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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

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Trajectory Prediction with Latent Belief Energy-Based Model

Trajectory Prediction with Latent Belief Energy-Based Model

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

作者： Pang, Bo Zhao, Tianyang Xie, Xu Wu, Ying Nian Univ Calif Los Angeles Dept Stat Los Angeles CA 90024 USA

ISBN: (纸本)9781665445092

Human trajectory prediction is critical for autonomous platforms like self-driving cars or social robots. We present a latent belief energy-based model (LB-EBM) for diverse human trajectory forecast. LB-EBM is a probabilistic model with cost function defined in the latent space to account for the movement history and social context. The low-dimensionality of the latent space and the high expressivity of the EBM make it easy for the model to capture the multi-modality of pedestrian trajectory distributions. LB-EBM is learned from expert demonstrations (i.e., human trajectories) projected into the latent space. Sampling from or optimizing the learned LB-EBM yields a belief vector which is used to make a path plan, which then in turn helps to predict a long-range trajectory. The effectiveness of LB-EBM and the two-step approach are supported by strong empirical results. Our model is able to make accurate, multi-modal, and social compliant trajectory predictions and improves over prior state-of-the-arts performance on the Stanford Drone trajectory prediction benchmark by 10.9% and on the ETH-UCY benchmark by 27.6%.

关键词： computer vision Predictive models Benchmark testing Probabilistic logic Cost function Trajectory pattern recognition

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iDisc: Internal Discretization for Monocular Depth Estimation

iDisc: Internal Discretization for Monocular Depth Estimatio...

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conference on computer vision and pattern recognition (cvpr)

作者： Luigi Piccinelli Christos Sakaridis Fisher Yu Computer Vision Lab ETH Zürich

Monocular depth estimation is fundamental for 3D scene understanding and downstream applications. However, even under the supervised setup, it is still challenging and ill-posed due to the lack of full geometric constraints. Although a scene can consist of millions of pixels, there are fewer high-level patterns. We propose iDisc to learn those patterns with internal discretized representations. The method implicitly partitions the scene into a set of high-level patterns. In particular, our new module, Internal Discretization (ID), implements a continuous-discrete-continuous bottleneck to learn those concepts without supervision. In contrast to state-of-the-art methods, the proposed model does not enforce any explicit constraints or priors on the depth output. The whole network with the ID module can be trained end-to-end, thanks to the bottleneck module based on attention. Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark. iDisc can also achieve state-of-the-art results on surface normal estimation. Further, we explore the model generalization capability via zero-shot testing. We observe the compelling need to promote diversification in the outdoor scenario. Hence, we introduce splits of two autonomous driving datasets, DDAD and Argoverse. Code is available at http://***/pub/idisc.

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Deep Analysis of CNN-based Spatio-temporal Representations for Action recognition

Deep Analysis of CNN-based Spatio-temporal Representations f...

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

作者： Chen, Chun-Fu (Richard) Panda, Rameswar Ramakrishnan, Kandan Feris, Rogerio Cohn, John Oliva, Aude Fan, Quanfu MIT IBM Watson AI Lab Cambridge MA 02142 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665445092

In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets. In this paper, we carry out in-depth comparative analysis to better understand the differences between these approaches and the progress made by them. To this end, we develop an unified framework for both 2D-CNN and 3D-CNN action models, which enables us to remove bells and whistles and provides a common ground for fair comparison. We then conduct an effort towards a large-scale analysis involving over 300 action recognition models. Our comprehensive analysis reveals that a) a significant leap is made in efficiency for action recognition, but not in accuracy;b) 2D-CNN and 3D-CNN models behave similarly in terms of spatio-temporal representation abilities and transferability.

关键词： Analytical models computer vision Three-dimensional displays Codes Benchmark testing pattern recognition Convolutional neural networks

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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

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

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： Image sensors Training computer vision Pipelines Object detection computer architecture Dynamic range

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NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

NeRF in the Wild: Neural Radiance Fields for Unconstrained P...

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

作者： Martin-Brualla, Ricardo Radwan, Noha Sajjadi, Mehdi S. M. Barron, Jonathan T. Dosovitskiy, Alexey Duckworth, Daniel Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multi-layer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

关键词： Learning systems Solid modeling Photorealism Three-dimensional displays Image color analysis Lighting Rendering (computer graphics)

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Virtual Fully-Connected Layer: Training a Large-Scale Face recognition Dataset with Limited Computational Resources

Virtual Fully-Connected Layer: Training a Large-Scale Face R...

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

作者： Li, Pengyu Wang, Biao Zhang, Lei Alibaba Grp Artificial Intelligence Ctr DAMO Acad Hangzhou Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Recently, deep face recognition has achieved significant progress because of Convolutional Neural Networks (CNNs) and large-scale datasets. However, training CNNs on a large-scale face recognition dataset with limited computational resources is still a challenge. This is because the classification paradigm needs to train a fully-connected layer as the category classifier, and its parameters will be in the hundreds of millions if the training dataset contains millions of identities. This requires many computational resources, such as GPU memory. The metric learning paradigm is an economical computation method, but its performance is greatly inferior to that of the classification paradigm. To address this challenge, we propose a simple but effective CNN layer called the Virtual fully-connected (Virtual FC) layer to reduce the computational consumption of the classification paradigm. Without bells and whistles, the proposed Virtual FC reduces the parameters by more than 100 times with respect to the fully-connected layer and achieves competitive performance on mainstream face recognition evaluation datasets. Moreover, the performance of our Virtual FC layer on the evaluation datasets is superior to that of the metric learning paradigm by a significant margin. Our code will be released in hopes of disseminating our idea to other domains1.

关键词： Training Measurement computer vision Codes Face recognition Memory management Graphics processing units

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NTIRE 2023 Challenge on Image Denoising: Methods and Results

NTIRE 2023 Challenge on Image Denoising: Methods and Results

引用

2023 ieee/cvf conference on computer vision and pattern recognition Workshops, cvprW 2023

作者： Li, Yawei Zhang, Yulun Van Gool, Luc Timofte, Radu Tu, Zhijun Du, Kunpeng Wang, Hailing Chen, Hanting Li, Wei Wang, Xiaofei Hu, Jie Wang, Yunhe Kong, Xiangyu Wu, Jinlong Zhang, Dafeng Zhang, Jianxing Liu, Shuai Bai, Furui Feng, Chaoyu Wang, Hao Zhang, Yuqian Shao, Guangqi Wang, Xiaotao Lei, Lei Xu, Rongjian Zhang, Zhilu Chen, Yunjin Ren, Dongwei Zuo, Wangmeng Wu, Qi Han, Mingyan Cheng, Shen Li, Haipeng Jiang, Ting Jiang, Chengzhi Li, Xinpeng Luo, Jinting Lin, Wenjie Yu, Lei Fan, Haoqiang Liu, Shuaicheng Arora, Aditya Zamir, Syed Waqas Vazquez-Corral, Javier Derpanis, Konstantinos G. Brown, Michael S. Li, Hao Zhao, Zhihao Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Bo Chen, Jingxiang Li, Chenghua Zhang, Xi Zhang, Zhao Ren, Jiahuan Ji, Zhicheng Miao, Kang Zhao, Suiyi Zheng, Huan Wei, YanYan Liu, Kangliang Du, Xiangcheng Liu, Sijie Zheng, Yingbin Wu, Xingjiao Jin, Cheng Wang, Yan Chen, Jiayin Wu, Xiaoxuan Chen, Huiming Zheng, Xing Chen, Yejia Irny, Rajeev Koundinya, Sriharsha Kamath, Vighnesh Khandelwal, Gaurav Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Chen, Shijie Zhao, Chengqiang Yang, Huabin Zhang, Zhongjian Huang, Junjia Zhang, Yanru Computer Vision Lab D-ITET Eth Zürich Switzerland Computer Vision Lab Ifi & Caidas University of Würzburg Germany Huawei Noah'Ark Lab Canada Huawei Consumer Business Group China Xiaomi Inc. China Harbin Institute of Technology China Megvii Technology China York University Toronto Canada Abu Dhabi United Arab Emirates Computer Vision Center Universitat Autonòma de Barcelona Spain Nanjing University of Science and Technology China Nanjing University of Information Science and Technology China China Fudan University China Videt Technology South China University of Technology China India University of Sindh Pakistan Iklab Inc. Korea Republic of Iklab Inc. Tech University of Korea Korea Republic of Xuzhou Medical University China Southwest Jiaotong University China

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image denoising (σ = 50) with a focus on the proposed solutions and results. The aim is to obtain a network design capable to produce high-quality results with the best performance measured by PSNR for image denoising. Independent additive white Gaussian noise (AWGN) is assumed and the noise level is 50. The challenge had 225 registered participants, and 16 teams made valid submissions. They gauge the state-of-the-art for image denoising. © 2023 ieee.

关键词： Image denoising

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PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks

PU-GCN: Point Cloud Upsampling using Graph Convolutional Net...

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

作者： Qian, Guocheng Abualshour, Abdulellah Li, Guohao Thabet, Ali Ghanem, Bernard King Abdullah Univ Sci & Technol KAUST Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves state-of-theart upsampling methods. For feature extraction, we also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds. We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference. Our code is publicly available at https://***/guochengqian/PU-GCN.

关键词： Convolutional codes computer vision Pipelines Performance gain Feature extraction pattern recognition

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StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place recognition

StructVPR: Distill Structural Knowledge with Weighting Sampl...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yanqing Shen Sanping Zhou Jingwen Fu Ruotong Wang Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University

Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.

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EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

EyePAD++: A Distillation-based approach for joint Eye Authen...

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2022 ieee/cvf conference on computer vision and pattern recognition, cvpr 2022

作者： Dhar, Prithviraj Kumar, Amit Kaplan, Kirsten Gupta, Khushi Ranjan, Rakesh Chellappa, Rama Johns Hopkins University United States Meta Reality Labs

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the 'versatility' of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality. © 2022 ieee.

关键词： Training Performance evaluation Image quality Iris Protocols Machine vision Image edge detection

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