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Lifting Monocular Events to 3D Human Poses

Lifting Monocular Events to 3D Human Poses

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

作者： Scarpellini, Gianluca Morerio, Pietro Del Bue, Alessio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Univ Genoa Genoa Italy Ist Italiano Tecnol Visual Geometry & Modelling Genoa Italy

ISBN: (纸本)9781665448994

This paper presents a novel 3D human pose estimation approach using a single stream of asynchronous events as input. Most of the state-of-the-art approaches solve this task with RGB cameras, however struggling when subjects are moving fast. On the other hand, event-based 3D pose estimation benefits from the advantages of event-cameras, especially their efficiency and robustness to appearance changes. Yet, finding human poses in asynchronous events is in general more challenging than standard RGB pose estimation, since little or no events are triggered in static scenes. Here we propose the first learning-based method for 3D human pose from a single stream of events. Our method consists of two steps. First, we process the event-camera stream to predict three orthogonal heatmaps per joint;each heatmap is the projection of of the joint onto one orthogonal plane. Next, we fuse the sets of heatmaps to estimate 3D localisation of the body joints. As a further contribution, we make available a new, challenging dataset for event-based human pose estimation by simulating events from the RGB Human3.6m dataset. Experiments demonstrate that our method achieves solid accuracy, narrowing the performance gap between standard RGB and event-based vision. The code is freely available at https://***/lifting_events_to_3d_hpe.

关键词： Heating systems Learning systems Three-dimensional displays Fuses Pose estimation Solids Robustness

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Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification

Phenology Alignment Network: A Novel Framework for Cross-Reg...

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

作者： Wang, Ziqiao Zhang, Hongyan He, Wei Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China RIKEN Ctr Adv Intelligence Project AIP Tokyo 1030027 Japan

ISBN: (纸本)9781665448994

Timely and accurate crop type classification plays an essential role in the study of agricultural application. However, large area or cross-regional crop classification confronts huge challenges owing to dramatic phenology discrepancy among training and test regions. In this work, we propose a novel framework to address these challenges based on deep recurrent network and unsupervised domain adaptation (DA). Specifically, we firstly propose a Temporal Spatial Network (TSNet) for pixelwise crop classification, which contains stacked RNN and self-attention module to adaptively extract multi-level features from crop samples under various planting conditions. To deal with the cross-regional challenge, an unsupervised DA-based framework named Phenology Alignment Network (PAN) is proposed. PAN consists of two branches of two identical TSNet pre-trained on source domain;one branch takes source samples while the other takes target samples as input. Through aligning the hierarchical deep features extracted from two branches, the discrepancy between two regions is decreased and the pre-trained model is adapted to the target domain without using target label information. As another contribution, a time series dataset based on Sentinel-2 was annotated containing winter crop samples collected on three study sites of China. Cross-regional experiments demonstrate that TSNet shows comparable accuracy to state-of-the-art methods, and PAN further improves the overall accuracy by 5.62%, and macro average F1 score by 0.094 unsupervisedly.

关键词： Training computer vision Adaptation models conferences Time series analysis Feature extraction Agriculture

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Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection

Pseudo-IoU: Improving Label Assignment in Anchor-Free Object...

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

作者： Li, Jiachen Cheng, Bowen Feris, Rogerio Xiong, Jinjun Huang, Thomas S. Hwu, Wen-Mei Shi, Humphrey UIUC Champaign IL 61820 USA MIT IBM Watson AI Lab Cambridge MA USA IBM TJ Watson Res Ctr Ossining NY USA NVIDIA Santa Clara CA USA Univ Oregon Eugene OR 97403 USA Picsart AI Res PAIR Champaign IL USA

ISBN: (纸本)9781665448994

Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods, which limits their potential in competing with classic anchor-based models that are supported by well-designed assignment methods based on the Intersection-over-Union (IoU) metric. In this paper, we present Pseudo-Intersection-over-Union (Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks without any additional computational cost or extra parameters for training and testing, making it possible to further improve anchor-free object detection by utilizing training samples of good quality under effective assignment rules that have been previously applied in anchor-based methods. By incorporating Pseudo-IoU metric into an end-to-end single-stage anchor-free object detection framework, we observe consistent improvements in their performance on general object detection benchmarks such as PASCAL VOC and MSCOCO. Our method (single-model and single-scale) also achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles. Our code is based on mmdetection toolbox and will be made publicly available at https://***/SHILabs/Pseudo-IoU-for-Anchor-Free-Object-Detection.

关键词： Measurement Training Location awareness computer vision Computational modeling conferences Object detection

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Geometric empirical Bayesian model for classification of functional data under diverse sampling regimes

Geometric empirical Bayesian model for classification of fun...

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

作者： Matuk, James Bharath, Karthik Chkrebtii, Oksana Kurtek, Sebastian Ohio State Univ Columbus OH 43210 USA Univ Nottingham Nottingham England

ISBN: (纸本)9781665448994

Functional data analysis (FDA) is focused on various statistical tasks, including inference, for observations that vary over a continuum, which are not effectively addressed by multivariate methods. A feature of these functional observations is the presence of two distinct forms of variability: amplitude that describes differences in magnitudes of features, e.g., extrema, and phase that describes differences in timings of amplitude features. One area of focus in FDA is the classification of new observations based on previously observed training data that has been split into pre-defined classes. Existing methods fail to directly account for both phase and amplitude variability, and work under the restrictive assumption that functional observations are measured on a common, fine grid over the input domain. In this work, we address these issues directly by formulating a Bayesian hierarchical model for irregular, fragmented or sparsely sampled functional observations, where training data from different classes are available. Our approach builds on a recently developed inferential framework for incomplete functional observations and the elastic FDA framework for characterizing amplitude and phase variability. The approach operates by inferring individual parameters that separately track amplitude and phase, which can be combined to infer complete functions underlying each observation, and a class parameter, which can be used to discern the class membership of an observation based on the training data. We validate the proposed framework using simulation studies and real data applications, and showcase the advantages of this perspective when both amplitude and phase variability are present in the data.

关键词： computer vision Phase measurement Computational modeling conferences Training data Data models Bayes methods

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Boosting Co-teaching with Compression Regularization for Label Noise

Boosting Co-teaching with Compression Regularization for Lab...

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

作者： Chen, Yingyi Shen, Xi Hu, Shell Xu Suykens, Johan A. K. Katholieke Univ Leuven ESAT STADIUS Leuven Belgium UPE Ecole Ponts LIGM UMR 8049 Champs Sur Marne France Upload AI LLC Houston TX USA

ISBN: (纸本)9781665448994

In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout [22]. We find that Nested Dropout [22], though originally proposed to perform fast information retrieval and adaptive data compression, can properly regularize a neural network to combat label noise. Moreover, owing to its simplicity, it can be easily combined with Co-teaching [5] to further boost the performance. Our final model remains simple yet effective: it achieves comparable or even better performance than the state-of-the-art approaches on two real-world datasets with label noise which are Clothing1M [28] and ANIMAL-10N [24]. On Clothing1M [28], our approach obtains 74.9% accuracy which is slightly better than that of DivideMix [12]. On ANIMAL-10N [24], we achieve 84.1% accuracy while the best public result by PLC [30] is 83.4%. We hope that our simple approach can be served as a strong baseline for learning with label noise. Our implementation is available at https://***/yingyichen-cyy/Nested-Co-teaching.

关键词： Adaptation models computer vision Image coding conferences Neural networks Data compression Information retrieval

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Differential Morph Face Detection using Discriminative Wavelet Sub-bands

Differential Morph Face Detection using Discriminative Wavel...

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

作者： Chaudhary, Baaria Aghdaie, Poorya Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665448994

Face recognition systems are extremely vulnerable to morphing attacks, in which a morphed facial reference image can be successfully verified as two or more distinct identities. In this paper, we propose a morph attack detection algorithm that leverages an undecimated 2D Discrete Wavelet Transform (DWT) for identifying morphed face images. The core of our framework is that artifacts resulting from the morphing process that are not discernible in the image domain can be more easily identified in the spatial frequency domain. A discriminative wavelet sub-band can accentuate the disparity between a real and a morphed image. To this end, multi-level DWT is applied to all images, yielding 48 mid and high-frequency sub-bands each. The entropy distributions for each sub-band are calculated separately for both bona fide and morph images. For some of the sub-bands, there is a marked difference between the entropy of the sub-band in a bona fide image and the identical sub-band's entropy in a morphed image. Consequently, we employ Kullback-Liebler Divergence (KLD) to exploit these differences and isolate the sub-bands that are the most discriminative. We measure how discriminative a sub-band is by its KLD value and the 22 sub-bands with the highest KLD values are chosen for network training. Then, we train a deep Siamese neural network using these 22 selected sub-bands for differential morph attack detection. We examine the efficacy of discriminative wavelet sub-bands for morph attack detection and show that a deep neural network trained on these sub-bands can accurately identify morph imagery.

关键词： Training Wavelet domain Protocols Face recognition Frequency-domain analysis Neural networks Entropy

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End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving

End-to-End Interactive Prediction and Planning with Optical ...

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

作者： Wang, Hengli Cai, Peide Fan, Rui Sun, Yuxiang Liu, Ming Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Calif San Diego La Jolla CA 92093 USA Hong Kong Polytech Univ Hong Kong Peoples R China

ISBN: (纸本)9781665448994

With the recent advancement of deep learning technology, data-driven approaches for autonomous car prediction and planning have achieved extraordinary performance. Nevertheless, most of these approaches follow a non-interactive prediction and planning paradigm, hypothesizing that a vehicle's behaviors do not affect others. The approaches based on such a non-interactive philosophy typically perform acceptably in sparse traffic scenarios but can easily fail in dense traffic scenarios. Therefore, we propose an end-to-end interactive neural motion planner (INMP) for autonomous driving in this paper. Given a set of past surrounding-view images and a high definition map, our INMP first generates a feature map in bird's-eye-view space, which is then processed to detect other agents and perform interactive prediction and planning jointly. Also, we adopt an optical flow distillation paradigm, which can effectively improve the network performance while still maintaining its real-time inference speed. Extensive experiments on the nuScenes dataset and in the closed-loop Carla simulation environment demonstrate the effectiveness and efficiency of our INMP for the detection, prediction, and planning tasks. Our project page is at ***/view/inmp-ofd.

关键词： Philosophical considerations Predictive models Feature extraction Real-time systems Planning pattern recognition Task analysis

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DeepShift: Towards Multiplication-Less Neural Networks

DeepShift: Towards Multiplication-Less Neural Networks

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

作者： Elhoushi, Mostafa Chen, Zihao Shafiq, Farhan Tian, Ye Henry Li, Joey Yiwei Huawei Technol Markham ON Canada Univ Toronto Toronto ON Canada

ISBN: (纸本)9781665448994

The high computation, memory, and power budgets of inferring convolutional neural networks (CNNs) are major bottlenecks of model deployment to edge computing platforms, e.g., mobile devices and IoT. Moreover, training CNNs is time and energy-intensive even on high-grade servers. Convolution layers and fully connected layers, because of their intense use of multiplications, are the dominant contributor to this computation budget. We propose to alleviate this problem by introducing two new operations: convolutional shifts and fully-connected shifts which replace multiplications with bitwise shift and sign flipping during both training and inference. During inference, both approaches require only 5 bits (or less) to represent the weights. This family of neural network architectures (that use convolutional shifts and fully connected shifts) is referred to as DeepShift models. We propose two methods to train DeepShift models: DeepShift-Q which trains regular weights constrained to powers of 2, and DeepShift-PS that trains the values of the shifts and sign flips directly. Very close accuracy, and in some cases higher accuracy, to baselines are achieved. Converting pre-trained 32bit floating-point baseline models of ResNet18, ResNet50, VGG16, and GoogleNet to DeepShift and training them for 15 to 30 epochs, resulted in Top-1/Top-5 accuracies higher than that of the original model. Last but not least, we implemented the convolutional shifts and fully connected shift GPU kernels and showed a reduction in latency time of 25% when inferring ResNet18 compared to unoptimized multiplication-based GPU kernels. The code can be found at https://***/mostafaelhoushi/DeepShift.

关键词： Training Convolutional codes Computational modeling Neural networks Graphics processing units Mobile handsets pattern recognition

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Proceedings of the ieee computer society conference on computer vision and pattern recognition

Proceedings of the IEEE Computer Society Conference on Compu...

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31st Meeting of the ieee/CVF conference on computer vision and pattern recognition, cvpr 2018

ISBN: (纸本)9781538664209

The proceedings contain 979 papers. The topics discussed include: embodied question answering;learning by asking questions;finding tiny faces in the wild with generative adversarial network;paired CycleGAN: asymmetric style transfer for applying and removing makeup;learning pose specific representations by predicting different views;weakly and semi supervised human body part parsing via pose-guided knowledge transfer;person transfer GAN to bridge domain gap for person re-identification;cross-modal deep variational hand pose estimation;disentangled person image generation;super-fan: integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs;multistage adversarial losses for pose-based human image synthesis;and a certifiably globally optimal solution to the non-minimal relative pose problem.

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A Closer Look at Self-training for Zero-Label Semantic Segmentation

A Closer Look at Self-training for Zero-Label Semantic Segme...

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

作者： Pastore, Giuseppe Cermelli, Fabio Xian, Yongqin Mancini, Massimiliano Akata, Zeynep Caputo, Barbara Politecn Torino Turin Italy Italian Inst Technol Genoa Italy MPI Informat Saarbrucken Germany Univ Tubingen Tubingen Germany MPI Intelligent Syst Saarbrucken Germany

ISBN: (纸本)9781665448994

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudolabels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https: //***/giuseppepastore10/STRICT.

关键词： Training Image segmentation Semantics Pipelines Predictive models Information filters pattern recognition

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