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Overview of the Tenth Dialog System Technology Challenge: DSTC10

IEEE/ACM Transactions on Audio speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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Decomposed Neural Architecture Search for image denoising

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Applied Soft Computing 2022年 124卷

作者： Li, Di Bai, Yunpeng Bai, Zongwen Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northwestern Polytechnical University Xi'an710129 China Department of Computer Science Faculty of Business and Physical Sciences Aberystwyth University AberystwythSY233DB United Kingdom Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data School of Physics and Electronic Information Yan'an University Yan'an716000 China

In practical applications of deep learning, as the demand for the modeling capability increases, the network size may need to be massively enlarged in response. This may form a significant challenge in practice, especially when facing the dilemma of limited computational resources, making model compression indispensable. It can be time-consuming and interminable to obtain an appropriate network architecture through manual compression. In this paper, we propose an automated method for searching decomposed network architectures, named DNAS (standing for Decomposed Neural Architecture Search). It integrates both tasks of neural architecture search and tensor decomposition based model compression within a unified framework. The method is able to efficiently find a compact network with high performance for image denoising, with respect to memory and runtime. Particularly, using one single V100 GPU, it only takes about 1.5 h to obtain a denoising network on the BSD500 dataset. Experimental results demonstrate that compared with models developed using existing methods, DNAS consumes significantly less inference time and employs much fewer trainable parameters, outperforming existing approaches on both synthetic and real-world denoising datasets. © 2022 Elsevier B.V.

关键词： Network architecture

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Maximal η-clique maintenance over uncertain graph streams

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Information sciences 2025年 717卷

作者： Ma, Ziyi Wang, Liqing Yang, Jianye Zhou, Xu Li, Kenli Gao, Cuiyun School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Chinacoal Beijing Coal Mining Machinery Co. Ltd. Beijing102400 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou543002 China Department of New Networks PengCheng Laboratory Shenzhen518055 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Shenzhen518055 China

Maximal clique enumeration is a critical task for analyzing graph data and has a wide range of applications, such as community detection, protein complex identification, and group recommendation. Although many efficient algorithms have been proposed, most of them are designed for static or deterministic graphs. However, in many real-life applications, graphs are often highly dynamic and nondeterministic. In this paper, we study the problem of maximal η-clique maintenance (shorted as MCM) over uncertain graph streams. Given an uncertain graph G and a probability threshold η, MCM reports all incremental η-cliques in G for each update edge of G. To the best of our knowledge, we are the first to systematically study this problem. We first develop a local pivot-based enumeration technique that utilizes the pivot priority of candidates to achieve effective pruning during the maximal η-clique enumeration procedure. Based on the technique, we propose a competitive baseline approach LPMCM, which finds newly generated maximal η-cliques and internal maximal η-cliques. Although LPMCM avoids repeated enumeration of the same maximal η-cliques, it still requires expensive costs in terms of η-clique maximality determination. To further enhance maintenance efficiency, we introduce an approach HLPMCM using the hash-based maximality determination technique and an improved internal maximal η-clique enumeration technique. Extensive experiments show that HLPMCM can achieve 1.1x-19.0x speedup over the competitors and consumes slightly more memory. © 2025 Elsevier Inc.

关键词： Maintenance

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PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images

arXiv

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arXiv 2024年

作者： Bai, Jieyun Zhou, Zihao Ou, Zhanhong Koehler, Gregor Stock, Raphael Maier-Hein, Klaus Elbatel, Marawan Martí, Robert Li, Xiaomeng Qiu, Yaoyang Gou, Panjie Chen, Gongping Zhao, Lei Zhang, Jianxun Dai, Yu Wang, Fangyijie Silvestre, Guénolé Curran, Kathleen Sun, Hongkun Xu, Jing Cai, Pengzhou Jiang, Lu Lan, Libin Ni, Dong Zhong, Mei Chen, Gaowen Campello, Víctor M. Lu, Yaosheng Lekadir, Karim Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization Jinan University Guangzhou China Auckland Bioengineering Institute The University of Auckland Auckland New Zealand Heidelberg Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Computer Vision and Robotics Group University of Girona Girona Spain Co. LTD Beijing China College of Artificial Intelligence Nankai University Tianjin China College of Computer Science and Electronic Engineering Hunan University Changsha China School of Medicine University College Dublin Dublin Ireland School of Computer Science University College Dublin Dublin Ireland School of Statistics & Mathematics Zhejiang Gongshang University Hangzhou China School of Computer Science & Engineering Chongqing University of Technology Chongqing China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China NanFang Hospital of Southern Medical University Guangzhou China Zhujiang Hospital of Southern Medical University Guangzhou China Departament de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain Institute The University of Auckland Private Bag 92019 Auckland1142 New Zealand

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound imaging as advocated by the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) for monitoring labor progression, is a crucial first step for quantitative diagnosis and clinical decision-making. This requires specialized analysis by obstetrics professionals, in a task that i) is highly time- and cost-consuming and ii) often yields inconsistent results. The utility of automatic segmentation algorithms for biometry has been proven, though existing results remain suboptimal. To push forward advancements in this area, the Grand Challenge on Pubic Symphysis-Fetal Head Segmentation (PSFHS) was held alongside the 26th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2023). This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5,101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions. The scientific community's enthusiastic participation led to the selection of the top 8 out of 179 entries from 193 registrants in the initial phase to proceed to the competition's second stage. These algorithms have elevated the state-of-the-art in automatic PSFHS from intrapartum ultrasound images. A thorough analysis of the results pinpointed ongoing challenges in the field and outlined recommendations for future work. The top solutions and the complete dataset remain publicly available, fostering further advancements in automatic segmentation and biometry for intrapartum ultrasound imaging. © 2024, CC0.

关键词： Ultrasonic imaging

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Test-Time Training-Free Domain Adaptation

Test-Time Training-Free Domain Adaptation

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International Conference on Acoustics, speech, and Signal Processing (ICASSP)

作者： Yongxiang Feng Weihua He Kaichao You Bing Liu Ziyang Zhang Yaoyuan Wang Minglei Li Yihang Lou Jiawei Li Guoqi Li Jianxing Liao Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. School of Electronics Engineering and Computer Science Peking University China Language & Speech Innovation Lab Huawei Technologies Co. Ltd. GoTen AI Lab Department of Intelligent Vision Huawei Technologies Co. Ltd. China Department of Production Automation Development Huawei Technologies Co. Ltd. China Institute of Automation Chinese Academy of Sciences China

Deploying deep learning models to new environments is very challenging. Domain adaptation (DA) is a promising paradigm to solve the problem by collecting and adapting to unlabeled data in new environments. Though research efforts have led to steady performance improvement over the past decade, DA algorithms are still hard to deploy, as training on unlabeled new data makes tuning difficult and not feasible for inference-only devices. To make DA practical, in this paper we study a new problem named Test-time Training-Free Domain Adaptation (TTDA), where trained models must adapt to a single input (mimicking the test-time scenario) without training. By exploiting spatial activation that was previously overlooked and simply averaged out, we propose a simple method based on Feature Statistics Transformation (FST) on-the-fly for each test example. The proposed algorithm is tested in the TTDA setting on two standard DA benchmarks. Surprisingly, it surpasses or performs on par with state-of-the-art DA methods, even though they require additional training. We envision that this training-free paradigm has the potential to bring DA to embedded devices and would be of interest to audience of community.

关键词： Training Performance evaluation Adaptation models Transfer learning Signal processing algorithms Statistical distributions Classification algorithms

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Deep semi-supervised knowledge distillation for overlapping cervical cell instance segmentation 23rd

Deep semi-supervised knowledge distillation for overlapping ...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Zhou, Yanning Chen, Hao Lin, Huangjing Heng, Pheng-Ann Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783030597092

Deep learning methods show promising results for overlapping cervical cell instance segmentation. However, in order to train a model with good generalization ability, voluminous pixel-level annotations are demanded which is quite expensive and time-consuming for acquisition. In this paper, we propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining (MMT-PSM), which consists of a teacher and a student network during training. Two networks are encouraged to be consistent both in feature and semantic level under small perturbations. The teachers self-ensemble predictions from K-time augmented samples are used to construct the reliable pseudo-labels for optimizing the student. We design a novel strategy to estimate the sensitivity to perturbations for each proposal and select informative samples from massive cases to facilitate fast and effective semantic distillation. In addition, to eliminate the unavoidable noise from the background region, we propose to use the predicted segmentation mask as guidance to enforce the feature distillation in the foreground region. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only, and outperforms state-of-the-art semi-supervised methods. Code: https://***/SIAAAAAA/MMT-PSM. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Vacuous standards – Subversion of the OSAC standards-development process

Forensic Science International: Synergy

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Forensic science International: Synergy 2020年 2卷 206-209页

作者： Morrison, Geoffrey Stewart Neumann, Cedric Geoghegan, Patrick Henry Forensic Data Science Laboratory Computer Science Department Aston University Birmingham United Kingdom Forensic Speech Science Laboratory Aston Institute for Forensic Linguistics Aston University Birmingham United Kingdom Department of Mathematics & Statistics South Dakota State University Brookings SD United States Department of Biomedical Engineering Aston University Birmingham United Kingdom

In the context of development of standards for forensic science, particularly standards initially developed by the U.S. Organization of Scientific Area Committees for Forensic science (OSAC), this perspective paper raises concern about the publication of vacuous standards. Vacuous standards generally state few requirements;the requirements they do state are often vague;compliance with their stated requirements can be achieved with little effort – the bar is set very low;and compliance with their stated requirements would not be sufficient to lead to scientifically valid results. This perspective paper proposes a number of requirements that we believe would be essential in order for a standard on validation of forensic-science methods to be fit for purpose. © 2020 The Authors

关键词： Forensic science Method validation OSAC Standard Validation

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Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN

arXiv

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arXiv 2022年

作者： Mo, Yaozong Li, Chaofeng Ren, Wenqi Shang, Shaopeng Wang, Wenwu Wu, Xiao-Jun The Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai201306 China The School of Cyber Science and Technology Sun Yatsen University Shenzhen518000 China The Vocational College Shanghai Jian Qiao University Shanghai201306 China The Center for Vision Speech and Signal Processing Department of Electrical and Electronic Engineering University of Surrey SurreyGU2 7XH United Kingdom The School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China

Deep learning-based methods have achieved significant performance for image defogging. However, existing methods are mainly developed for land scenes and perform poorly when dealing with overwater foggy images, since overwater scenes typically contain large expanses of sky and water. In this work, we propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. To promote the recovery of the objects on water in the image, two loss functions are exploited for the network where a prior map is designed to invert the dark channel and the min-max normalization is used to suppress the sky and emphasize objects. However, due to the unpaired training set, the network may learn an under-constrained domain mapping from foggy to fog-free image, leading to artifacts and loss of details. Thus, we propose an intuitive Upscaling Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC) to mitigate this issue. Extensive experiments on qualitative and quantitative comparisons demonstrate that the proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Low-resolution human pose estimation

arXiv

引用

arXiv 2021年

作者： Wang, Chen Zhang, Feng Zhu, Xiatian Ge, Shuzhi Sam School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China Centre for Vision Speech and Signal Processing Faculty of Engineering and Physical Sciences University of Surrey GuildfordGU2 7XH United Kingdom Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Human pose estimation has achieved significant progress on images with high imaging resolution. However, low-resolution imagery data bring nontrivial challenges which are still under-studied. To fill this gap, we start with investigating existing methods and reveal that the most dominant heatmap-based methods would suffer more severe model performance degradation from low-resolution, and offset learning is an effective strategy. Established on this observation, in this work we propose a novel Confidence-Aware Learning (CAL) method which further addresses two fundamental limitations of existing offset learning methods: inconsistent training and testing, decoupled heatmap and offset learning. Specifically, CAL selectively weighs the learning of heatmap and offset with respect to ground-truth and most confident prediction, whilst capturing the statistical importance of model output in mini-batch learning manner. Extensive experiments conducted on the COCO benchmark show that our method outperforms significantly the state-of-the-art methods for low-resolution human pose estimation. © 2021, CC BY-SA.

关键词： Learning systems

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