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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Marginalized denoising autoencoder via graph regularization for domain adaptation

Marginalized denoising autoencoder via graph regularization ...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MoE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Domain adaptation, which aims to learn domain-invariant features for sentiment classification, has received increasing attention. The underlying rationality of domain adaptation is that the involved domains share some common latent factors. Recently neural network based on Stacked Denoising Auto-Encoders (SDA) and its marginalized version (mSDA) have shown promising results on learning domain-invariant features. To explicitly preserve the intrinsic structure of data, this paper proposes a marginalized Denoising Autoencoders via graph Regularization (GmSDA) in which the autoencoder based framework can learn more robust features with the help of newly incorporated graph regularization. The learned representations are fed into the sentiment classifiers and experiments show that the GmSDA can effectively improve the classification accuracy when comparing with some state-of-the-art models on the cropped Amazon benchmark data set. © Springer-Verlag 2013.

关键词： Classification (of information)

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Structure preserving low-rank representation for semi-supervised face recognition

Structure preserving low-rank representation for semi-superv...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Suhang Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both lab.led and unlab.led samples as the low-rank representation (LRR) coefficients, has shown excellent performance in semi-supervised learning. In this paper, we additionally impose twofold constraints (local affinity and distant repulsion) on the LRR graph. The improved model, termed structure preserving LRR (SPLRR), can preserve the local geometrical structure but without distorting the distant repulsion property. Experiments are taken on three widely used face data sets to investigate the performance of SPLRR and the results show that it is superior to some state-of-the-art semi-supervised graphs. © Springer-Verlag 2013.

关键词： Face recognition

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An Asymmetric Modeling for Action Assessment 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Gao, Jibin Zheng, Wei-Shi Pan, Jia-Hui Gao, Chengying Wang, Yaowei Zeng, Wei Lai, Jianhuang School of Data and Computer Science Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen518005 China School of Electronics Engineering and Computer Science Peking University Beijing China Pazhou Lab Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (数字)9783030585778

ISBN: (纸本)9783030585761

Action assessment is a task of assessing the performance of an action. It is widely applicable to many real-world scenarios such as medical treatment and sporting events. However, existing methods for action assessment are mostly limited to individual actions, especially lacking modeling of the asymmetric relations among agents (e.g., between persons and objects);and this limitation undermines their ability to assess actions containingasymmetrically interactive motion patterns, since there always exists subordination between agents in many interactive actions. In this work, we model the asymmetric interactions among agents for action assessment. In particular, we propose an asymmetric interaction module (AIM), to explicitly model asymmetric interactions between intelligent agents within an action, where we group these agents into a primary one (e.g., human) and secondary ones (e.g., objects). We perform experiments on JIGSAWS dataset containing surgical actions, and additionally collect a new dataset, TASD-2, for interactive sporting actions. The experimental results on two interactive action datasets show the effectiveness of our model, and our method achieves state-of-the-art performance. The extended experiment on AQA-7 dataset also demonstrates the generalization capability of our framework to conventional action assessment. © 2020, Springer Nature Switzerland AG.

关键词： Intelligent agents

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Calibrating Multimodal Learning

arXiv

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arXiv 2023年

作者： Ma, Huan Zhang, Qingyang Zhang, Changqing Wu, Bingzhe Fu, Huazhu Zhou, Joey Tianyi Hu, Qinghua College of Intelligence and Computing Tianjin University Tianjin China AI Lab. Tencent Shenzhen China Tianjin Key Lab of Machine Learning Tianjin China Singapore Singapore

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness. © 2023, CC BY.

关键词： Learning systems

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SynopGround: A Large-Scale Dataset for Multi-Paragraph Video Grounding from TV Dramas and Synopses 24

SynopGround: A Large-Scale Dataset for Multi-Paragraph Video...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tan, Chaolei Lin, Zihang Pu, Junfu Qi, Zhongang Pei, Wei-Yi Qu, Zhi Wang, Yexin Shan, Ying Zheng, Wei-Shi Hu, Jian-Fang Sun Yat-sen University Guangzhou China ARC Lab Tencent PCG Shenzhen China Tencent Video PCG Shenzhen China Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Sun Yat-sen University Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Video grounding is a fundamental problem in multimodal content understanding, aiming to localize specific natural language queries in an untrimmed video. However, current video grounding datasets merely focus on simple events and are either limited to shorter videos or brief sentences, which hinders the model from evolving toward stronger multimodal understanding capabilities. To address these limitations, we present a large-scale video grounding dataset named SynopGround, in which more than 2800 hours of videos are sourced from popular TV dramas and are paired with accurately localized human-written synopses. Each paragraph in the synopsis serves as a language query and is manually annotated with precise temporal boundaries in the long video. These paragraph queries are tightly correlated to each other and contain a wealth of abstract expressions summarizing video storylines and specific descriptions portraying event details, which enables the model to learn multimodal perception on more intricate concepts over longer context dependencies. Based on the dataset, we further introduce a more complex setting of video grounding dubbed Multi-Paragraph Video Grounding (MPVG), which takes as input multiple paragraphs and a long video for grounding each paragraph query to its temporal interval. In addition, we propose a novel Local-Global Multimodal Reasoner (LGMR) to explicitly model the local-global structures of long-term multimodal inputs for MPVG. Our method provides an effective baseline solution to the multi-paragraph video grounding problem. Extensive experiments verify the proposed model's effectiveness as well as its superiority in long-term multi-paragraph video grounding over prior state-of-the-arts. Dataset and code are publicly availab.e. Project page: https://***/. © 2024 Owner/Author.

关键词： Large datasets

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AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection

AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domai...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yipeng Gao Kun-Yu Lin Junkai Yan Yaowei Wang Wei-Shi Zheng School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pengcheng Lab

In this work, we study few-shot domain adaptive object detection (FSDAOD), where only a few target lab.led images are availab.e for training in addition to sufficient source lab.led images. Critically, in FSDAOD, the data scarcity in the target domain leads to an extreme data imbalance between the source and target domains, which potentially causes over-adaptation in traditional feature alignment. To address the data imbalance problem, we propose an asymmetric adaptation paradigm, namely AsyFOD, which leverages the source and target instances from different perspectives. Specifically, by using target distribution estimation, the AsyFOD first identifies the target-similar source instances, which serves to augment the limited target instances. Then, we conduct asynchronous alignment between target-dissimilar source instances and augmented target instances, which is simple yet effective for alleviating the over-adaptation. Extensive experiments demonstrate that the proposed AsyFOD outperforms all state-of-the-art methods on four FSDAOD benchmarks with various environmental variances, e.g., 3.1% mAP improvement on Cityscapes-to-FoggyCityscapes and 2.9% mAP increase on Sim10k-to-Cityscapes. The code is availab.e at https://***/Hlings/AsyFPD.

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Set-based particle swarm optimization for mapping and scheduling tasks on heterogeneous embedded systems

Set-based particle swarm optimization for mapping and schedu...

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International Workshop on advanced Computational intelligence (IWACI)

作者： Xiao-Xiao Xu Xiao-Min Hu Wei-Neng Chen Yun Li Ministry of Education Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Guangzhou China Sun Yat-Sen University Guangzhou Guangdong CN School of Engineering University of Glasgow Glasgow U.K.

Modern heterogeneous multiprocessor embedded platforms is important for the high volume markets that have strict performance. However, it presents many challenges that need to be addressed in order to be efficiently utilized for multitask applications. Since mapping and scheduling problems for multi processors belong to the classic of NP-Complete problems, common methods used to solve this kind of problem usually fail. In this paper, we present an algorithm based on the meta-heuristic optimization technique, set-based discrete particle swarm optimization (S-PSO), which efficiently solves scheduling and mapping problems on the target platform. This algorithm can simultaneously addressed the mapping and scheduling problems on a complex and heterogeneous MPSoC and it has better performance than other algorithms in dealing with large scale problems. This algorithm also reduces the execution time of the application by exploring various solutions for mapping and scheduling of tasks and communications. We compare our approach with other heuristics, Ant Colony Optimization (ACO), on the performance to reach the optimum value and on the potential to explore the target platform. The results show that our approach performs better than other heuristics.

关键词： Scheduling Optimization Processor scheduling Algorithm design and analysis Program processors Particle swarm optimization Data transfer

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Understanding, Predicting and Scheduling Serverless Workloads under Partial Interference

Understanding, Predicting and Scheduling Serverless Workload...

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Supercomputing Conference

作者： Laiping Zhao Yanan Yang Yiming Li Xian Zhou Keqiu Li State Key Laboratory of Communication Content Cognition Colleage of Intelligence & Computing(CIC) Tianjin University Tianjin Key Lab. of Advanced Networking (TANKLAB) CIC Tianjin University

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Interference among distributed cloud applications can be classified into three types: full, partial and zero. While prior research merely focused on full interference, the partial interference that occurs at parts of applications is far more common yet still lacks in-depth study. Serverless computing that structures applications into small-sized, short-lived functions further exacerbate partial interference. We characterize the features of partial interference in serverless as exhibiting high volatility, spatial-temporal variation, and propagation. Given these observations, we propose an incremental learning predictor, named Gsight, which can achieve high precision by harnessing the spatial-temporal overlap codes and profiles of functions via an end-to-end call path. Experimental results show that Gsight can achieve an average error of 1.71%. Its convergence speed is at least 3x faster than that in a serverful system. A scheduling case study shows that the proposed method can improve function density by ≥18.79% while guaranteeing the quality of service (QoS).

关键词： Codes Processor scheduling Computational modeling High performance computing Serverless computing Interference Quality of service

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Tournament selection based artificial bee colony algorithm with elitist strategy

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial intelligence and Lecture Notes in Bioinformatics) 2014年 8916卷 387-396页

作者： Zhang, Meng-Dan Zhan, Zhi-Hui Li, Jing-Jing Zhang, Jun Department of Computer Science Sun Yat-sen University 510275 China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China Key Lab. Software Technology Education Department of Guangdong Province China School of Computer Science South China Normal University China

Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure. © Springer International Publishing Switzerland 2014.

关键词： Optimization

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