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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

Deep Video Representation Learning: A Survey

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arXiv 2024年

作者： Ravanbakhsh, Elham Liang, Yongqing Ramanujam, J. Li, Xin Division of Electrical & Computer Engineering Center for Computation & Technology Louisiana State University Baton RougeLA70803 United States Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States Section of Visual Computing and Interactive Media Texas A&M University College StationTX77843 United States

This paper provides a review on representation learning for videos. We classify recent spatio-temporal feature learning methods for sequential visual data and compare their pros and cons for general video analysis. Building effective features for videos is a fundamental problem in computer vision tasks involving video analysis and understanding. Existing features can be generally categorized into spatial and temporal features. Their effectiveness under variations of illumination, occlusion, view and background are discussed. Finally, we discuss the remaining challenges in existing deep video representation learning studies. © 2024, CC BY-NC-ND.

关键词： Deep learning

AlphaChimp: Tracking and Behavior Recognition of Chimpanzees

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arXiv 2024年

作者： Ma, Xiaoxuan Lin, Yutang Xu, Yuan Kaufhold, Stephan P. Terwilliger, Jack Meza, Andres Zhu, Yixin Rossano, Federico Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Yuanpei College Peking University China Department of Cognitive Science University of California San Diego United States Institute for Artificial Intelligence Peking University China Nat'l Eng. Research Center of Visual Technology Nat'l Key Lab of General Artificial Intelligence Peking University China

Understanding non-human primate behavior is crucial for improving animal welfare, modeling social behavior, and gaining insights into both distinctly human and shared behaviors. Despite recent advances in computer vision, automated analysis of primate behavior remains challenging due to the complexity of their social interactions and the lack of specialized algorithms. Existing methods often struggle with the nuanced behaviors and frequent occlusions characteristic of primate social dynamics. This study aims to develop an effective method for automated detection, tracking, and recognition of chimpanzee behaviors in video footage. Here we show that our proposed method, AlphaChimp, an end-to-end approach that simultaneously detects chimpanzee positions and estimates behavior categories from videos, significantly outperforms existing methods in behavior recognition. AlphaChimp achieves approximately 10% higher tracking accuracy and a 20% improvement in behavior recognition compared to state-of-the-art methods, particularly excelling in the recognition of social behaviors. This superior performance stems from AlphaChimp's innovative architecture, which integrates temporal feature fusion with a Transformer-based self-attention mechanism, enabling more effective capture and interpretation of complex social interactions among chimpanzees. Our approach bridges the gap between computer vision and primatology, enhancing technical capabilities and deepening our understanding of primate communication and sociality. We release our code and models at project page and hope this will facilitate future research in animal social dynamics. This work contributes to ethology, cognitive science, and artificial intelligence, offering new perspectives on social intelligence. Copyright © 2024, The Authors. All rights reserved.

关键词： Mammals

Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation

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arXiv 2025年

作者： Zheng, Hong Chen, Yucheng Mu, Nan Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Department of Cardiology West China Hospital Sichuan University Chengdu610041 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610066 China Sichuan 2011 Collaborative Innovation Center for Educational Big Data Chengdu610066 China

Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based segmentation methods have recently garnered significant attention due to their impressive performance. However, these segmentation methods are typically good at partitioning regularly shaped organs, such as the left ventricle (LV) and the myocardium (MYO), whereas they perform poorly on irregularly shaped organs, such as the right ventricle (RV). In this study, we argue that this limitation of segmentation models stems from their insufficient generalization ability to address the distribution shift of segmentation targets across slices, cardiac phases, and disease conditions. To overcome this issue, we present a Multi-Disease-Aware Training Strategy (MTS) and restructure the introduced CMRI datasets into multi-disease datasets. Additionally, we propose a specialized data processing technique for preprocessing input images to support the MTS. To validate the effectiveness of our method, we performed control group experiments and cross-validation tests. The experimental results show that (1) network models trained using our proposed strategy achieved superior segmentation performance, particularly in RV segmentation, and (2) these networks exhibited robust performance even when applied to data from unknown diseases. Copyright © 2025, The Authors. All rights reserved.

关键词： Image segmentation

STRPM: A Spatiotemporal Residual Predictive Model for High-Resolution Video Prediction

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arXiv 2022年

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Gao, Wen School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Although many video prediction methods have obtained good performance in low-resolution (64∼128) videos, predictive models for high-resolution (512∼4K) videos have not been fully explored yet, which are more meaningful due to the increasing demand for high-quality videos. Compared with low-resolution videos, high-resolution videos contain richer appearance (spatial) information and more complex motion (temporal) information. In this paper, we propose a Spatiotemporal Residual Predictive Model (STRPM) for high-resolution video prediction. On the one hand, we propose a Spatiotemporal Encoding-Decoding Scheme to preserve more spatiotemporal information for high-resolution videos. In this way, the appearance details for each frame can be greatly preserved. On the other hand, we design a Residual Predictive Memory (RPM) which focuses on modeling the spatiotemporal residual features (STRF) between previous and future frames instead of the whole frame, which can greatly help capture the complex motion information in high-resolution videos. In addition, the proposed RPM can supervise the spatial encoder and temporal encoder to extract different features in the spatial domain and the temporal domain, respectively. Moreover, the proposed model is trained using generative adversarial networks (GANs) with a learned perceptual loss (LP-loss) to improve the perceptual quality of the predictions. Experimental results show that STRPM can generate more satisfactory results compared with various existing methods. © 2022, CC BY.

关键词： Signal encoding

Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy

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arXiv 2024年

作者： Guan, Zhenyu Kong, Xiangyu Zhong, Fangwei Wang, Yizhou Institute for Artificial Intelligence Peking University Beijing China Computer School Beijing Information Science & Technology University Beijing China School of Artificial Intelligence Beijing Normal University Beijing China Center on Frontiers of Computing Studies School of Computer Science Nat’l Eng. Research Center of Visual Technology State Key Lab of General Artificial Intelligence Peking University Beijing China State Key Laboratory of General Artificial Intelligence BIGAI Beijing China

Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and large action spaces in multi-agent tasks. However, diplomacy involves a staggering magnitude of decision spaces, especially considering the negotiation stage required. While recent agents based on large language models (LLMs) have shown potential in various applications, they still struggle with extended planning periods in complex multi-agent settings. Leveraging recent technologies for LLM-based agents, we aim to explore AI’s potential to create a human-like agent capable of executing comprehensive multi-agent missions by integrating three fundamental capabilities: 1) strategic planning with memory and reflection;2) goal-oriented negotiation with social reasoning;and 3) augmenting memory through self-play games for self-evolution without human in the loop. Copyright © 2024, The Authors. All rights reserved.

关键词： Strategic planning

Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

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Bi-directional Feature Fusion Generative Adversarial Network...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kexin Sun Zhineng Chen Gongwei Wang Jun Liu Xiongjun Ye Yu-Gang Jiang Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science & Shanghai Fudan University Shanghai Qi Zhi Institute Peking University People's Hospital Department of Urology National Cancer Center & National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

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Real-time Holistic Robot Pose Estimation with Unknown States

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothes...

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yuan Xu Xiaoxuan Ma Jiajun Su Wentao Zhu Yu Qiao Yizhou Wang Center on Frontiers of Computing Studies School of Computer Science Peking University International Digital Economy Academy (IDEA) School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Inst. for Artificial Intelligence Peking University Nat'1 Eng. Research Center of Visual Technology Nat'1 Key Lab of General Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obtaining high-quality estimates from them. To address this limitation, we introduce ScoreHypo, a versatile framework by first leveraging our novel HypoNet to generate multiple hy-potheses, followed by employing a meticulously designed scorer, ScoreNet, to evaluate and select high-quality esti-mates. ScoreHypo formulates the estimation process as a re-verse denoising process, where HypoNet produces a diverse set of plausible estimates that effectively align with the im-age cues. Subsequently, ScoreNet is employed to rigorously evaluate and rank these estimates based on their quality and finally identify superior ones. Experimental results demon-strate that HypoNet outperforms existing state-of-the-art probabilistic methods as a multi-hypothesis mesh estimator. Moreover, the estimates selected by ScoreNet significantly outperform random generation or simple averaging. Notably, the trained ScoreNet exhibits generalizability, as it can effectively score existing methods and significantly reduce their errors by more than 15%. Code and models are available at ht tps: / /xy02- 05. gi thub. io/ScoreHypo.

关键词： computer vision Three-dimensional displays Accuracy Codes Noise reduction Estimation Benchmark testing