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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

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

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

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What Affects Learned Equivariance in Deep Image recognition Models?

What Affects Learned Equivariance in Deep Image Recognition ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Robert-Jan Bruintjes Tomasz Motyka Jan van Gemert Computer Vision Lab Delft University of Technology Synerise

Equivariance w.r.t. geometric transformations in neural networks improves data efficiency, parameter efficiency and robustness to out-of-domain perspective shifts. When equivariance is not designed into a neural network, the network can still learn equivariant functions from the data. We quantify this learned equivariance, by proposing an improved measure for equivariance. We find evidence for a correlation between learned translation equivariance and validation accuracy on ImageNet. We therefore investigate what can increase the learned equivariance in neural networks, and find that data augmentation, reduced model capacity and inductive bias in the form of convolutions induce higher learned equivariance in neural networks.

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Combining Magnification and Measurement for Non-Contact Cardiac Monitoring

Combining Magnification and Measurement for Non-Contact Card...

引用

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

作者： Nowara, Ewa M. McDuff, Daniel Veeraraghavan, Ashok Rice Univ Houston TX 77251 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781665448994

Deep learning approaches currently achieve the state-of-the-art results on camera-based vital signs measurement. One of the main challenges with using neural models for these applications is the lack of sufficiently large and diverse datasets. Limited data increases the chances of overfitting models to the available data which in turn can harm generalization. In this paper, we show that the generalizability of imaging photoplethysmography models can be improved by augmenting the training set with "magnified" videos. These augmentations are specifically designed to reveal useful features for recovering the photoplethysmogram. We show that using augmentations of this form is more effective at improving model robustness than other commonly used data augmentation approaches. We show better within-dataset and especially cross-dataset performance with our proposed data augmentation approach on three publicly available datasets.

关键词： Training Deep learning Photoplethysmography Data models Skin Robustness pattern recognition

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Generalizable Multi-Camera 3D Pedestrian Detection

Generalizable Multi-Camera 3D Pedestrian Detection

引用

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

作者： Lima, Joao Paulo Roberto, Rafael Figueiredo, Lucas Simoes, Francisco Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Univ Fed Rural Pernambuco Dept Comp Recife PE Brazil

ISBN: (纸本)9781665448994

We present a multi-camera 3D pedestrian detection method that does not need to train using data from the target scene. We estimate pedestrian location on the ground plane using a novel heuristic based on human body poses and person's bounding boxes from an off-the-shelf monocular detector. We then project these locations onto the world ground plane and fuse them with a new formulation of a clique cover problem. We also propose an optional step for exploiting pedestrian appearance during fusion by using a domain-generalizable person re-identification model. We evaluated the proposed approach on the challenging WILDTRACK dataset. It obtained a MODA of 0.569 and an F-score of 0.78, superior to state-of-the-art generalizable detection techniques.

关键词： Training computer vision Three-dimensional displays Sensitivity Fuses conferences Pose estimation

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DeepObjStyle: Deep Object-based Photo Style Transfer

DeepObjStyle: Deep Object-based Photo Style Transfer

引用

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

作者： Mastan, Indra Deep Raman, Shanmuganathan Indian Inst Technol Gandhinagar Gandhinagar Gujarat India

ISBN: (纸本)9781665448994

One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input images (style and content). An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.

关键词： Training Visualization computer vision conferences Semantics Tag clouds Quality assessment

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Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P–Events to Polarization Reconstruction from P...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tobi Delbruck Zuowen Wang Haiyang Mei Germain Haessig Damien Joubert Justin Haque Yingkai Chen Moritz B. Milde Viktor Gruev Sensors Group Institute of Neuroinformatics Univ. of Zurich and ETH Zurich Switzerland AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Intl. Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [...

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TAEN: Temporal Aware Embedding Network for Few-Shot Action recognition

TAEN: Temporal Aware Embedding Network for Few-Shot Action R...

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

作者： Ben-Ari, Rami Nacson, Mor Shpigel Azulai, Ophir Barzelay, Udi Rotman, Daniel OriginAI Haifa Israel Technion Haifa Israel IBM Res AI Haifa Israel

ISBN: (纸本)9781665448994

Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.

关键词： Training computer vision conferences Semantics Benchmark testing Extraterrestrial measurements pattern recognition

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Interpolation-Based Event Visual Data Filtering Algorithms

Interpolation-Based Event Visual Data Filtering Algorithms

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Marcin Kowalczyk Tomasz Kryjak Embedded Vision Systems Group AGH University of Krakow

The field of neuromorphic vision is developing rapidly, and event cameras are finding their way into more and more applications. However, the data stream from these sensors is characterised by significant noise. In this paper, we propose a method for event data that is capable of removing approximately 99% of noise while preserving the majority of the valid signal. We have proposed four algorithms based on the matrix of infinite impulse response (IIR) filters method. We compared them on several event datasets that were further modified by adding artificially generated noise and noise recorded with dynamic vision sensor. The proposed methods use about 30KB of memory for a sensor with a resolution of 1280 × 720 and is therefore well suited for implementation in embedded devices.

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

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

作者： Yang, Ren Timofte, Radu Zheng, Meisong Xing, Qunliang Qiao, Minglang Xu, Mai Jiang, Lai Liu, Huaida Chen, Ying Ben, Youcheng Zhou, Xiao Fu, Chen Cheng, Pei Yu, Gang Li, Junyi Wu, Renlong Zhang, Zhilu Shang, Wei Lv, Zhengyao Chen, Yunjin Zhou, Mingcai Ren, Dongwei Zhang, Kai Zuo, Wangmeng Ostyakov, Pavel Dmitry, Vyal Soltanayev, Shakarim Sergey, Chervontsev Magauiya, Zhussip Zou, Xueyi Yan, Youliang Michelini, Pablo Navarrete Lu, Yunhua Zhang, Diankai Liu, Shaoli Gao, Si Wu, Biao Zheng, Chengjian Zhang, Xiaofeng Lu, Kaidi Wang, Ning Thuong Nguyen Canh Bach, Thong Wang, Qing Sun, Xiaopeng Ma, Haoyu Zhao, Shijie Li, Junlin Xie, Liangbin Shi, Shuwei Yang, Yujiu Wang, Xintao Gu, Jinjin Dong, Chao Shi, Xiaodi Nian, Chunmei Jiang, Dong Lin, Jucai Xie, Zhihuai Ye, Mao Luo, Dengyan Peng, Liuhan Chen, Shengjie Liu, Xin Wang, Qian Liang, Boyang Dong, Hang Huang, Yuhao Chen, Kai Guo, Xingbei Sun, Yujing Wu, Huilei Wei, Pengxu Huang, Yulin Chen, Junying Lee, Ik Hyun Khowaja, Sunder Ali Yoon, Jiseok Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Julius Maximilian Univ Wurzburg Wurzburg Germany Alibaba Grp Dept Tao Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China Tencent Shenzhen Peoples R China Harbin Inst Technol Harbin Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China BOE Technol Grp Co Ltd Beijing Peoples R China ZTE Audio & Video Technol Platform Dept Nanjing Peoples R China Osaka Univ Osaka Japan ByteDance Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen Peoples R China Hangzhou Univ Elect Sci & Technol Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Xinjiang Univ Urumqi Xinjiang Peoples R China China Mobile Res Inst Beijing Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Peking Univ Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China South China Univ Technol Guangzhou Peoples R China Tech Univ Korea Siheung Si South Korea Univ Sindh Sindh Pakistan IKLAB New York NY USA

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the superresolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https:// ***/RenYanghome/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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Boosting Adversarial Robustness using Feature Level Stochastic Smoothing

Boosting Adversarial Robustness using Feature Level Stochast...

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

作者： Addepalli, Sravanti Jain, Samyak Sriramanan, Gaurang Babu, R. Venkatesh Indian Inst Sci Video Analyt Lab Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781665448994

Advances in adversarial defenses have led to a significant improvement in the robustness of Deep Neural Networks. However, the robust accuracy of present state-of-the-art defenses is far from the requirements in critical applications such as robotics and autonomous navigation systems. Further, in practical use cases, network prediction alone might not suffice, and assignment of a confidence value for the prediction can prove crucial. In this work, we propose a generic method for introducing stochasticity in the network predictions, and utilize this for smoothing decision boundaries and rejecting low confidence predictions, thereby boosting the robustness on accepted samples. The proposed Feature Level Stochastic Smoothing based classification also results in a boost in robustness without rejection over existing adversarial training methods. Finally, we combine the proposed method with adversarial detection methods, to achieve the benefits of both approaches.

关键词： Training Deep learning computer vision Smoothing methods conferences Boosting Feature extraction

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