Hierarchical Adaptive Fusion Module (HAFM), integrating Transformer and CNN to leverage both global and local features across multiple visual cues. By modeling frame-wise relationships, it generates robust pixel-level warping maps for stable geometric transformations. Furthermore, we design the Stability Discriminator Module (SDM), which provides pixel-wise supervision for authenticity and consistency in training period, ensuring more complete field-of-view while minimizing jitter artifacts and enhancing visual fidelity. Extensive experiments on NUS, DeepStab, and Selfie benchmarks demonstrate state-of-the-art performance.