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Learning by Watching

Learning by Watching

引用

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

作者： Zhang, Jimuyang Ohn-Bar, Eshed Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

When in a new situation or geographical location, human drivers have an extraordinary ability to watch others and learn maneuvers that they themselves may have never performed. In contrast, existing techniques for learning to drive preclude such a possibility as they assume direct access to an instrumented ego-vehicle with fully known observations and expert driver actions. However, such measurements cannot be directly accessed for the non-ego vehicles when learning by watching others. Therefore, in an application where data is regarded as a highly valuable asset, current approaches completely discard the vast portion of the training data that can be potentially obtained through indirect observation of surrounding vehicles. Motivated by this key insight, we propose the Learning by Watching (LbW) framework which enables learning a driving policy without requiring full knowledge of neither the state nor expert actions. To increase its data, i.e., with new perspectives and maneuvers, LbW makes use of the demonstrations of other vehicles in a given scene by (1) transforming the ego-vehicle's observations to their points of view, and (2) inferring their expert actions. Our LbW agent learns more robust driving policies while enabling data-efficient learning, including quick adaptation of the policy to rare and novel scenarios. In particular, LbW drives robustly even with a fraction of available driving data required by existing methods, achieving an average success rate of 92% on the original CARLA benchmark with only 30 minutes of total driving data and 82% with only 10 minutes.

关键词： computer vision Instruments Current measurement Training data Benchmark testing pattern recognition Noise measurement

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Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

引用

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

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

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StablePose: Learning 6D Object Poses from Geometrically Stable Patches

StablePose: Learning 6D Object Poses from Geometrically Stab...

引用

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

作者： Shi, Yifei Huang, Junwen Xu, Xin Zhang, Yifan Xu, Kai Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781665445092

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A sub-network is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation.

关键词： Deep learning computer vision Three-dimensional displays Pose estimation Benchmark testing Stability analysis pattern recognition

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Interpolation-based Semi-supervised Learning for Object Detection

Interpolation-based Semi-supervised Learning for Object Dete...

引用

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

作者： Jeong, Jisoo Verma, Vikas Hyun, Minsung Kannala, Juho Kwak, Nojun Seou1 Natl Univ Seoul South Korea Aalto Univ Espoo Finland Mila Quebec Artificial Intelligence Inst Montreal PQ Canada SK Hynix Icheon Si South Korea

ISBN: (纸本)9781665445092

Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection have not been studied much. In this paper, we propose an Interpolation-based Semi-supervised learning method for object Detection (ISD), which considers and solves the problems caused by applying conventional Interpolation Regularization (IR) directly to object detection. We divide the output of the model into two types according to the objectness scores of both original patches that are mixed in IR. Then, we apply a separate loss suitable for each type in an unsupervised manner. The proposed losses dramatically improve the performance of semi-supervised learning as well as supervised learning. In the supervised learning setting, our method improves the baseline methods by a significant margin. In the semi-supervised learning setting, our algorithm improves the performance on a benchmark dataset (PASCAL VOC and MSCOCO) in a benchmark architecture (SSD).

关键词： Interpolation Supervised learning Object detection Detectors computer architecture Semisupervised learning Benchmark testing

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Incremental Learning via Rate Reduction

Incremental Learning via Rate Reduction

引用

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

作者： Wu, Ziyang Baek, Christina You, Chong Ma, Yi Cornell Ithaca NY 14850 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781665445092

Current deep learning architectures suffer from catastrophic forgetting, a failure to retain knowledge of previously learned classes when incrementally trained on new classes. The fundamental roadblock faced by deep learning methods is that the models are optimized as "black boxes," making it difficult to properly adjust the model parameters to preserve knowledge about previously seen data. To overcome the problem of catastrophic forgetting, we propose utilizing an alternative "white box" architecture derived from the principle of rate reduction, where each layer of the network is explicitly computed without back propagation. Under this paradigm, we demonstrate that, given a pretrained network and new data classes, our approach can provably construct a new network that emulates joint training with all past and new classes. Finally, our experiments show that our proposed learning algorithm observes significantly less decay in classification performance, outperforming state of the art methods on MNIST and CIFAR-10 by a large margin and justifying the use of "white box" algorithms for incremental learning even for sufficiently complex image data.

关键词： Deep learning Training Backpropagation computer vision Computational modeling computer architecture Data models

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Self-supervised Video Hashing via Bidirectional Transformers

Self-supervised Video Hashing via Bidirectional Transformers

引用

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

作者： Li, Shuyan Li, Xiu Lu, Jiwen Zhou, Jie Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Grad Sch Shenzhen Beijing Peoples R China

ISBN: (纸本)9781665445092

Most existing unsupervised video hashing methods are built on unidirectional models with less reliable training objectives, which underuse the correlations among frames and the similarity structure between videos. To enable efficient scalable video retrieval, we propose a self-supervised video Hashing method based on Bidirectional Transformers (BTH). Based on the encoder-decoder structure of transformers, we design a visual cloze task to fully exploit the bidirectional correlations between frames. To unveil the similarity structure between unlabeled video data, we further develop a similarity reconstruction task by establishing reliable and effective similarity connections in the video space. Furthermore, we develop a cluster assignment task to exploit the structural statistics of the whole dataset such that more discriminative binary codes can be learned. Extensive experiments implemented on three public benchmark datasets, FCVID, ActivityNet and YFCC, demonstrate the superiority of our proposed approach.

关键词： Training Hash functions Visualization computer vision Correlation Benchmark testing Transformers

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Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias

<i>Camera Pose Matters</i>: Improving Depth Prediction by Mi...

引用

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

作者： Zhao, Yunhan Kong, Shu Fowlkes, Charless UC Irvine Irvine CA 92697 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fail to make reliable depth predictions for testing examples captured under uncommon camera poses. To address this issue, we propose two novel techniques that exploit the camera pose during training and prediction. First, we introduce a simple perspective-aware data augmentation that synthesizes new training examples with more diverse views by perturbing the existing ones in a geometrically consistent manner. Second, we propose a conditional model that exploits the per-image camera pose as prior knowledge by encoding it as a part of the input. We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses. We show that our methods improve performance when applied to a range of different predictor architectures. Lastly, we show that explicitly encoding the camera pose distribution improves the generalization performance of a synthetically trained depth predictor when evaluated on real images.

关键词： Training computer vision Image coding Handheld computers Perturbation methods Cameras Encoding

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Tackling the Ill-Posedness of Super-Resolution through Adaptive Target Generation

Tackling the Ill-Posedness of Super-Resolution through Adapt...

引用

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

作者： Jo, Younghyun Oh, Seoung Wug Vajda, Peter Kim, Seon Joo Yonsei Univ Seoul South Korea Adobe Res San Jose CA USA Facebook Menlo Pk CA USA

ISBN: (纸本)9781665445092

By the one-to-many nature of the super-resolution (SR) problem, a single low-resolution (LR) image can be mapped to many high-resolution (HR) images. However, learning based SR algorithms are trained to map an LR image to the corresponding ground truth (GT) HR image in the training dataset. The training loss will increase and penalize the algorithm when the output does not exactly match the GT target, even when the outputs are mathematically valid candidates according to the SR framework. This becomes more problematic for the blind SR, as diverse unknown blur kernels exacerbate the ill-posedness of the problem. To this end, we propose a fundamentally different approach for the SR by introducing the concept of the adaptive target. The adaptive target is generated from the original GT target by a transformation to match the output of the SR network. The adaptive target provides an effective way for the SR algorithm to deal with the ill-posed nature of the SR, by providing the algorithm with the flexibility of accepting a variety of valid solutions. Experimental results show the effectiveness of our algorithm, especially for improving the perceptual quality of HR outputs.

关键词： Training computer vision Adaptive systems Impedance matching Superresolution Training data Network architecture

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NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering using RGB Cameras

NeuralHumanFVV: Real-Time Neural Volumetric Human Performanc...

引用

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

作者： Suo, Xin Jiang, Yuheng Lin, Pei Zhang, Yingliang Wu, Minye Guo, Kaiwen Xu, Lan ShanghaiTech Univ Shanghai Peoples R China Dgene Shanghai Peoples R China Google Mountain View CA 94043 USA Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9781665445092

4D reconstruction and rendering of human activities is critical for immersive VR/AR experience. Recent advances still fail to recover fine geometry and texture results with the level of detail present in the input images from sparse multi-view RGB cameras. In this paper, we propose Neural-HumanFVV, a real-time neural human performance capture and rendering system to generate both high-quality geometry and photo-realistic texture of human activities in arbitrary novel views. We propose a neural geometry generation scheme with a hierarchical sampling strategy for real-time implicit geometry inference, as well as a novel neural blending scheme to generate high resolution (e.g., 1k) and photo-realistic texture results in the novel views. Furthermore, we adopt neural normal blending to enhance geometry details and formulate our neural geometry and texture rendering into a multi-task learning framework. Extensive experiments demonstrate the effectiveness of our approach to achieve high-quality geometry and photo-realistic free view-point reconstruction for challenging human performances.

关键词： Geometry computer vision Entertainment industry Rendering (computer graphics) Cameras Real-time systems Cognition

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Deep Video Matting via Spatio-Temporal Alignment and Aggregation

Deep Video Matting via Spatio-Temporal Alignment and Aggrega...

引用

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

作者： Sun, Yanan Wang, Guanzhi Gu, Qiao Tang, Chi-Keung Tai, Yu-Wing HKUST Daejeon South Korea Stanford Univ Stanford CA 94305 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Kuaishou Technol Seoul South Korea

ISBN: (纸本)9781665445092

Despite the significant progress made by deep learning in natural image matting, there has been so far no representative work on deep learning for video matting due to the inherent technical challenges in reasoning temporal domain and lack of large-scale video matting datasets. In this paper, we propose a deep learning-based video matting framework which employs a novel and effective spatio-temporal feature aggregation module (ST-FAM). As optical flow estimation can be very unreliable within matting regions, ST-FAM is designed to effectively align and aggregate information across different spatial scales and temporal frames within the network decoder. To eliminate frame-by-frame trimap annotations, a lightweight interactive trimap propagation network is also introduced. The other contribution consists of a large-scale video matting dataset with groundtruth alpha mattes for quantitative evaluation and real-world high-resolution videos with trimaps for qualitative evaluation. Quantitative and qualitative experimental results show that our framework significantly outperforms conventional video matting and deep image matting methods applied to video in presence of multi frame temporal information.

关键词： Deep learning computer vision Annotations Estimation Coherence Predictive models Cognition

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