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CSG0: Continual Urban Scene Generation with Zero Forgetting

CSG0: Continual Urban Scene Generation with Zero Forgetting

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jain, Himalaya Tuan-Hung Vu Perez, Patrick Cord, Matthieu Valeo Ai Paris France Sorbonne Univ Paris France

ISBN: (纸本)9781665487399

With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains;ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, i.e., with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin.

关键词： Training visualization Costs Semantics Memory management Generative adversarial networks Pattern recognition

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Unsupervised Simultaneous Learning for Camera Re-Localization and Depth Estimation from Video

Unsupervised Simultaneous Learning for Camera Re-Localizatio...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Taguchi, Shun Hirose, Noriaki Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi Japan

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We present an unsupervised simultaneous learning framework for the task of monocular camera re-localization and depth estimation from unlabeled video sequences. Monocular camera re-localization refers to the task of estimating the absolute camera pose from an instance image in a known environment, which has been intensively studied for alternative localization in GPS-denied environments. In recent works, camera re-localization methods are trained via supervised learning from pairs of camera images and camera poses. In contrast to previous works, we propose a completely unsupervised learning framework for camera re-localization and depth estimation, requiring only monocular video sequences for training. In our framework, we train two networks that estimate the scene coordinates using directions and the depth map from each image which are then combined to estimate the camera pose. The networks can be trained through the minimization of loss functions based on our loop closed view synthesis. In experiments with the 7-scenes dataset, the proposed method outperformed the re-localization of the state-of-the-art visual SLAM, ORB-SLAM3. Our method also outperforms state-of-the-art monocular depth estimation in a trained environment.

关键词： Training visualization Simultaneous localization and mapping Video sequences Robot vision systems Supervised learning Estimation

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WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation

WarpingGAN: Warping Multiple Uniform Priors for Adversarial ...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Tang, Yingzhi Qian, Yue Zhang, Qijian Zeng, Yiming Hou, Junhui Zhe, Xuefei City Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, Warping-GAN learns a unified local-warping function to warp multiple identical pre-defined priors (i.e., sets of points uniformly distributed on regular 3D grids) into 3D shapes driven by local structure-aware semantics. In addition, we also ingeniously utilize the principle of the discriminator and tailor a stitching loss to eliminate the gaps between different partitions of a generated shape corresponding to different priors for boosting quality. Owing to the novel generating mechanism, WarpingGAN, a single lightweight network after one-time training, is capable of efficiently generating uniformly distributed 3D point clouds with various resolutions. Extensive experimental results demonstrate the superiority of our WarpingGAN over state-of-the-art methods in terms of quantitative metrics, visual quality, and efficiency.

关键词： Point cloud compression Measurement Training visualization Computer vision Three-dimensional displays Codes

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Back to the Manifold: Recovering from Out-of-Distribution States

Back to the Manifold: Recovering from Out-of-Distribution St...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Reichlin, Alfredo Marchetti, Giovanni Luca Yin, Hang Ghadirzadeh, Ali Kragic, Danica KTH Royal Inst Technol Stockholm Sweden Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Learning from previously collected datasets of expert data offers the promise of acquiring robotic policies without unsafe and costly online explorations. However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time. While prior works mainly studied the distribution shift caused by the policy during the offline training, the problem of recovering from out-of-distribution states at the deployment time is not very well studied yet. We alleviate the distributional shift at the deployment time by introducing a recovery policy that brings the agent back to the training manifold whenever it steps out of the in-distribution states, e.g., due to an external perturbation. The recovery policy relies on an approximation of the training data density and a learned equivariant mapping that maps visual observations into a latent space in which translations correspond to the robot actions. We demonstrate the effectiveness of the proposed method through several manipulation experiments on a real robotic platform. Our results show that the recovery policy enables the agent to complete tasks while the behavioral cloning alone fails because of the distributional shift problem.

关键词： Training Manifolds visualization Perturbation methods Training data Cloning Behavioral sciences

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Attributable Visual Similarity Learning

Attributable Visual Similarity Learning

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Borui Zheng, Wenzhao Zhou, Jie Lu, Jiwen Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to a single point in the embedding space with a distance metric (e.g., Mahalanobis distance, Euclidean distance). Motivated by the human semantic similarity cognition, we propose a generalized similarity learning paradigm to represent the similarity between two images with a graph and then infer the overall similarity accordingly. Furthermore, we establish a bottom-up similarity construction and top-down similarity inference framework to infer the similarity based on semantic hierarchy consistency. We first identify unreliable higher-level similarity nodes and then correct them using the most coherent adjacent lower-level similarity nodes, which simultaneously preserve traces for similarity attribution. Extensive experiments on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate significant improvements over existing deep similarity learning methods and verify the interpretability of our framework.(1)

关键词： Learning systems Representation learning Measurement visualization Computer vision Text recognition Semantics

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A visualization Platform for the Influence of the Internet and Big Data on Social Governance Research and Practice

A Visualization Platform for the Influence of the Internet a...

引用

6th International conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Gao, Feng Hubei University of Police Department of Investigation Wuhan 430034 China

ISBN: (纸本)9781665410359

This article studies the influence of the Internet and big data on social governance research and practice. With the continuous development and application of big data technology, big data can also be developed and utilized as an important starting point for modern social governance. The article discusses the characteristics and impact of the big data era and its role in modern social governance, and then designs the visualization platform from the aspects of social research methods, social governance thinking, social ecological changes, and social governance models. © 2022 ieee.

关键词： Big data

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MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition

MeMViT: Memory-Augmented Multiscale Vision Transformer for E...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wu, Chao-Yuan Li, Yanghao Mangalam, Karttikeya Fan, Haoqi Xiong, Bo Malik, Jitendra Feichtenhofer, Christoph Facebook AI Res Menlo Pk CA 94025 USA Univ Calif Berkeley Berkeley CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video architectures can only process <5 seconds of a video without hitting the computation or memory bottlenecks. In this paper, we propose a new strategy to overcome this challenge. Instead of trying to process more frames at once like most existing methods, we propose to process videos in an online fashion and cache "memory" at each iteration. Through the memory, the model can reference prior context for long-term modeling, with only a marginal cost. Based on this idea, we build MeMViT, a Memory-augmented Multiscale Vision Transformer, that has a temporal support 30xlonger than existing models with only 4.5% more compute;traditional methods need >3,000% more compute to do the same. On a wide range of settings, the increased temporal support enabled by MeMViT brings large gains in recognition accuracy consistently. MeMViT obtains state-of-the-art results on the AVA, EPIC-Kitchens-100 action classification, and action anticipation datasets. Code and models will be made publicly available.

关键词： Computer vision visualization Costs Codes Computational modeling Cache memory Computer architecture

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Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks

Transporters with Visual Foresight for Solving Unseen Rearra...

引用

ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Wu, Hongtao Ye, Jikai Meng, Xin Paxton, Chris Chirikjian, Gregory S. Johns Hopkins Univ Baltimore MD 21218 USA Natl Univ Singapore Singapore Singapore NVIDIA Santa Clara CA USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Rearrangement tasks have been identified as a crucial challenge for intelligent robotic manipulation, but few methods allow for precise construction of unseen structures. We propose a visual foresight model for pick-and-place rearrangement manipulation which is able to learn efficiently. In addition, we develop a multi-modal action proposal module which builds on the Goal-Conditioned Transporter Network, a state-of-theart imitation learning method. Our image-based task planning method, Transporters with Visual Foresight, is able to learn from only a handful of data and generalize to multiple unseen tasks in a zero-shot manner. TVF is able to improve the performance of a state-of-the-art imitation learning method on unseen tasks in simulation and real robot experiments. In particular, the average success rate on unseen tasks improves from 55.4% to 78.5% in simulation experiments and from 30% to 63.3% in real robot experiments when given only tens of expert demonstrations. Video and code are available on our project website: https://***/tvf/

关键词： Learning systems visualization Interpolation Training data Predictive models Data models Planning

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GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation

GRAM: Generative Radiance Manifolds for 3D-Aware Image Gener...

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Deng, Yu Yang, Jiaolong Xiang, Jianfeng Tong, Xin Tsinghua Univ Beijing Peoples R China Microsoft Res Asia Beijing Peoples R China USTC Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still cannot generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance manifolds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency.(1)

关键词： Training Manifolds Representation learning visualization Three-dimensional displays Monte Carlo methods Image synthesis

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Towards More Effective Data visualization Methods Using Haptics

Towards More Effective Data Visualization Methods Using Hapt...

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ieee World Haptics conference (WHC)

作者： Isran, Rayan Sepehri, Katayoun Theivendran, Karthikan Anwar, Ahmed McGill Univ Montreal PQ Canada Univ British Columbia Vancouver BC Canada Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665418713

Cognitive overload in complex multi-dimensional data visualization can cause challenges for users to comprehend, navigate, and determine statistical measures. To address this problem, we tested the feasibility of integrating vibrotactile and force-feedback haptics with bubble charts to test several haptic rendering techniques for visualizing data. The preliminary results suggest that force-feedback was effective in quickly navigating to points of interest in the data, and vibrotactile feedback was effective in representing higher dimensions and locating statistical measures of data.

关键词： Force measurement Navigation conferences Data visualization Rendering (computer graphics) Haptic interfaces

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