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Low Bandwidth Video-Chat Compression using Deep Generative Models

Low Bandwidth Video-Chat Compression using Deep Generative M...

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

作者： Oquab, Maxime Stock, Pierre Gafni, Oran Haziza, Daniel Xu, Tao Zhang, Peizhao Celebi, Onur Hasson, Yana Labatut, Patrick Bose-Kolanu, Bobo Peyronel, Thibault Couprie, Camille Facebook Menlo Pk CA 94025 USA INRIA Le Chesnay France

ISBN: (纸本)9781665448994

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3 MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.

关键词： Image segmentation Image coding Lips Bandwidth computer architecture Video compression Streaming media

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SaR: Self-adaptive Refinement on Pseudo Labels for Multiclass-Imbalanced Semi-supervised Learning

SaR: Self-adaptive Refinement on Pseudo Labels for Multiclas...

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

作者： Lai, Zhengfeng Wang, Chao Cheung, Sen-ching Chuah, Chen-Nee Univ Calif Davis Davis CA 95616 USA Southern Univ Sci & Technol Shenzhen Peoples R China Univ Kentucky Lexington KY 40506 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Class-imbalanced datasets can severely deteriorate the performance of semi-supervised learning (SSL). This is due to the confirmation bias especially when the pseudo labels are highly biased towards the majority classes. Traditional resampling or reweighting techniques may not be directly applicable when the unlabeled data distribution is unknown. Inspired by the threshold-moving method that performs well in supervised learning-based binary classification tasks, we provide a simple yet effective scheme to address the multiclass imbalance issue of SSL. This scheme, named SaR, is a Self-adaptive Refinement of soft labels before generating pseudo labels. The pseudo labels generated post-SaR will be less biased, resulting in higher quality data for training the classifier. We show that SaR can consistently improve recent consistency-based SSL algorithms on various image classification problems across different imbalanced ratios. We also show that SaR is robust to the situations where unlabeled data have different distributions as labeled data. Hence, SaR does not rely on the assumptions that unlabeled data share the same distribution as the labeled data.

关键词： Training computer vision conferences Semisupervised learning pattern recognition Classification algorithms Task analysis

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Learning Contextual Causality between Daily Events from Time-consecutive Images

Learning Contextual Causality between Daily Events from Time...

引用

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

作者： Zhang, Hongming Huo, Yintong Zhao, Xinran Song, Yangqiu Roth, Dan HKUST Hong Kong Peoples R China CUHK Hong Kong Peoples R China UPenn Philadelphia PA 19104 USA

ISBN: (纸本)9781665448994

Conventional textual-based causal knowledge acquisition methods typically require laborious and expensive human annotations. As a result, their scale is often limited. Moreover, as no context is provided during the annotation, the resulting causal knowledge records (e.g., ConceptNet) typically do not consider the context. In this paper, we move out of the textual domain to explore a more scalable way of acquiring causal knowledge and investigate the possibility of learning contextual causality from the visual signal. Specifically, we first propose a high-quality dataset Vis-Causal and then conduct experiments to demonstrate that with good language and visual representations, it is possible to discover meaningful causal knowledge from the videos. Further analysis also shows that the contextual property of causal relations indeed exists and considering the contextual property can help better predict the causal relation between events. The Vis-Causal dataset and experiment code are available at https://***/HKUST-KnowComp/Vis_Causal.

关键词： Training Visualization computer vision Annotations Knowledge acquisition conferences pattern recognition

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On the Robustness of Monte Carlo Dropout Trained with Noisy Labels

On the Robustness of Monte Carlo Dropout Trained with Noisy ...

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

作者： Goel, Purvi Li Chen Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

The memorization effect of deep learning hinders its performance to effectively generalize on test set when learning with noisy labels. Prior study has discovered that epistemic uncertainty techniques are robust when trained with noisy labels compared with neural networks without uncertainty estimation. They obtain prolonged memorization effect and better generalization performance under the adversarial setting of noisy labels. Due to its superior performance amongst other selected epistemic uncertainty methods under noisy labels, we focus on Monte Carlo Dropout (MCDropout) and investigate why it is robust when trained with noisy labels. Through empirical studies on datasets MNIST, CIFAR-10, Animal-10n, we deep dive into three aspects of MCDropout under noisy label setting: 1. efficacy: understanding the learning behavior and test accuracy of MCDropout when training set contains artificially generated or naturally embedded label noise;2. representation volatility: studying the responsiveness of neurons by examining the mean and standard deviation on each neuron's activation;3. network sparsity: investigating the network support of MCDropout in comparison with deterministic neural networks. Our findings suggest that MCDropout further sparsifies and regularizes the deterministic neural networks and thus provides higher robustness against noisy labels.

关键词： Training Uncertainty Monte Carlo methods Neurons Estimation Robustness pattern recognition

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Pseudo-label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

Pseudo-label Generation and Various Data Augmentation for Se...

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

作者： Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Yu, Ye Wang, Lei Ling, Qiang Univ Sci & Technol China Hefei Anhui Peoples R China Hefei ZhanDa Intelligence Technol Co Ltd Hefei Anhui Peoples R China Hefei Univ Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning is a highly researched problem, but existing semi-supervised object detection frameworks are based on RGB images, and existing pre-trained models cannot be used for hyperspectral images. To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images. Next, in order to make full use of the unlabeled training set, we generate pseudo-labels with the model trained in the first stage and mix the obtained pseudo-labels with the labeled training set. Then, a large number of strong data augmentation methods are added to make the final model better. We achieve the SOTA, with an AP of 26.35, on the Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC) in the CVPR 2022 Perception Beyond the Visible Spectrum Workshop, and win the first place in this Challenge.

关键词： Training computer vision conferences Object detection Semisupervised learning Data models pattern recognition

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Ternary Feature Masks: zero-forgetting for task-incremental learning

Ternary Feature Masks: zero-forgetting for task-incremental ...

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

作者： Masana, Marc Tuytelaars, Tinne Van de Weijer, Joost Comp Vis Ctr Barcelona Spain Katholieke Univ Leuven ESAT PSI Leuven Belgium

ISBN: (纸本)9781665448994

We propose an approach without any forgetting to continual learning for the task-aware regime, where at inference the task-label is known. By using ternary masks we can upgrade a model to new tasks, reusing knowledge from previous tasks while not forgetting anything about them. Using masks prevents both catastrophic forgetting and backward transfer. We argue - and show experimentally - that avoiding the former largely compensates for the lack of the latter, which is rarely observed in practice. In contrast to earlier works, our masks are applied to the features (activations) of each layer instead of the weights. This considerably reduces the number of mask parameters for each new task;with more than three orders of magnitude for most networks. The encoding of the ternary masks into two bits per feature creates very little overhead to the network, avoiding scalability issues. To allow already learned features to adapt to the current task without changing the behavior of these features for previous tasks, we introduce task-specific feature normalization. Extensive experiments on several finegrained datasets and ImageNet show that our method outperforms current state-of-the-art while reducing memory overhead in comparison to weight-based approaches.

关键词： computer vision Scalability conferences Encoding pattern recognition Computational efficiency Task analysis

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KCNN: Extremely-Efficient Hardware Keypoint Detection with a Compact Convolutional Neural Network 31

KCNN: Extremely-Efficient Hardware Keypoint Detection with a...

引用

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

作者： Di Febbo, Paolo Dal Mutto, Carlo Tieu, Kinh Mattoccia, Stefano Aquifi Inc Palo Alto CA 94303 USA Univ Bologna Bologna Italy

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Keypoint detection algorithms are typically based on handcrafted combinations of derivative operations implemented with standard image filtering approaches. The early layers of Convolutional Neural Networks (CNNs) for image classification, whose implementation is nowadays often available within optimized hardware units, are characterized by a similar architecture. Therefore, the exploration of CNNs for keypoint detection is a promising avenue to obtain a low-latency implementation, also enabling to effectively move the computational cost of the detection to dedicated Neural Network processing units. This paper proposes a methodology for effective keypoint detection by means of an efficient CNN characterized by a compact three-layer architecture. A novel training procedure is proposed for learning values of the network parameters which allow for an approximation of the response of handcrafted detectors, showing that the proposed architecture is able to obtain results comparable with the state of the art. The capability of emulating different detectors allows to deploy a variety of algorithms to dedicated hardware by simply retraining the network. A sensor-based FPGA implementation of the introduced CNN architecture is presented, allowing latency smaller than 1[ms].

关键词： Detectors Training computer architecture Hardware Field programmable gate arrays Convolution Complexity theory

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Multi-modal Aerial View Object Classification Challenge Results - PBVS 2022

Multi-modal Aerial View Object Classification Challenge Resu...

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

作者： Low, Spencer Nina, Oliver Sappa, Angel D. Blasch, Erik Brigham Young Univ Provo UT 84602 USA Air Force Res Lab Dayton OH USA ESPOL Polytech Univ Ecuador Comp Vision Ctr Guayaquil Ecuador Air Force Off Sci Res Arlington VA USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This paper details the results and main findings of the second iteration of the Multi-modal Aerial View Object Classification (MAVOC) challenge. The primary goal of both MAVOC challenges is to inspire research into methods for building recognition models that utilize both synthetic aperture radar (SAR) and electro-optical (EO) imagery. Teams are encouraged to develop multi-modal approaches that incorporate complementary information from both domains. While the 2021 challenge showed a proof of concept that both modalities could be used together, the 2022 challenge focuses on the detailed multi-modal methods. The 2022 challenge uses the same UNIfied COincident Optical and Radar for recognition (UNICORN) dataset and competition format that was used in 2021. Specifically, the challenge focuses on two tasks, (1) SAR classification and (2) SAR + EO classification. The bulk of this document is dedicated to discussing the top performing methods and describing their performance on our blind test set. Notably, all of the top ten teams outperform a Resnet-18 baseline. For SAR classification, the top team showed a 129% improvement over baseline and an 8% average improvement from the 2021 winner. The top team for SAR + EO classification shows a 165% improvement with a 32% average improvement over 2021.

关键词： Training Laser radar Image recognition Radar imaging Optical imaging pattern recognition Task analysis

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QFabric: Multi-Task Change Detection Dataset

QFabric: Multi-Task Change Detection Dataset

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

作者： Verma, Sagar Panigrahi, Akash Gupta, Siddharth Univ Paris Saclay INRIA Cent Supelec Ctr Vis Numer Paris France Granular AI Somerville NJ USA

ISBN: (纸本)9781665448994

Detecting change through multi-image, multi-date remote sensing is essential to developing and understanding of global conditions. Despite recent advancements in remote sensing realized through deep learning, novel methods for accurate multi-image change detection remain unrealized. Recently, several promising methods have been proposed to address this topic, but a paucity of publicly available data limits the methods that can be assessed. In particular, there exists limited work on categorizing the nature and status of change across an observation period. This paper introduces the first labeled dataset available for such a task. We present an open-source change detection dataset, termed QFabric, with 450,000 change polygons annotated across 504 locations in 100 different cities covering a wide range of geographies and urban fabrics. QFabric is a temporal multi-task dataset with 6 change types and 9 change status classes. The geography and environment metadata around each polygon provides context that can be leveraged to build robust deep neural networks. We apply multiple benchmarks on our dataset for change detection, change type and status classification tasks. Project page: https://***/qfabric

关键词： Deep learning Urban areas Predictive models Benchmark testing Metadata Fabrics pattern recognition

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A Train Station Surveillance System: Challenges and Solutions

A Train Station Surveillance System: Challenges and Solution...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Ozer, Burak Wolf, Marilyn Perkasie United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781479943098

In this paper, we describe our multi-year development effort to create a smart-camera surveillance system for use in train stations. We address the problem of activity analysis in very crowded areas and offer solutions to the detection/tracking problems in one of the most crowded environments. The smart camera system is installed in several train stations around Tokyo, Japan and is currently deployed analyzing crowd movement and individual gestures of the passengers in the stations. The paper summarizes the technical problems we faced during our research /development stages as well as during system deployment. The hierarchical smart camera system, which can detect the overall crowd movement in the higher level and individual activities in the lower level, is developed by Verificon Corp., a Princeton University startup company. We developed smart camera algorithms and spent several years commercializing the technology in a joint project with Yokogawa Electric. A railway company was the lead customer on this project. Verificon system passed several rounds of testing, running for several months on live data in several Tokyo train stations and was deployed by the railway company. This project has led to several extensions of the original technology.

关键词： surveillance tracking gesture recognitionin

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