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Three Gaps for Quantisation in Learned Image Compression

Three Gaps for Quantisation in Learned Image Compression

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pan, Shi Finlay, Chris Besenbruch, Chri Knottenbelt, William Imperial Coll London Dept Comp London England DeepRender London England

ISBN: (纸本)9781665448994

Learned lossy image compression has demonstrated impressive progress via end-to-end neural network training. However, this end-to-end training belies the fact that lossy compression is inherently not differentiable, due to the necessity of quantisation. To overcome this difficulty in training, researchers have used various approximations to the quantisation step. However, little work has studied the mechanism of quantisation approximation itself. We address this issue, identifying three gaps arising in the quantisation approximation problem. These gaps are visualised, and show the effect of applying different quantisation approximation methods. Following this analysis, we propose a Soft-STE quantisation approximation method, which closes these gaps and demonstrates better performance than other quantisation approaches on the Kodak dataset.

关键词： Training Visualization computer vision Quantization (signal) Image coding conferences Neural networks

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Perceptual Image Compression using Relativistic Average Least Squares GANs

Perceptual Image Compression using Relativistic Average Leas...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Cheng, Zhengxue Fu, Ting Hu, Jiapeng Guo, Li Wang, Shihao Zhao, Xiongxin Zhou, Dajiang Song, Yang Ant Grp Hangzhou Peoples R China

ISBN: (纸本)9781665448994

In this work, we provide a detailed description on our submitted methods ANTxNN and ANTxNN SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021. We propose to incorporate Relativistic average Least Squares GANs (RaLSGANs) into Rate-Distortion Optimization for end-to-end training, to achieve perceptual image compression. We also compare two types of discriminator networks and visualize their reconstructed images. Experimental results have validated our method optimized by RaLSGANs can achieve higher subjective quality compared to PSNR, MS-SSIM or LPIPS-optimized models.

关键词： Training Visualization computer vision Image coding conferences Rate-distortion Generative adversarial networks

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Audio Transformer for Synthetic Speech Detection via Multi-Formant Analysis

Audio Transformer for Synthetic Speech Detection via Multi-F...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Luca Cuccovillo Milica Gerhardt Patrick Aichroth Fraunhofer Institute for Digital Media Technology IDMT Ilemanu Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper introduces a novel multi-task transformer for detecting synthetic speech. The network encodes magnitude and phase of the input speech with a feature bottleneck, used to autoencode the input magnitude, to predict the trajectory of the first phonetic formants (F0, F1, F2), and to distinguish whether the input speech is synthetic or natural. The approach achieves state-of-the-art performance on the ASVspoof 2019 LA dataset with an AUC score of 0.932, while ensuring interpretability at the same time.

关键词： computer vision conferences Phonetics Transformers Multitasking Trajectory pattern recognition

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ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up recognition and Explanation of Events in Video

ObjectGraphs: Using Objects and a Graph Convolutional Networ...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Gkalelis, Nikolaos Goulas, Andreas Galanopoulos, Damianos Mezaris, Vasileios CERTH ITI 6th Km Charilaou Thermi RdPOB 60361 Thessaloniki Greece

ISBN: (纸本)9781665448994

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-of-the-art performance on the publicly available FCVID and YLI-MED datasets(1).

关键词： computer vision Computational modeling conferences Detectors Cognition pattern recognition Object recognition

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BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Precision

BCNN: A Binary CNNWith All Matrix Ops Quantized To 1 Bit Pre...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Redfern, Arthur J. Zhu, Lijun Newquist, Molly K. Texas Instruments Inc 12500 TI Blvd Dallas TX 75243 USA Georgia Inst Technol North Ave NW Atlanta GA 30332 USA

ISBN: (纸本)9781665448994

This paper describes a CNN where all CNN style 2D convolution operations that lower to matrix matrix multiplication are fully binary. The network is derived from a common building block structure that is consistent with a constructive proof outline showing that binary neural networks are universal function approximators. 71.24% top 1 accuracy on the 2012 ImageNet validation set was achieved with a 2 step training procedure and implementation strategies optimized for binary operands are provided.

关键词： Training computer vision Convolution conferences Neural networks Memory management Decoding

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CE-PeopleSeg: Real-time people segmentation with 10% CPU usage for video conference

CE-PeopleSeg: Real-time people segmentation with 10% CPU usa...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Jiang, Ziyu He, Zhenhua Huang, Xueqin Yang, Zibin Tan, Pearl Texas A&M Univ College Stn TX 77843 USA EEO Washington DC USA

ISBN: (纸本)9781665448994

Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users' privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://***/geekJZY/***.

关键词： Visualization Privacy Government Education Graphics processing units Switches Streaming media

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LFNAT 2023 Challenge on Light Field Depth Estimation: Methods and Results

LFNAT 2023 Challenge on Light Field Depth Estimation: Method...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Sheng, Hao Liu, Yebin Yu, Jingyi Wu, Gaochang Xiong, Wei Guo, Longzhao Xie, Yanlin Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Wang, Tun Yang, Da Cui, Zhenglong Wang, Sizhe Zhao, Mingyuan Wang, Qiong Chen, Qianyu Liang, Zhengyu Wang, Yingqian Yang, Jungang Yang, Xueting Deng, Junli Cong, Ruixuan Chen, Rongshan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China Tsinghua University China Shanghaitech University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University China Beijing Meet Yuan Co. Ltd China Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada College of Computer Science and Technology Zhejiang University of Technology China National University of Defense Technology China School of Information and Communication Engineering Communication University of China China

ISBN: (纸本)9798350302493

This paper reviews the 1st LFNAT challenge on light field depth estimation, which aims at predicting disparity information of central view image in a light field (i.e., pixel offset between central view image and adjacent view image). Compared to multi-view stereo matching, light field depth estimation emphasizes efficient utilization of the 2D angular information from multiple regularly varying views. This challenge specifies UrbanLF [20] light field dataset as the sole data source. There are two phases in total: submission phase and final evaluation phase, in which 75 registered participants successfully submit their predicted results in the first phase and 7 eligible teams compete in the second phase. The performance of all submissions is carefully reviewed and shown in this paper as a new standard for the current state-of-the-art in light field depth estimation. Moreover, the implementation details of these methods are also provided to stimulate related advanced research. © 2023 ieee.

关键词： Stereo image processing

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Fast Solvers for Minimal Radial Distortion Relative Pose Problems

Fast Solvers for Minimal Radial Distortion Relative Pose Pro...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Oskarsson, Magnus Lund Univ Ctr Math Sci Lund Sweden

ISBN: (纸本)9781665448994

In this paper we present a unified formulation for a large class of relative pose problems with radial distortion and varying calibration. For minimal cases, we show that one can eliminate the number of parameters down to one to three. The relative pose can then be expressed using varying calibration constraints on the fundamental matrix, with entries that are polynomial in the parameters. We can then apply standard techniques based on the action matrix and Sturm sequences to construct our solvers. This enables efficient solvers for a large class of relative pose problems with radial distortion, using a common framework. We evaluate a number of these solvers for robust two-view inlier and epipolar geometry estimation, used as minimal solvers in RANSAC.

关键词： Geometry computer vision conferences Estimation Tools Distortion Cameras

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Modeling Fashion Compatibility with Explanation by using Bidirectional LSTM

Modeling Fashion Compatibility with Explanation by using Bid...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pang Kaicheng Zou Xingxing Wong, Wai Keung Hong Kong Polytech Univ Inst Text & Clothing Hong Kong Peoples R China Lab Artificial Intelligence Design Hong Kong Peoples R China

ISBN: (纸本)9781665448994

The goal of this paper is to model the fashion compatibility of an outfit and provide the explanations. We first extract features of all attributes of all items via convolutional neural networks, and then train the bidirectional Long Shortterm Memory (Bi-LSTM) model to learn the compatibility of an outfit by treating these attribute features as a sequence. Gradient penalty regularization is exploited for training inter-factor compatibility net which is used to compute the loss for judgment and provide its explanation which is generated from the recognized reasons related to the judgment. To train and evaluate the proposed approach, we expanded the EVALUATION3 dataset in terms of the number of items and attributes. Experiment results show that our approach can successfully evaluate compatibility with reason.

关键词： Training Industries computer vision Computational modeling conferences Feature extraction pattern recognition

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NTIRE 2022 Burst Super-Resolution Challenge

NTIRE 2022 Burst Super-Resolution Challenge

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Bhat, Goutam Danelljan, Martin Timofte, Radu Cao, Yizhen Cao, Yuntian Chen, Meiya Chen, Xihao Cheng, Shen Dudhane, Akshay Fan, Haoqiang Gang, Ruipeng Gao, Jian Gu, Yan Huang, Jie Huang, Liufeng Jo, Youngsu Kang, Sukju Khan, Salman Khan, Fahad Shahbaz Kondo, Yuki Li, Chenghua Li, Fangya Li, Jinjing Li, Youwei Li, Zechao Liu, Chenming Liu, Shuaicheng Liu, Zikun Liu, Zhuoming Luo, Ziwei Luo, Zhengxiong Mehta, Nancy Murala, Subrahmanyam Nam, Yoonchan Nakatani, Chihiro Ostyakov, Pavel Pan, Jinshan Song, Ge Sun, Jian Sun, Long Tang, Jinhui Ukita, Norimichi Wen, Zhihong Wu, Qi Wu, Xiaohe Xiao, Zeyu Xiong, Zhiwei Xu, Rongjian Xu, Ruikang Yan, Youliang Yang, Jialin Yang, Wentao Yang, Zhongbao Yasue, Fuma Yao, Mingde Yu, Lei Zhang, Cong Zamir, Syed Waqas Zhang, Jianxing Zhang, Shuohao Zhang, Zhilu Zheng, Qian Zhou, Gaofeng Zhussip, Magauiya Zou, Xueyi Zuo, Wangmeng Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Commun Univ China Beijing Peoples R China UHDTV Res & Applicat Lab Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Harbin Inst Technol Harbin Peoples R China South China Univ Technol Guangzhou Peoples R China Megvii Technol Beijing Peoples R China Univ Elect Sci & Technol China UESTC Chengdu Peoples R China Xiaomi Beijing Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China Nanjing Univ Sci & Technol Nanjing Peoples R China SRC B Beijing Peoples R China CASIA Beijing Peoples R China Indian Inst Technol Ropar IIT Ropar Rupnagar Punjab India Mohamed Bin Zayed Univ AI MBZUAI Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence IIAI Abu Dhabi U Arab Emirates MBZUAI Abu Dhabi U Arab Emirates Australian Natl Univ ANU Canberra ACT Australia Linkoping Univ Linkoping Sweden Toyota Technol Inst TTI Nagoya Aichi Japan Sogang Univ Seoul South Korea UESTC Chengdu Peoples R China USTC Hefei Peoples R China WHU Wuhan Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.

关键词： Training Photography Superresolution Noise reduction Network architecture Transformers pattern recognition

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