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Autoregressive Stylized Motion Synthesis with Generative Flow

Autoregressive Stylized Motion Synthesis with Generative Flo...

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

作者： Wen, Yu-Hui Yang, Zhipeng Fu, Hongbo Gao, Lin Sun, Yanan Liu, Yong-Jin Tsinghua Univ BNRist CS Dept Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China City Univ Hong Kong Sch Creat Media Hong Kong Peoples R China Chinese Acad Sci Beijing Key Lab Mobile Comp & Pervas Device ICT Beijing Peoples R China

ISBN: (纸本)9781665445092

Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics. Most existing deep learning methods for this problem are supervised and trained by registered motion pairs. In addition, these methods are often limited to yielding a deterministic output, given a pair of style and content motions. In this paper, we propose an unsupervised approach for motion style transfer by synthesizing stylized motions autoregressively using a generative flow model M. M is trained to maximize the exact likelihood of a collection of unlabeled motions, based on an autoregressive context of poses in previous frames and a control signal representing the movement of a root joint. Thanks to invertible flow transformations, latent codes that encode deep properties of motion styles are efficiently inferred by M. By combining the latent codes (from an input style motion S) with the autoregressive context and control signal (from an input content motion C), M outputs a stylized motion which transfers style from S to C. Moreover, our model is probabilistic and is able to generate various plausible motions with a specific style. We evaluate the proposed model on motion capture datasets containing different human motion styles. Experiment results show that our model outperforms the state-of-the-art methods, despite not requiring manually labeled training data.

关键词： Training Deep learning computer vision Codes Training data Games Probabilistic logic

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Reformulating HOI Detection as Adaptive Set Prediction

Reformulating HOI Detection as Adaptive Set Prediction

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

作者： Chen, Mingfei Liao, Yue Liu, Si Chen, Zhiyuan Wang, Fei Qian, Chen Huazhong Univ Sci & Technol Wuhan Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Determining which image regions to concentrate is critical for Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on either detected human and object pairs or pre-defined interaction locations, which limits learning of the effective features. In this paper, we reformulate HOI detection as an adaptive set prediction problem, with this novel formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instance and interaction branches. To attain this, we map a trainable interaction query set to an interaction prediction set with transformer. Each query adaptively aggregates the interaction-relevant features from global contexts through multi-head co-attention. Besides, the training process is supervised adaptively by matching each ground-truth with the interaction prediction. Furthermore, we design an effective instance-aware attention module to introduce instructive features from the instance branch into the interaction branch. Our method outperforms previous state-of-the-art methods without any extra human pose and language features on three challenging HOI detection datasets. Especially, we achieve over 31% relative improvement on a large scale HICO-DET dataset. Code is available at https://***/yoyomimi/AS-Net.

关键词： Training computer vision Codes Aggregates Detectors Feature extraction Transformers

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HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

HITNet: Hierarchical Iterative Tile Refinement Network for R...

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

作者： Tankovich, Vladimir Hane, Christian Zhang, Yinda Kowdle, Adarsh Fanello, Sean Bouaziz, Sofien Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

This paper presents HITNet, a novel neural network architecture for real-time stereo matching. Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses. To achieve a high level of accuracy, our network not only geometrically reasons about disparities but also infers slanted plane hypotheses allowing to more accurately perform geometric warping and upsampling operations. Our architecture is inherently multi-resolution allowing the propagation of information across different levels. Multiple experiments prove the effectiveness of the proposed approach at a fraction of the computation required by state-of-the-art methods. At the time of writing, HITNet ranks 1st3rd on all the metrics published on the ETH3D website for two view stereo, ranks 1st on most of the metrics amongst all the end-to-end learning approaches on Middlebury-v3, ranks 1st on the popular KITTI 2012 and 2015 benchmarks among the published methods faster than 100 ms.

关键词： Measurement Three-dimensional displays Neural networks Training data computer architecture Benchmark testing Writing

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TransNAS-Bench-101: Improving transferability and Generalizability of Cross-Task Neural Architecture Search

TransNAS-Bench-101: Improving transferability and Generaliza...

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

作者： Duan, Yawen Chen, Xin Xu, Hang Chen, Zewei Liang, Xiaodan Zhang, Tong Li, Zhenguo Univ Hong Kong Hong Kong Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recent breakthroughs of Neural Architecture Search (NAS) extend the field's research scope towards a broader range of vision tasks and more diversified search spaces. While existing NAS methods mostly design architectures on a single task, algorithms that look beyond single-task search are surging to pursue a more efficient and universal solution across various tasks. Many of them leverage transfer learning and seek to preserve, reuse, and refine network design knowledge to achieve higher efficiency in future tasks. However, the enormous computational cost and experiment complexity of cross-task NAS are imposing barriers for valuable research in this direction. Existing NAS benchmarks all focus on one type of vision task, i.e., classification. In this work, we propose TransNAS-Bench-101, a benchmark dataset containing network performance across seven tasks, covering classification, regression, pixel-level prediction, and self-supervised tasks. This diversity provides opportunities to transfer NAS methods among tasks and allows for more complex transfer schemes to evolve. We explore two fundamentally different types of search space: cell-level search space and macro-level search space. With 7,352 backbones evaluated on seven tasks, 51,464 trained models with detailed training information are provided. With TransNAS-Bench-101, we hope to encourage the advent of exceptional NAS algorithms that raise cross-task search efficiency and generalizability to the next level. Our dataset and code will be available at Mindspore(1) and VEGA(2).

关键词： Training Knowledge engineering computer vision Design methodology Transfer learning computer architecture Benchmark testing

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Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces

Out-of-Distribution Detection Using Union of 1-Dimensional S...

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

作者： Zaeemzadeh, Alireza Bisagno, Niccolo Sambugaro, Zeno Conci, Nicola Rahnavard, Nazanin Shah, Mubarak Univ Cent Florida Orlando FL 32816 USA Univ Trento Trento Italy

ISBN: (纸本)9781665445092

The goal of out-of-distribution (OOD) detection is to handle the situations where the test samples are drawn from a different distribution than the training data. In this paper, we argue that OOD samples can be detected more easily if the training data is embedded into a low-dimensional space, such that the embedded training samples lie on a union of 1-dimensional subspaces. We show that such embedding of the in-distribution (ID) samples provides us with two main advantages. First, due to compact representation in the feature space, OOD samples are less likely to occupy the same region as the known classes. Second, the first singular vector of ID samples belonging to a 1-dimensional subspace can be used as their robust representative. Motivated by these observations, we train a deep neural network such that the ID samples are embedded onto a union of 1-dimensional subspaces. At the test time, employing sampling techniques used for approximate Bayesian inference in deep learning, input samples are detected as OOD if they occupy the region corresponding to the ID samples with probability 0. Spectral components of the ID samples are used as robust representative of this region. Our method does not have any hyperparameter to be tuned using extra information and it can be applied on different modalities with minimal change. The effectiveness of the proposed method is demonstrated on different benchmark datasets, both in the image and video classification domains.

关键词： Deep learning Training Measurement computer vision Training data Benchmark testing Feature extraction

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Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation

Target-Aware Object Discovery and Association for Unsupervis...

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

作者： Zhou, Tianfei Li, Jianwu Li, Xueyi Shao, Ling Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Incept Inst Artificial Intelligence Al Ain U Arab Emirates

ISBN: (纸本)9781665445092

This paper addresses the task of unsupervised video multi-object segmentation. Current approaches follow a two-stage paradigm: 1) detect object proposals using pre-trained Mask R-CNN, and 2) conduct generic feature matching for temporal association using re-identification techniques. However, the generic features, widely used in both stages, are not reliable for characterizing unseen objects, leading to poor generalization. To address this, we introduce a novel approach for more accurate and efficient spatio-temporal segmentation. In particular, to address instance discrimination, we propose to combine foreground region estimation and instance grouping together in one network, and additionally introduce temporal guidance for segmenting each frame, enabling more accurate object discovery. For temporal association, we complement current video object segmentation architectures with a discriminative appearance model, capable of capturing more fine-grained target-specific information. Given object proposals from the instance discrimination network, three essential strategies are adopted to achieve accurate segmentation: 1) target-specific tracking using a memory-augmented appearance model;2) target-agnostic verification to trace possible tracklets for the proposal;3) adaptive memory updating using the verified segments. We evaluate the proposed approach on DAVIS(17) and YouTube-VIS, and the results demonstrate that it outperforms state-of-the-art methods both in segmentation accuracy and inference speed.

关键词： Adaptation models computer vision Target tracking Estimation Object segmentation Feature extraction pattern recognition

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A functional approach to rotation equivariant non-linearities for Tensor Field Networks

A functional approach to rotation equivariant non-linearitie...

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

作者： Poulenard, Adrien Guibas, Leonidas J. Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

Learning pose invariant representation is a fundamental problem in shape analysis. Most existing deep learning algorithms for 3D shape analysis are not robust to rotations and are often trained on synthetic datasets consisting of pre-aligned shapes, yielding poor generalization to unseen poses. This observation motivates a growing interest in rotation invariant and equivariant methods. The field of rotation equivariant deep learning is developing in recent years thanks to a well established theory of Lie group representations and convolutions. A fundamental problem in equivariant deep learning is to design activation functions which are both informative and preserve equivariance. The recently introduced Tensor Field Network (TFN) framework provides a rotation equivariant network design for point cloud analysis. TFN features undergo a rotation in feature space given a rotation of the input pointcloud. TFN and similar designs consider nonlinearities which operate only over rotation invariant features such as the norm of equivariant features to preserve equivariance, making them unable to capture the directional information. In a recent work entitled "Gauge Equivariant Mesh CNNs: Anisotropic Convolutions on Geometric Graphs" Hann et al. interpret 2D rotation equivariant features as Fourier coefficients of functions on the circle. In this work we transpose the idea of Hann et al. to 3D by interpreting TFN features as spherical harmonics coefficients of functions on the sphere. We introduce a new equivariant nonlinearity and pooling for TFN. We show improvments over the original TFN design and other equivariant nonlinearities in classification and segmentation tasks. Furthermore our method is competitive with state of the art rotation invariant methods in some instances.

关键词： Deep learning computer vision Three-dimensional displays Tensors Shape Harmonic analysis pattern recognition

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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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Domain Consensus Clustering for Universal Domain Adaptation

Domain Consensus Clustering for Universal Domain Adaptation

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

作者： Li, Guangrui Kang, Guoliang Zhu, Yi Wei, Yunchao Yang, Yi Univ Technol Sydney ReLER Lab AAII Sydney NSW Australia Carnegie Mellon Univ Pittsburgh PA 15213 USA Amazon Web Serv Seattle WA USA

ISBN: (纸本)9781665445092

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to transfer the knowledge from source to target under unaligned label space. The main challenge of UniDA lies in how to separate common classes (i.e., classes shared across domains), from private classes (i.e., classes only exist in one domain). Previous works treat the private samples in the target as one generic class but ignore their intrinsic structure. Consequently, the resulting representations are not compact enough in the latent space and can be easily confused with common samples. To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones. Specifically, we draw the domain consensus knowledge from two aspects to facilitate the clustering and the private class discovery, i.e., the semantic-level consensus, which identifies the cycle-consistent clusters as the common classes, and the sample-level consensus, which utilizes the cross-domain classification agreement to determine the number of clusters and discover the private classes. Based on DCC, we are able to separate the private classes from the common ones, and differentiate the private classes themselves. Finally, we apply a class-aware alignment technique on identified common samples to minimize the distribution shift, and a prototypical regularizer to inspire discriminative target clusters. Experiments on four benchmarks demonstrate DCC significantly outperforms previous state-of-the-arts.

关键词： computer vision Benchmark testing pattern recognition

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RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features

RefineMask: Towards High-Quality Instance Segmentation with ...

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

作者： Zhang, Gang Lu, Xin Tan, Jingru Li, Jianmin Zhang, Zhaoxiang Li, Quanquan Hu, Xiaolin Tsinghua Univ State Key Lab Intelligent Technol & Syst Inst AI BNRistDept Comp Sci & Technol Beijing Peoples R China SenseTime Res Hong Kong Peoples R China Tongji Univ Shanghai Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China UCAS Beijing Peoples R China

ISBN: (纸本)9781665445092

The two-stage methods for instance segmentation, e.g. Mask R-CNN, have achieved excellent performance recently. However, the segmented masks are still very coarse due to the downsampling operations in both the feature pyramid and the instance-wise pooling process, especially for large objects. In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner. Through fusing more detailed information stage by stage, RefineMask is able to refine high-quality masks consistently. RefineMask succeeds in segmenting hard cases such as bent parts of objects that are over-smoothed by most previous methods and outputs accurate boundaries. Without bells and whistles, RefineMask yields significant gains of 2.6, 3.4, 3.8 AP over Mask R-CNN on COCO, LVIS, and Cityscapes benchmarks respectively at a small amount of additional computational cost. Furthermore, our single-model result outperforms the winner of the LVIS Challenge 2020 by 1.3 points on the LVIS test-dev set and establishes a new state-of-the-art. Code will be available at https.//***/zhanggang001/RefineMask.

关键词： computer vision Codes Benchmark testing Feature extraction pattern recognition Computational efficiency

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