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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanhang Zhang Shuang Yang Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a novel strategy, ES 3 , for self-supervised learning of robust audio-visual speech representations from unlabeled talking face videos. While many recent approaches for this task primarily rely on guiding the learning process using the audio modality alone to capture information shared between audio and video, we reframe the problem as the acquisition of shared, unique (modality-specific) and synergistic speech information to address the inherent asymmetry between the modalities. Based on this formulation, we propose a novel “evolving” strategy that progressively builds joint audio-visual speech representations that are strong for both uni-modal (audio & visual) and bi-modal (audio-visual) speech. First, we leverage the more easily learnable audio modality to initialize audio and visual representations by capturing audio-unique and shared speech information. Next, we incorporate video-unique speech information and bootstrap the audio-visual repre-sentations on top of the previously acquired shared knowledge. Finally, we maximize the total audio-visual speech information, including synergistic information to obtain robust and comprehensive representations. We implement ES 3 as a simple Siamese framework and experiments on both English benchmarks and a newly contributed large-scale Mandarin dataset show its effectiveness. In particular, on LRS2-BBC, our smallest model is on par with SoTA models with only 1/2 parameters and 1/8 unlabeled data (223h).

关键词： Visualization Computer vision Self-supervised learning Manuals Benchmark testing Data models Pattern recognition

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Synthesizing Knowledge-enhanced Features for Real-world Zero-shot Food Detection

arXiv

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arXiv 2024年

作者： Zhou, Pengfei Min, Weiqing Song, Jiajun Zhang, Yang Jiang, Shuqiang The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China The Institute of Intelligent Computing Technology Chinese Academy of Sciences Suzhou215124 China

Food computing brings various perspectives to computer vision like vision-based food analysis for nutrition and health. As a fundamental task in food computing, food detection needs Zero-Shot Detection (ZSD) on novel unseen food objects to support real-world scenarios, such as intelligent kitchens and smart restaurants. Therefore, we first benchmark the task of Zero-Shot Food Detection (ZSFD) by introducing FOWA dataset with rich attribute annotations. Unlike ZSD, fine-grained problems in ZSFD like inter-class similarity make synthesized features inseparable. The complexity of food semantic attributes further makes it more difficult for current ZSD methods to distinguish various food categories. To address these problems, we propose a novel framework ZSFDet to tackle fine-grained problems by exploiting the interaction between complex attributes. Specifically, we model the correlation between food categories and attributes in ZSFDet by multi-source graphs to provide prior knowledge for distinguishing fine-grained features. Within ZSFDet, Knowledge-Enhanced Feature Synthesizer (KEFS) learns knowledge representation from multiple sources (e.g., ingredients correlation from knowledge graph) via the multi-source graph fusion. Conditioned on the fusion of semantic knowledge representation, the region feature diffusion model in KEFS can generate fine-grained features for training the effective zero-shot detector. Extensive evaluations demonstrate the superior performance of our method ZSFDet on FOWA and the widely-used food dataset UECFOOD-256, with significant improvements by 1.8% and 3.7% ZSD mAP compared with the strong baseline RRFS. Further experiments on PASCAL VOC and MS COCO prove that enhancement of the semantic knowledge can also improve the performance on general ZSD. Code and dataset are available at https://***/LanceZPF/KEFS. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Multi-View Consistent 3D GAN Inversion via Bidirectional Encoder

Multi-View Consistent 3D GAN Inversion via Bidirectional Enc...

引用

International Conference on Automatic Face and Gesture Recognition

作者： Haozhan Wu Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of the Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

3D GAN inversion enables not only 3D reconstruction from a 2D image, but also novel view synthesis and image editing. Existing works ensure the novel view synthesis quality by constraining the synthesized views to conform to the real image distribution. However, most of the methods did not consider the multi-view consistency, i.e., different photos of the same 3D scene via 3D GAN inversion can be inverted to the same 3D scene. In this paper, we propose a bidirectional encoder (BiDiE) for 3D GAN inversion that can improve the multi-view consistency and alleviate the interference of camera parameter prediction errors. On the one hand, the bidirectional encoder takes real images as input, estimates the camera parameters, and performs 3D reconstruction. On the other hand, the bidirectional encoder takes randomly sampled latent code and camera parameters as input, and generates synthesized images to assist in the latent code learning process. In addition, we extend the latent space from W+ to W++ to improve its reconstruction and editing capabilities. Experiments on the FFHQ, CelebA-HQ and Multi-PIE datasets prove that our proposed method outperforms state-of-the-art methods in multi-view consistent reconstruction as well as editing capability. 1 1 Code and datasets are available at https://***/WHZMM/BiDiE

关键词： Three-dimensional displays Codes Face recognition Interference Gesture recognition Cameras Image reconstruction

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Electromagnetic scattering of two-dimensional rough cylinder based on statistical integral equation 2

Electromagnetic scattering of two-dimensional rough cylinder...

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2020 2nd International Conference on Artificial Intelligence Technologies and Application, ICAITA 2020

作者： Zhu, Mengnan Wang, Anqi Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

The study of electromagnetic scattering from Gaussian rough surface is of great significance in radar reconnaissance, target tracking and ocean remote sensing. The moment method (MOM) is a commonly used method with high accuracy. However, in the past research, Monte Carlo method is used to simulate the Gaussian rough surface. In this method, a large number of random simulation operations are carried out, and then a statistical convergence result is obtained by counting the results of all operations. In this paper, the stochastic integral equation method SIEM(Stochastic Integral Equation Method) is proposed to calculate the electromagnetic scattering of Gaussian rough surface. SIEM avoids the difficulty of high difficulty precise modeling and repeated calculation many times, and only single modeling and calculation are carried out for the target problem. In this paper, the electromagnetic scattering of two-dimensional cylinder with Gaussian rough surface is solved, and the effectiveness of SIEM is proved by comparing with mom. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Method of moments

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Research on the Structure and Realization of Mixture-of-Experts

Research on the Structure and Realization of Mixture-of-Expe...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Qidong Yan Yingjie Li Ning Ma Fucheng Wan Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Intelligent Processing of Gansu Province Northwest Minzu University Lanzhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The development of Large Language Model (LLM) has reached a bottleneck, and in order to solve these problems it is necessary to continue to increase the complexity of the model. The parameters of LLM become larger and larger with the practical requirements of different application scenarios, which increases the difficulty of training as well as inference. One of the approaches to deal with the question is also known as Mixture-of-Experts (MoE), which is based on gated networks. The Mixture-of-Experts is a sparse gate-controlled deep learning model that mainly consists of a set of expert models and a gated model. The basic idea of the Mixture-of-Experts is to partition the input data into multiple regions based on the task type and assign one or more expert models to the data in each region. It is able to achieve excellent performance in complex tasks and flexibly adapt to different input distributions and task scenarios. Its high sparsity improves computational efficiency, and its specialized design enhances the ability to model complex data structures.

关键词： Training Deep learning Adaptation models Computational modeling Large language models Logic gates Data structures

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RALT—A film review sentiment classification model

RALT—A film review sentiment classification model

引用

International Conference on Computer Engineering and Applications (ICCEA)

作者： Ning Chen Xiangzhen He Xianghe Meng Yugang Dai Key Laboratory of Linguistic and Cultural Computing Ministry of Education (Northwest Minzu University) Lanzhou China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Province (Northwest Minzu University) Lanzhou China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

With the development of the internet and film industry, filmgoers have published a large amount of review texts in many areas such as film analysis, film sharing, and social media. The interpretation of review texts is diverse and strongly environmentally dependent, which adds more difficulty to the task of text classification. How to quickly and accurately judge the sentiment tendency of review texts and classify them accurately, and improve the application value of review text data has become one of the popular research directions in natural language processing (NLP). This article proposes a film review sentiment analysis model RALT, which combines a text convolutional neural network (TextCNN) with a self-attention mechanism and a fused long short-term memory (LSTM). The model uses the RoBERTa Employing a pre-trained model to produce dynamic textual word embeddings addresses the intricacies associated with polysemy. Sentiment classification experiments were conducted on the IMDB dataset. The experiments were conducted by comparing BERT and RoBERTa with TextCNN, RNN, LSTM, BI-LSTM and other models, and it was found that the proposed model achieved the best accuracy and F1-score in the classification *** experiment showed that the model is suitable for the field of film review sentiment classification.

关键词： Accuracy Reviews Social networking (online) Computational modeling Text categorization Feature extraction Data models

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Highly isolated two-elements ultra-wideband mimo fractal antenna with multi band-notched characteristics

引用

Progress In Electromagnetics Research C 2021年 116卷 13-24页

作者： Cai, Yong Cheng, Guangshang Ren, Xingang Wu, Jie Ren, Hao Song, Kaihong Huang, Zhixiang Wu, Xianliang Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Luan237000 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Institute of Physical Science and Information Technology Anhui University Hefei230039 China Key Laboratory of Electromagnetic Environmental Sensing Department of Education of Anhui Province Anhui University Hefei230039 China

This work presents a high isolation UWB-MIMO antenna with a bandwidth up to 8.6 GHz based on a Minkowski fractal structure. The proposed antenna is fed by microstrip and comprises two orthogonal monopole antennas, which delivers a decent isolation effect. Moreover, the ground is designed as two separated blocks with an I-shaped branch for improving the isolation degree between the units. The resultant isolation degree of this antenna is greater than 25 dB. Besides, the electromagnetic interference in the partial frequency band (such as Wi-Max band (3.45–4.45 GHz), WLAN band (5.1– 5.8 GHz), and X-band (7.25–7.75 GHz)) is further prevented through etching a split-ring resonator (SRR) and C-slot on the unit. The antenna reflection coefficient of the UWB-MIMO antenna at the notch is 3.5 dB, which indicates that the antenna has a conspicuousness anti-interference effect. Through the above judicious design, the proposed UWB-MIMO antenna possesses a relative bandwidth of 113% (up to 8.6 GHz). The envelope correlation coefficient between antenna units is less than 0.005, and the antenna radiation efficiency is up to 80%. The results indicate that the proposed MIMO antenna meets UWB applications. © 2021, Electromagnetics Academy. All rights reserved.

关键词： Monopole antennas

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Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement

arXiv

引用

arXiv 2024年

作者： Yuan, Zheng Zhang, Jie Wang, Yude Shan, Shiguang Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

The attention mechanism has been proven effective on various visual tasks in recent years. In the semantic segmentation task, the attention mechanism is applied in various methods, including the case of both Convolution Neural Networks (CNN) and Vision Transformer (ViT) as backbones. However, we observe that the attention mechanism is vulnerable to patch-based adversarial attacks. Through the analysis of the effective receptive field, we attribute it to the fact that the wide receptive field brought by global attention may lead to the spread of the adversarial patch. To address this issue, in this paper, we propose a Robust Attention Mechanism (RAM) to improve the robustness of the semantic segmentation model, which can notably relieve the vulnerability against patch-based attacks. Compared to the vallina attention mechanism, RAM introduces two novel modules called Max Attention Suppression and Random Attention Dropout, both of which aim to refine the attention matrix and limit the influence of a single adversarial patch on the semantic segmentation results of other positions. Extensive experiments demonstrate the effectiveness of our RAM to improve the robustness of semantic segmentation models against various patch-based attack methods under different attack settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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A RIS-Assisted Multiuser MIMO Communication Method Based on Deep Reinforcement Learning

A RIS-Assisted Multiuser MIMO Communication Method Based on ...

引用

IEEE International Conference on Communication and Information Systems (ICCIS)

作者： Changlin Tian Hui Zhi Kui Tie Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Electronic and Information Engineering Anhui University Hefei China Ceyear Technologies (AnHui) CO. LTD Bengbu China

In this paper, a RIS-assisted multiuser MIMO communication method based on deep reinforcement learning (RMMC-DRL) is proposed for multiuser scenarios. Our objective is to find the optimal transmit beamforming matrix of BS and optimal phase shift matrix of reflective intelligent surface (RIS) to maximize the sum rate of multiuser, this problem is reduced into a constrained optimization problem. It is a non-convex optimization problem, so we solve it through deep reinforcement learning (DRL) and then use the results for communication. In the DRL, a deep deterministic policy gradient (DDPG) framework that can handle continuous states and actions is designed, reward is set as optimization goal, and the transmit beamforming matrix and the phase shift matrix of RIS are obtained through the interaction with environment. Unlike the alternating optimization (AO) method, which solve the transmit beamforming matrix and the RIS phase shift matrix alternatively, the RMMC-DRL can obtain both transmit beamforming matrix and RIS phase shift matrix simultaneously as the output of DRL. Simulation results show that RMMC-DRL can learn and improve its behavior by interacting with the environment. Compared with AO method, RMMC-DRL can obtain higher sum rate and lower computational complexity.

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A 0.33–2.61-GHz Rectifier with Expanded Dynamic Input Power Range Using Microstrip Impedance Compression Circuit

TechRxiv

引用

TechRxiv 2023年

作者： Li, Yingsong Liu, Xin Zhang, Dawei Wu, Xiaohu Liu, Xiaoguang Huang, Zhixiang The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China Shenzhen518055 China

In this letter, an ultra-broadband rectifier with expanded dynamic input power range (IPR) for both wireless power transfer (WPT) and radio frequency energy-harvesting (RFEH) is proposed and analyzed. Expanded dynamic IPR and broadband impedance matching are realized by utilizing a microstrip transmission line impedance compression circuit (MTLICC) with the topology of the paralleled voltage doubler. A proof-of-concept prototype shows a dynamic IPR of 8.5–23.5 dBm, a frequency range of 0.33–2.61 GHz (fractional bandwidth of 155%), PCE is greater than 50%, and a peak PEC of 79.7% (achieved at an input-power level of 19 dBm). The measurement results are in good agreement with the simulation. Covering most 3G/4G/5G/ISM frequency bands, this design is promising for RF power transmission and energy harvesting in the age of the Internet of Things systems. © 2023, CC BY.

关键词： Timing circuits

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