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Skeletor: Skeletal Transformers for Robust Body-Pose Estimation

Skeletor: Skeletal Transformers for Robust Body-Pose Estimat...

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

作者： Jiang, Tao Camgoz, Necati Cihan Bowden, Richard Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England

ISBN: (纸本)9781665448994

Predicting 3D human pose from a single monoscopic video can be highly challenging due to factors such as low resolution, motion blur and occlusion, in addition to the fundamental ambiguity in estimating 3D from 2D. Approaches that directly regress the 3D pose from independent images can be particularly susceptible to these factors and result in jitter, noise and/or inconsistencies in skeletal estimation. Much of which can be overcome if the temporal evolution of the scene and skeleton are taken into account. However, rather than tracking body parts and trying to temporally smooth them, we propose a novel transformer based network that can learn a distribution over both pose and motion in an unsupervised fashion. We call our approach Skeletor. Skeletor overcomes inaccuracies in detection and corrects partial or entire skeleton corruption. Skeletor uses strong priors learn from on 25 million frames to correct skeleton sequences smoothly and consistently. Skeletor can achieve this as it implicitly learns the spatio-temporal context of human motion via a transformer based neural network. Extensive experiments show that Skeletor achieves improved performance on 3D human pose estimation and further provides benefits for downstream tasks such as sign language translation.

关键词： Training Three-dimensional displays Tracking Assistive technology Pose estimation Neural networks Gesture recognition

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ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation

ASMNet: a Lightweight Deep Neural Network for Face Alignment...

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

作者： Fard, Ali Pourramezan Abdollahi, Hojjat Mahoor, Mohammad Univ Denver Dept Elect & Comp Engn Denver CO 80210 USA

ISBN: (纸本)9781665448994

Active Shape Model (ASM) is a statistical model of object shapes that represents a target structure. ASM can guide machine learning algorithms to fit a set of points representing an object (e.g., face) onto an image. This paper presents a lightweight Convolutional Neural Network (CNN) architecture with a loss function being assisted by ASM for face alignment and estimating head pose in the wild. We use ASM to first guide the network towards learning a smoother distribution of the facial landmark points. Inspired by transfer learning, during the training process, we gradually harden the regression problem and guide the network towards learning the original landmark points distribution. We define multi-tasks in our loss function that are responsible for detecting facial landmark points as well as estimating the face pose. Learning multiple correlated tasks simultaneously builds synergy and improves the performance of individual tasks. We compare the performance of our proposed model called ASMNet with MobileNetV2 (which is about 2 times bigger than ASMNet) in both the face alignment and pose estimation tasks. Experimental results on challenging datasets show that by using the proposed ASM assisted loss function, the ASMNet performance is comparable with MobileNetV2 in the face alignment task. In addition, for face pose estimation, ASMNet performs much better than MobileNetV2. ASMNet achieves an acceptable performance for facial landmark points detection and pose estimation while having a significantly smaller number of parameters and floating-point operations compared to many CNN-based models.

关键词： Training Machine learning algorithms Shape Face recognition Pose estimation Transfer learning Magnetic heads

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Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration

Know Your Surroundings: Panoramic Multi-Object Tracking by M...

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

作者： He, Yuhang Yu, Wentao Han, Jie Wei, Xing Hong, Xiaopeng Gong, Yihong Xi An Jiao Tong Univ Coll Artificial Intelligence Xian Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781665448994

In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation. Most existing MOT methods track multiple objects using a singular RGB camera, which are prone to camera field-of-view and suffer tracking failures in complex scenarios due to background clutters and poor light conditions. To meet these challenges, we propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT), which takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. The proposed method contains four major modules, a panorama image detection module, a multimodality data fusion module, a data association module and a trajectory inference model. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks and significantly outperforms state-of-the-art methods by a large margin (15.7 and 8.5 improvement in terms of AP and MOTA, respectively).

关键词： Three-dimensional displays Target tracking Navigation Robot vision systems Object detection Cameras Data models

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Weak Multi-View Supervision for Surface Mapping Estimation

Weak Multi-View Supervision for Surface Mapping Estimation

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

作者： Rai, Nishant Liaudanskas, Aidas Rao, Srinivas Cayon, Rodrigo Ortiz Munaro, Matteo Holzer, Stefan Fyusion Inc San Francisco CA 94105 USA Stanford Univ Stanford CA 94305 USA Fyusion San Francisco CA USA

ISBN: (纸本)9781665448994

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations. We learn the underlying surface geometry of common categories, such as human faces, cars, and airplanes, given instances from those categories. While traditional approaches solve this problem using extensive supervision in the form of pixel-level annotations, we take advantage of the fact that pixel-level UV and mesh predictions can be combined with 3D reprojections to form consistency cycles. As a result of exploiting these cycles, we can establish a dense correspondence mapping between image pixels and the mesh acting as a self-supervisory signal, which in turn helps improve our overall estimates. Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding without the need for explicit annotations. We also propose the use of deformation fields for predictions of an instance specific mesh. Given the lack of datasets providing multiple images of similar object instances from different viewpoints, we generate and release a multi-view ShapeNet Cars and Airplanes dataset created by rendering ShapeNet meshes using a 360ffi camera trajectory around the mesh. For the human faces category, we process and adapt an existing dataset to a multi-view setup. Through experimental evaluations, we show that, at test time, our method can generate accurate variations away from the mean shape, is multi-view consistent, and performs comparably to fully supervised approaches.

关键词： Deformable models Surface reconstruction Airplanes Annotations Rendering (computer graphics) Trajectory Automobiles

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Self-supervised multi-image super-resolution for push-frame satellite images

Self-supervised multi-image super-resolution for push-frame ...

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

作者： Ngoc Long Nguyen Anger, Jeremy Davy, Axel Arias, Pablo Facciolo, Gabriele Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS Gif Sur Yvette France Kayrros SAS Paris France

ISBN: (纸本)9781665448994

Recent constellations of optical satellites are adopting multi-image super-resolution (MISR) from bursts of push-frame images as a way to increase the resolution and reduce the noise of their products while maintaining a lower cost of operation. Most MISR techniques are currently based on the aggregation of samples from registered low resolution images. A promising research trend aimed at incorporating natural image priors in MISR consists in using data-driven neural networks. However, due to the unavailability of ground truth high resolution data, these networks cannot be trained on real satellite images. In this paper, we present a framework for training MISR algorithms from bursts of satellite images without requiring high resolution ground truth. This is achieved by adapting the recently proposed frame-to-frame framework to process bursts of satellite images. In addition we propose an architecture based on feature aggregation that allows to fuse a variable number of frames and is capable of handling degenerate samplings while also reducing noise. On synthetic datasets, the proposed self-supervision strategy attains results on par with those obtained with a supervised training. We applied our framework to real SkySat satellite image bursts leading to results that are more resolved and less noisy than the L1B product from Planet.

关键词： Training Satellites Planets Superresolution Neural networks computer architecture Optical imaging

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Isolated Sign Language recognition with Multi-Scale Spatial-Temporal Graph Convolutional Networks

Isolated Sign Language Recognition with Multi-Scale Spatial-...

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

作者： Vazquez-Enriquez, Manuel Alba-Castro, Jose L. Docio-Fernandez, Laura Rodriguez-Banga, Eduardo Univ Vigo AtlanTTic Res Ctr Vigo 36310 Spain

ISBN: (纸本)9781665448994

Isolated Sign Language recognition (ISLR) fits nicely in the domain of problems that can be handled by graph-structured spatial-temporal algorithms. A recent multi-scale spatial-temporal graph convolution operator, MS-G3D, takes advantage of the semantic connectivity among non-neighbor nodes of the graph in a flexible temporal scale, which results in improved performance in classical Human Action recognition datasets. In this work, we present a solution for ISLR using a skeleton graph that includes body and finger joints and makes use of this specific property of MS-G3D, which seems crucial to capture the internal relationship among semantically connected distant nodes in sign language dynamics. To complete the analysis, we compare the results with a 3D-CNN architecture, S3D, already used for SLR, and fuse it with MS-G3D. The performance achieved on the AUTSL dataset shows that MS-G3D alone stands out as a viable technique for ISLR. In fact, the improvement after fusing with a 3D-CNN approach, at least on this medium-scale dataset, appears marginal. The transfer learning capability of the trained models is also explored using pre-training with the larger WLASL dataset and post-training with the smaller LSE_UVIGO dataset. The classification performance based on the MS-G3D model over AUTSL does not benefit from pre-training with WLASL, but the performance on the more similarly acquired LSE UVIGO dataset improves significantly from fine-tuning the MS-G3D AUTSL model.

关键词： computer vision Fuses Convolution Assistive technology Computational modeling conferences Transfer learning

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Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report

Fast Camera Image Denoising on Mobile GPUs with Deep Learnin...

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

作者： Ignatov, Andrey Byeoung-Su, Kim Timofte, Radu Pouget, Angeline Song, Fenglong Li, Cheng Xiao, Shuai Fu, Zhongqian Maggioni, Matteo Huang, Yibin Cheng, Shen Lu, Xin Zhou, Yifeng Chen, Liangyu Liu, Donghao Zhang, Xiangyu Fan, Haoqiang Sun, Jian Liu, Shuaicheng Kwon, Minsu Lee, Myungje Yoo, Jaeyoon Kang, Changbeom Wang, Shinjo Huang, Bin Zhou, Tianbao Liu, Shuai Lei, Lei Feng, Chaoyu Huang, Liguang Lei, Zhikun Chen, Feifei Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Samsung Elect Suwon South Korea AI Witchlabs Zollikerberg Switzerland Huawei Noahs Ark Lab Hong Kong Peoples R China Megvii Beijing Peoples R China ENERZAi Seoul South Korea Xiaomi AI Lab Beijing Peoples R China North China Univ Technol Beijing Peoples R China Xiaomi Inc Beijing Peoples R China Goodix Technol Algorithm Dept Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data and are too computationally expensive to run on mobile devices. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs. For this, the participants were provided with a novel large-scale dataset consisting of noisy-clean image pairs captured in the wild. The runtime of all models was evaluated on the Samsung Exynos 2100 chipset with a powerful Mali GPU capable of accelerating floating-point and quantized neural networks. The proposed solutions are fully compatible with any mobile GPU and are capable of processing 480p resolution images under 40-80 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

关键词： Runtime Image resolution Neural networks Graphics processing units Mobile handsets pattern recognition Noise measurement

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SrvfRegNet: Elastic Function Registration Using Deep Neural Networks

SrvfRegNet: Elastic Function Registration Using Deep Neural ...

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

作者： Chen, Chao Srivastava, Anuj Florida State Univ Dept Stat Tallahassee FL 32306 USA

ISBN: (纸本)9781665448994

Registering functions (curves) using time warpings (re-parameterizations) is central to many computer vision and shape analysis solutions. While traditional registration methods minimize penalized-L-2 norm, the elastic Riemannian metric and square-root velocity functions (SRVFs) have resulted in significant improvements in terms of theory and practical performance. This solution uses the dynamic programming algorithm to minimize the L-2 norm between SRVFs of given functions. However, the computational cost of this elastic dynamic programming framework - O(nT(2)k) - where T is the number of time samples along curves, n is the number of curves, and k < T is a parameter - limits its use in applications involving big data. This paper introduces a deep-learning approach, named SRVF Registration Net or SrvfRegNet to overcome these limitations. SrvfRegNet architecture trains by optimizing the elastic metric-based objective function on the training data and then applies this trained network to the test data to perform fast registration. In case the training and the test data are from different classes, it generalizes to the test data using transfer learning, i.e., retraining of only the last few layers of the network. It achieves the state-of-the-art alignment performance albeit at much reduced computational cost. We demonstrate the efficiency and efficacy of this framework using several standard curve datasets.

关键词： Training Measurement computer vision Shape Transfer learning Training data Dynamic programming

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Exploring Large-scale Unlabeled Faces to Enhance Facial Expression recognition

Exploring Large-scale Unlabeled Faces to Enhance Facial Expr...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Jun Yu Zhongpeng Cai Renda Li Gongpeng Zhao Guochen Xie Jichao Zhu Wangyuan Zhu Qiang Ling Lei Wang Cong Wang Luyu Qiu Wei Zheng University of Science and Technology of China Huawei Techologies

Facial Expression recognition (FER) is an important task in computer vision and has wide applications in many fields. In this paper, we introduce our approach to the fifth Affective Behavior Analysis in-the-wild (ABAW) Competition which will be held in cvpr20223. For facial expression recognition task, there is an urgent need to solve the problem that the limited size of FER datasets limits the generalization ability of expression recognition models, resulting in ineffective performance. To address this problem, we propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module (DTM) that can adaptively adjust the confidence threshold to fully utilize the face recognition (FR) data to generate pseudo-labels, thus improving the model’s ability to model facial expressions. In the 5th ABAW Expression Classification Challenge, our method achieves good results on the Aff-Wild2 validation and test sets, demonstrating that large scale unlabeled faces can indeed improve the performance of face expression recognition.

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CSANet: High Speed Channel Spatial Attention Network for Mobile ISP

CSANet: High Speed Channel Spatial Attention Network for Mob...

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

作者： Hsyu, Ming-Chun Liu, Chih-Wei Chen, Chao-Hung Chen, Chao-Wei Tsai, Wen-Chia Ind Technol Res Inst Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781665448994

The Image Signal Processor (ISP) is a customized device to restore RGB images from the pixel signals of CMOS image sensor. In order to realize this function, a series of processing units are leveraged to tackle different artifacts, such as color shifts, signal noise, moire effects, and so on, that are introduced from the photo-capturing devices. However, tuning each processing unit is highly complicated and requires a lot of experience and effort from image experts. In this paper, a novel network architecture, CSANet, with emphases on inference speed and high PSNR is proposed for end-to-end learned ISP task. The proposed CSANet applies a double attention module employing both channel and spatial attentions. Particularly, its spatial attention is simplified to a light-weighted dilated depth-wise convolution and still performs as well as others. As proof of performance, CSANet won 2nd place in the Mobile AI 2021 Learned Smartphone ISP Challenge with 1st place PSNR score.

关键词： Performance evaluation Runtime Pipelines Network architecture Service-oriented architecture pattern recognition Image restoration

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