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More Grounded Image Captioning by Distilling Image-Text Matching Model

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More Grounded Image Captioning by Distilling Image-Text Matc...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanen Zhou Meng Wang Daqing Liu Zhenzhen Hu Hanwang Zhang Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) School of Computer Science and Information Engineering Hefei University of Technology University of Science and Technology of China Nanyang Technological University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual attention not only improves the performance of image captioners, but also serves as a visual interpretation to qualitatively measure the caption rationality and model transparency. Specifically, we expect that a captioner can fix its attentive gaze on the correct objects while generating the corresponding words. This ability is also known as grounded image captioning. However, the grounding accuracy of existing captioners is far from *** improve the grounding accuracy while retaining the captioning quality, it is expensive to collect the word-region alignment as strong *** this end, we propose a Part-of-Speech (POS) enhanced image-text matching model (SCAN[24]): POS-SCAN, as the effective knowledge distillation for more grounded image captioning. The benefits are two-fold: 1) given a sentence and an image, POS-SCAN can ground the objects more accurately than SCAN; 2) POS-SCAN serves as a word-region alignment regularization for the captioner's visual attention module. By showing benchmark experimental results, we demonstrate that conventional image captioners equipped with POS-SCAN can significantly improve the grounding accuracy without strong supervision. Last but not the least, we explore the indispensable Self-Critical Sequence Training (SCST[46]) in the context of grounded image captioning and show that the image-text matching score can serve as a reward for more grounded captioning 1 .

关键词： Visualization Grounding Task analysis Training Measurement Computational modeling Image edge detection

Integrated Sensing and Communication Signals Toward 5G-A and 6G: A Survey

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arXiv 2023年

作者： Wei, Zhiqing Qu, Hanyang Wang, Yuan Yuan, Xin Wu, Huici Du, Ying Han, Kaifeng Zhang, Ning Feng, Zhiyong The Key Laboratory of Universal Wireless Communications Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China The National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Peng Cheng Laboratory Shenzhen518066 China Data61 CSIRO Sydney Australia The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The University of Science and Technology of China China Academy of Information and Communications Technology Beijing100191 China China Academy of Information and Communications Technology Beijing100191 China

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method and super resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication *** Codes 94-02 Copyright © 2023, The Authors. All rights reserved.

关键词： Orthogonal frequency division multiplexing

Multi-frame joint enhancement for early interlaced videos

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arXiv 2021年

作者： Zhao, Yang Ma, Yanbo Chen, Yuan Jia, Wei Wang, Ronggang Liu, Xiaoping The Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China The School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School 2199 Lishui Road Shenzhen518055 China Peng Cheng Laboratory Shenzhen518000 China

Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditional methods mainly focus on simple interlacing mechanism, and cannot deal with the complex artifacts in real-world early videos. Recent interlaced video reconstruction deep deinterlacing models only focus on single frame, while neglecting important temporal information. Therefore, this paper proposes a multiframe deinterlacing network joint enhancement network for early interlaced videos that consists of three modules, i.e., spatial vertical interpolation module, temporal alignment and fusion module, and final refinement module. The proposed method can effectively remove the complex artifacts in early videos by using temporal redundancy of multi-fields. Experimental results demonstrate that the proposed method can recover high quality results for both synthetic dataset and real-world early interlaced videos. Copyright © 2021, The Authors. All rights reserved.

关键词： Image reconstruction

3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training

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arXiv 2021年

作者： Deng, Yicheng Sun, Cheng Sun, Yongqi Zhu, Jiahui Key Lab of Big Data & Artificial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Information Science and Electrical Engineering Kyushu University Fukuoka8190395 Japan

3D human pose estimation from a single image is still a challenging problem despite the large amount of work that has been performed in this field. Generally, most methods directly use neural networks and ignore certain constraints (e.g., reprojection constraints, joint angle, and bone length constraints). While a few methods consider these constraints but train the network separately, they cannot effectively solve the depth ambiguity problem. In this paper, we propose a GAN-based model for 3D human pose estimation, in which a reprojection network is employed to learn the mapping of the distribution from 3D poses to 2D poses, and a discriminator is employed for 2D-3D consistency discrimination. We adopt a novel strategy to synchronously train the generator, the reprojection network and the discriminator. Furthermore, inspired by the typical kinematic chain space (KCS) matrix, we introduce a weighted KCS matrix and take it as one of the discriminator’s inputs to impose joint angle and bone length constraints. The experimental results on Human3.6M show that our method significantly outperforms state-of-the-art methods in most cases. © 2021, CC0.

关键词： Generative adversarial networks

Size-invariance matters: rethinking metrics and losses for imbalanced multi-object salient object detection 24

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Size-invariance matters: rethinking metrics and losses for i...

Proceedings of the 41st International Conference on Machine Learning

作者： Feiran Li Qianqian Xu Shilong Bao Zhiyong Yang Runmin Cong Xiaochun Cao Qingming Huang Institute of Information Engineering Chinese Academy of Sciences Beijing China and School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China and School of Control Science and Engineering Shandong University Jinan China and Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Shenzhen Campus Sun Yat-sen University School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China and Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD.

关键词：

Towards efficient local causal structure learning

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arXiv 2021年

作者： Yang, Shuai Wang, Hao Yu, Kui Cao, Fuyuan Wu, Xindong The School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China The School of Computer and Information Technology Shanxi University Taiyuan030006 China The Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology Hefei230601 China Mininglamp Academy of Sciences Mininglamp Technology Beijing100102 China

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

Traffic Matrix Prediction in SDN based on Spatial-Temporal Residual Graph Convolutional Network

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Traffic Matrix Prediction in SDN based on Spatial-Temporal R...

Chinese Control and Decision Conference, CCDC

作者： Xintong Wang Yibo Sun Xuan Wang Enliang Wang Zhixin Sun Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing P.R. China Post Industry Technology Research and Development Center of the State Posts Bureau (Internet of Things Technology) Nanjing University of Posts and Telecommunications Nanjing P.R. China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing University of Posts and Telecommunications Nanjing P.R. China School of Information Technology and Electrical Engineering The University of Queensland Brisbane Brisbane Australia

Traffic prediction is the basis for dynamic network services and resource optimization. Software Defined Networking (SDN) provides a global view for network measurement and management, and a reliable way for network information collection and network traffic prediction. Recently, SDN traffic prediction model enabled by deep learning mainly focuses on mining the temporal correlation of traffic data, while the modeling of its spatial correlation remains to be explored. From the perspective of the problem, a novel Spatial-Temporal Residual Graph Convolutional Network (STRGCN) model for SDN Traffic Matrix (TM) prediction is proposed. Combined with SDN network topology, TM prediction is defined as time series data prediction in non-Euclidean space. Temporal Gated-Conv Blocks are based on Gated Convolutional Layer, and Spatial Graph-Conv Blocks are based on Graph Convolutional Network (GCN). Through identity mapping between Spatial-Temporal Conv Blocks, the deep spatial and temporal features extraction is implemented. Tested by TM data from CERNET and GÉANT datasets, the accuracy and the timely performance of our model are evaluated. Experimental results suggest that the general performance of STRGCN for TM prediction in SDN is superior to the current deep learning models.

关键词：

Combining Global and Sequential Patterns for Multivariate Time Series Forecasting

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Combining Global and Sequential Patterns for Multivariate Ti...

IEEE International Conference on Big data

作者： Zhaoxi Li Jun He Hongyan Liu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China School of Economics and Management Tsinghua g University Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Multivariate time series forecasting is very important for many applications. Many studies have been conducted for accurate and interpretable prediction methods. However, existing methods either cannot take both times series and covariates into consideration, lacking of interpretability, or ignore global trends across multivariate time series. In this paper, we aim to solve these issues. To this end, we propose a new model named TEDGE for accurate and interpretable time series prediction. In this model, we extract global trends hidden across multivariate times series to improve prediction accuracy. Meanwhile, we utilize a deep recurrent model with attention mechanism to find long-and short-term sequential patterns hidden in individual time series with interpretability. We conduct experiments on several datasets to evaluate the proposed models performance. Results demonstrate the superior performance of our proposed model.

关键词： Conferences Time series analysis Predictive models Big data Market research data models Forecasting

Pareto-aware Neural Architecture Generation for Diverse Computational Budgets

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arXiv 2022年

作者： Guo, Yong Chen, Yaofo Zheng, Yin Chen, Qi Zhao, Peilin Chen, Jian Huang, Junzhou Tan, Mingkui The School of Software Engineering South China University of Technology China Max Planck Institute for Informatics Germany The Weixin Group Tencent China The School of Computer Science The University of Adelaide Australia Tencent AI Lab Tencent China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Pazhou Laboratory Guangzhou China

Designing feasible and effective architectures under diverse computational budgets, incurred by different applications/devices, is essential for deploying deep models in real-world applications. To achieve this goal, existing methods often perform an independent architecture search process for each target budget, which is very inefficient yet unnecessary. More critically, these independent search processes cannot share their learned knowledge (i.e., the distribution of good architectures) with each other and thus often result in limited search results. To address these issues, we propose a Pareto-aware Neural Architecture Generator (PNAG) which only needs to be trained once and dynamically produces the Pareto optimal architecture for any given budget via inference. To train our PNAG, we learn the whole Pareto frontier by jointly finding multiple Pareto optimal architectures under diverse budgets. Such a joint search algorithm not only greatly reduces the overall search cost but also improves the search results. Extensive experiments on three hardware platforms (i.e., mobile device, CPU, and GPU) show the superiority of our method over existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Pareto principle