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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 TRANSFORMER NETWORKS FOR TIME SERIES CLASSIFICATION: THE NPP SAFETY CASE

DEEP TRANSFORMER NETWORKS FOR TIME SERIES CLASSIFICATION: TH...

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2021 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2021

作者： Zha, Bing Vanni, Alessandro Hassan, Yassin Aldemir, Tunc Yilmaz, Alper Photogrammetric Computer Vision Lab Ohio State University 2070 Neil Avenue ColumbusOH United States Nuclear Engineering Program Ohio State University 201 W. 19th Avenue ColumbusOH United States Department of Nuclear Engineering Texas A&M University Building 3133 423 Spence St College StationTX United States

A challenging part of dynamic probabilistic risk assessment for nuclear power plants is the need for large amounts of temporal simulations given various initiating events and branching conditions from which representative feature extraction becomes complicated for subsequent applications. Artificial Intelligence techniques have been shown to be powerful tools in time dependent sequential data processing to automatically extract and yield complex features from large data. An advanced temporal neural network referred to as the Transformer is used within a supervised learning fashion to model the time dependent nuclear power plant (NPP) simulation data and to infer whether a given sequence of events leads to core damage or not. The training and testing datasets for the Transformer are obtained by running 10,000 RELAP5-3D NPP station blackout scenarios with the list of variables obtained from the RAVEN software. Each simulation is classified as ‘OK’ or ‘CORE DAMAGE’ based on the consequence. The results show that the Transformer can learn the characteristics of the sequential data and yield promising performance with approximately 99% classification accuracy for the testing dataset. © 2021 PSA. All Rights Reserved.

关键词： Nuclear power plants

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Procedural reasoning networks for understanding multimodal procedures 23

Procedural reasoning networks for understanding multimodal p...

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23rd Conference on Computational Natural Language Learning, CoNLL 2019

作者： Amac, Mustafa Sercan Yagcioglu, Semih Erdem, Aykut Erdem, Erkut Hacettepe University Computer Vision Lab. Dept. of Computer Engineering Ankara Turkey

ISBN: (纸本)9781950737727

This paper addresses the problem of comprehending procedural commonsense knowledge. This is a challenging task as it requires identifying key entities, keeping track of their state changes, and understanding temporal and causal relations. Contrary to most of the previous work, in this study, we do not rely on strong inductive bias and explore the question of how multimodality can be exploited to provide a complementary semantic signal. Towards this end, we introduce a new entity-aware neural comprehension model augmented with external relational memory units. Our model learns to dynamically update entity states in relation to each other while reading the text instructions. Our experimental analysis on the visual reasoning tasks in the recently proposed RecipeQA dataset reveals that our approach improves the accuracy of the previously reported models by a large margin. Moreover, we find that our model learns effective dynamic representations of entities even though we do not use any supervision at the level of entity states. © 2019 Association for Computational Linguistics.

关键词： Semantics

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Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models

arXiv

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arXiv 2024年

作者： Sun, Wangtao Zhang, Chenxiang Zhang, Xueyou Yu, Xuanqing Huang, Ziyang Xu, Haotian Chen, Pei He, Shizhu Zhao, Jun Liu, Kang The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Engineering Laboratory for Intelligent Industrial Vision Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Texas A&M University United States Shanghai Artificial Intelligence Laboratory China Xiaohongshu Inc China AI Lab AIGility Cloud Innovation Beijing China

Although Large Language Models (LLMs) have demonstrated strong instruction-following ability, they are further supposed to be controlled and guided by inferential rules in real-world scenarios to be safe, accurate, and intelligent. This demands the possession of inferential rule-following capability of LLMs. However, no prior work has made a clear evaluation of the inferential rule-following capability of LLMs. Previous studies that try to evaluate the inferential rule-following capability of LLMs fail to distinguish the inferential rule-following scenarios from the instruction-following scenarios. Therefore, this paper first clarifies the concept of inferential rule-following and proposes a comprehensive benchmark, RuleBench, to evaluate a diversified range of inferential rule-following abilities. Our experimental results on a variety of LLMs show that they are still limited in following rules. Our analysis based on the evaluation results provides insights into the improvements for LLMs toward a better inferential rule-following intelligent agent. We further propose Inferential Rule-Following Tuning (IRFT). The experimental results show that through IRFT, LLMs can learn abstract inferential rule-following abilities from purely synthetic data and then generalize to RuleBench. The data and code can be found at: https://***/r/llm-rulefollowing-B3E3/ Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent agents

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A Survey of the Self Supervised Learning Mechanisms for vision Transformers

arXiv

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arXiv 2024年

作者： Khan, Asifullah Sohail, Anabia Fiaz, Mustansar Hassan, Mehdi Afridi, Tariq Habib Marwat, Sibghat Ullah Munir, Farzeen Ali, Safdar Naseem, Hannan Zaheer, Muhammad Zaigham Ali, Kamran Sultana, Tangina Tanoli, Ziaurrehman Akhter, Naeem Pattern Recognition Lab DCIS PIEAS Nilore Islamabad45650 Pakistan PIEAS Nilore Islamabad45650 Pakistan Deep Learning Lab Center for Mathematical Sciences PIEAS Nilore Islamabad45650 Pakistan Center of Secure Cyber-Physical Security Systems Khalifa University Abu Dhabi United Arab Emirates IBM Research United States Department of Computer Science Air University Islamabad Pakistan Department of Computer Science and Engineering Kyung Hee University Global Campus 1732 Gyeonggi-do Yongin17104 Korea Republic of Department of Electrical Engineering and Automation Aalto University Finland Finnish Center of Artificial Center Finland Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Malaysia Computer Vision Department Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates Karachi Pakistan Department of Electronics and Communication Engineering Hajee Mohammad Danesh Science and Technology University Bangladesh HiLIFE University of Helsinki Finland

vision Transformers (ViTs) have recently demonstrated remarkable performance in computer vision tasks. However, their parameter-intensive nature and reliance on large amounts of data for effective performance have shifted the focus from traditional human-annotated labels to unsupervised learning and pretraining strategies that uncover hidden structures within the data. In response to this challenge, self-supervised learning (SSL) has emerged as a promising paradigm. SSL leverages inherent relationships within the data itself as a form of supervision, eliminating the need for manual labeling and offering a more scalable and resource-efficient alternative for model training. Given these advantages, it is imperative to explore the integration of SSL techniques with ViTs, particularly in scenarios with limited labeled data. Inspired by this evolving trend, this survey aims to systematically review SSL mechanisms tailored for ViTs. We propose a comprehensive taxonomy to classify SSL techniques based on their representations and pre-training tasks. Additionally, we discuss the motivations behind SSL, review prominent pre-training tasks, and highlight advancements and challenges in this field. Furthermore, we conduct a comparative analysis of various SSL methods designed for ViTs, evaluating their strengths, limitations, and applicability to different scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Density-aware and Depth-aware Visual Representation for Zero-Shot Object Counting

Density-aware and Depth-aware Visual Representation for Zero...

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

作者： Fang Nan Feng Tian Ni Zhang Nian Liu Haonan Miao Guang Dai Mengmeng Wang Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE SGIT AI Lab State Grid Corporation of China Zhejiang University of Technology Hang Zhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous methods often utilize CLIP semantic classifiers with class names for zero-shot object counting. However, they ignore crucial density and depth knowledge for counting tasks. Thus, we propose a density-aware and depth-aware prompt counting model, which captures density information via learning density-aware prompts based on density-aware contrastive loss and incorporates depth guidance with predefined depth-aware prompts. To facilitate the training process, we design two strategies for standard counting loss and the contrastive loss, where the former prioritizes larger and sparser objects initially, gradually focusing on smaller and denser objects, and the latter adopts coarse-to-fine density learning. Besides, we construct a dataset named LVIS-372 with more real-world scenarios and balanced instance distribution compared to existing ones. Finally, the experimental results demonstrate the effectiveness of our proposed method.

关键词： Training Visualization Semantics Focusing Signal processing Acoustics Power capacitors Speech processing Standards

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

NTIRE 2023 Challenge on 360° Omnidirectional Image and Vide...

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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Cao, Mingdeng Mou, Chong Yu, Fanghua Wang, Xintao Zheng, Yinqiang Zhang, Jian Dong, Chao Li, Gen Shan, Ying Timofte, Radu Sun, Xiaopeng Li, Weiqi Sheng, Xuhan Chen, Bin Ma, Haoyu Cheng, Ming Zhao, Shijie Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Wu, Renlong Yang, Yi Zhang, Zhilu Zhang, Shuohao Li, Junyi Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Yang, Hao-Hsiang Chen, Yi-Chung Huang, Zhi-Kai Chen, Wei-Ting Chiang, Yuan-Chun Chang, Hua-En Chen, I.-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Zhang, Zebin Zhang, Jiaqi Wang, Yuhui Cui, Shuhao Huang, Junshi Zhu, Li Tian, Shuman Yu, Wei Luo, Bingchun Cui, Wanwan Xu, Tianyu Li, Chunyang Bao, Long Sun, Heng Zhang, Zhenyu Wang, Qian The University of Tokyo Japan Arc Lab Tencent Pcg China Peking University China Shenzhen Institute of Advanced Technology Cas China Platform Technologies Tencent Online Video China Computer Vision Lab Ifi & Caidas University of Würzburg Germany ByteDance China Peking University Shenzhen Graduate School China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China Harbin Institute of Technology China Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States ShanghaiTech University China Meituan China Xiaomi Inc

ISBN: (纸本)9798350302493

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets. © 2023 IEEE.

关键词： Image compression

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Polar-Net: A Clinical-Friendly Model for Alzheimer’s Disease Detection in OCTA Images

arXiv

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arXiv 2023年

作者： Liu, Shouyue Hao, Jinkui Xu, Yanwu Fu, Huazhu Guo, Xinyu Liu, Jiang Zheng, Yalin Liu, Yonghuai Zhang, Jiong Zhao, Yitian Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Cixi Biomedical Research Institute Wenzhou Medical University China School of Future Technology South China University of Technology Guangzhou China Pazhou Lab Guangzhou China Institute of High-Performance Computing Agency for Science Technology and Research Singapore Department of Computer Science Southern University of Science and Technology China Department of Eye and Vision Science University of Liverpool United Kingdom Department of Computer Science Edge Hill University United Kingdom

Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer’s disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. In this work, we propose a novel deep-learning framework called Polar-Net. Our approach involves mapping OCTA images from Cartesian coordinates to polar coordinates, which allows for the use of approximate sector convolution and enables the implementation of the ETDRS grid-based regional analysis method commonly used in clinical practice. Furthermore, Polar-Net incorporates clinical prior information of each sector region into the training process, which further enhances its performance. Additionally, our framework adapts to acquire the importance of the corresponding retinal region, which helps researchers and clinicians understand the model’s decision-making process in detecting AD and assess its conformity to clinical observations. Through evaluations on private and public datasets, we have demonstrated that Polar-Net outperforms existing state-of-the-art methods and provides more valuable pathological evidence for the association between retinal vascular changes and AD. In addition, we also show that the two innovative modules introduced in our framework have a significant impact on improving overall performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

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Visual Compositional Learning for Human-Object Interaction Detection 16th

Visual Compositional Learning for Human-Object Interaction D...

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16th European Conference on computer vision, ECCV 2020

作者： Hou, Zhi Peng, Xiaojiang Qiao, Yu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030585549

Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types forms a long-tail distribution. We devise a deep Visual Compositional Learning (VCL) framework, which is a simple yet efficient framework to effectively address this problem. VCL first decomposes an HOI representation into object and verb specific features, and then composes new interaction samples in the feature space via stitching the decomposed features. The integration of decomposition and composition enables VCL to share object and verb features among different HOI samples and images, and to generate new interaction samples and new types of HOI, and thus largely alleviates the long-tail distribution problem and benefits low-shot or zero-shot HOI detection. Extensive experiments demonstrate that the proposed VCL can effectively improve the generalization of HOI detection on HICO-DET and V-COCO and outperforms the recent state-of-the-art methods on HICO-DET. Code is available at https://***/zhihou7/VCL. © 2020, Springer Nature Switzerland AG.

关键词： Object detection

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