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ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation

ADAS: A Direct Adaptation Strategy for Multi-Target Domain A...

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

作者： Lee, Seunghun Choi, Wonhyeok Kim, Changjae Choi, Minwoo Im, Sunghoon Dgist Department of Electrical Engineering and Computer Science Daegu Korea Republic of

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so, we design a multi-target domain transfer network (MTDT-Net) that aligns visual attributes across domains by transferring the domain distinctive features through a new target adaptive denormalization (TAD) module. Moreover, we propose a bi-directional adaptive region selection (BARS) that reduces the attribute ambiguity among the class labels by adaptively selecting the regions with consistent feature statistics. We show that our single MTDT-Net can synthesize visually pleasing domain transferred images with complex driving datasets, and BARS effectively filters out the unnecessary region of training images for each target domain. With the collaboration of MTDT-Net and BARS, our ADAS achieves state-of-the-art performance for multi-target domain adaptation (MTDA). To the best of our knowledge, our method is the first MTDA method that directly adapts to multiple domains in semantic segmentation. © 2022 ieee.

关键词： Semantic Segmentation

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NTIRE 2022 Challenge on Night Photography Rendering

NTIRE 2022 Challenge on Night Photography Rendering

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

作者： Ershov, Egor Savchik, Alex Shepelev, Denis Banic, Nikola Brown, Michael S. Timofte, Radu Koscevic, Karlo Freeman, Michael Tesalin, Vasily Bocharov, Dmitry Semenkov, Illya Subasic, Marko Loncaric, Sven Terekhin, Arseniy Liu, Shuai Feng, Chaoyu Wang, Hao Zhu, Ran Li, Yongqiang Lei, Lei Li, Zhihao Yi, Si Han, Ling-Hao Wu, Ruiqi Jin, Xin Guo, Chunle Kinli, Furkan Mentes, Sami Ozcan, Baris Kirac, Furkan Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Li, Wei Ma, Yipeng Wang, Tao Xu, Ruikang Song, Fenglong Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Kuo, Sy-Yen Liang, Zhexin Zhou, Shangchen Feng, Ruicheng Li, Chongyi Chen, Xiangyu Song, Binbin Zhang, Shile Liu, Lin Wang, Zhendong Ryu, Dohoon Bae, Hyokyoung Kwon, Taesung Desai, Chaitra Akalwadi, Nikhil Joshi, Amogh Mandi, Chinmayee Malagi, Sampada Uppin, Akash Reddy, Sai Sudheer Tabib, Ramesh Ashok Patil, Ujwala Mudenagudi, Uma Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia Gideon Bros Osijek Croatia York Univ Toronto ON Canada Swiss Fed Inst Technol Zurich Zurich Switzerland Univ Wurzburg Wurzburg Germany Univ Zagreb Fac Elect Engn & Comp Zagreb Croatia Michael Freeman Photog London England Xiaomi Inc Beijing Peoples R China Nanjing Univ Nanjing Peoples R China Nankai Univ Tianjin Peoples R China Wuhan Univ Technol Wuhan Peoples R China Ozyegin Univ Istanbul Turkey Univ Milano Bicocca Milan Italy Huawei Noahs Ark Lab Montreal PQ Canada Natl Taiwan Univ Grad Inst Elect Engn New Taipei Taiwan Natl Taiwan Univ Dept Elect Engn New Taipei Taiwan Nanyang Technol Univ Singapore Singapore Univ Macau Taipa Macao Peoples R China Univ Sci & Technol China Hefei Peoples R China Korea Adv Inst Sci & Technol Daejeon South Korea KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India

ISBN: (纸本)9781665487399

This paper reviews the NTIRE 2022 challenge on night photography rendering. The challenge solicited solutions that processed RAW camera images captured in night scenes to produce a photo-finished output image encoded in the standard RGB (sRGB) space. Given the subjective nature of this task, the proposed solutions were evaluated based on the mean opinions of viewers asked to judge the visual appearance of the results. Michael Freeman, a world-renowned photographer, further ranked the solutions with the highest mean opinion scores. A total of 13 teams competed in the final phase of the challenge. The proposed methods provided by the participating teams represent state-of-the-art performance in nighttime photography. Results from the various teams can be found here: https://***/

关键词： Photography Visualization computer vision conferences Rendering (computer graphics) Cameras pattern recognition

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AFRIFASHION1600: A Contemporary African Fashion Dataset for computer vision

AFRIFASHION1600: A Contemporary African Fashion Dataset for ...

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

作者： Oyewusi, Wuraola Fisayo Adekanmbi, Olubayo Ibejih, Sharon Osakuade, Opeyemi Okoh, Ifeoma Salami, Mary Data Sci Nigeria Lagos Nigeria

ISBN: (纸本)9781665448994

This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computer vision ***1600 dataset is available here.

关键词： computer vision conferences Computational modeling Machine learning pattern recognition Task analysis

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Learning Video Representations of Human Motion from Synthetic Data

Learning Video Representations of Human Motion from Syntheti...

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

作者： Guo, Xi Wu, Wei Wang, Dongliang Su, Jing Su, Haisheng Gan, Weihao Huang, Jian Yang, Qin Beihang University China SenseTime Research

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we take an early step towards video representation learning of human actions with the help of large-scale synthetic videos, particularly for human motion representation enhancement. Specifically, we first introduce an automatic action-related video synthesis pipeline based on a photorealistic video game. A large-scale human action dataset named GATA (GTA Animation Transformed Actions) is then built by the proposed pipeline, which includes 8.1 million action clips spanning over 28K action classes. Based on the presented dataset, we design a contrastive learning framework for human motion representation learning, which shows significant performance improvements on several typical video datasets for action recognition, e.g., Charades, HAA 500 and NTU-RGB. Besides, we further explore a domain adaptation method based on cross-domain positive pairs mining to alleviate the domain gap between synthetic and realistic data. Extensive properties analyses of learned representation are conducted to demonstrate the effectiveness of the proposed dataset for enhancing human motion representation learning. © 2022 ieee.

关键词： Representation learning computer vision Pipelines Games Animation Data mining

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Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models

Drop the GAN: In Defense of Patches Nearest Neighbors as Sin...

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

作者： Granot, Niv Feinstein, Ben Shocher, Assaf Bagon, Shai Irani, Michal The Weizmann Institute of Science Dept. of Computer Science and Applied Math Israel Israel

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Image manipulation dates back long before the deep learning era. The classical prevailing approaches were based on maximizing patch similarity between the input and generated output. Recently, single-image GANs were introduced as a superior and more sophisticated solution to image manipulation tasks. Moreover, they offered the opportunity not only to manipulate a given image, but also to generate a large and diverse set of different outputs from a single natural image. This gave rise to new tasks, which are considered 'GAN-only'. However, despite their impressiveness, single-image GANs require long training time (usually hours) for each image and each task and often suffer from visual artifacts. In this paper we revisit the classical patch-based methods, and show that - unlike previously believed - classical methods can be adapted to tackle these novel 'GAN-only' tasks. Moreover, they do so better and faster than single-image GAN-based methods. More specifically, we show that: (i) by introducing slight modifications, classical patch-based methods are able to unconditionally generate diverse images based on a single natural image;(ii) the generated output visual quality exceeds that of single-image GANs by a large margin (confirmed both quantitatively and qualitatively);(iii) they are orders of magnitude faster (runtime reduced from hours to seconds). 22This project received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 788535), and the Carolito Stiftung. Dr Bagon is a Robin Chemers Neustein AI Fellow. © 2022 ieee.

关键词： Training Deep learning Visualization Technological innovation computer vision Runtime Europe

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NPBG++: Accelerating Neural Point-Based Graphics

NPBG++: Accelerating Neural Point-Based Graphics

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

作者： Rakhimov, Ruslan Ardelean, Andrei-Timotei Lempitsky, Victor Burnaev, Evgeny Skolkovo Institute of Science and Technology Russia Yandex Russia Artificial Intelligence Research Institute Russia

ISBN: (纸本)9781665469463

We present a new system $(NPBG++)$ for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently lever-ages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graph-ics [1] in several important ways. By predicting the descrip-tors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suit-able for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality. Project page: https://***/npbgpp/. © 2022 ieee.

关键词： Rendering (computer graphics)

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Rethinking the Self-Attention in vision Transformers

Rethinking the Self-Attention in Vision Transformers

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

作者： Kim, Kyungmin Wu, Bichen Dai, Xiaoliang Zhang, Peizhao Yan, Zhicheng Vajda, Peter Kim, Seon Yonsei Univ Seoul South Korea Facebook Menlo Pk CA 94025 USA

ISBN: (纸本)9781665448994

Self-attention is a corner stone for transformer models. However, our analysis shows that self-attention in vision transformer inference is extremely sparse. When applying a sparsity constraint, our experiments on image (ImageNet-1K) and video (Kinetics-400) understanding show we can achieve 95% sparsity on the self-attention maps while maintaining the performance drop to be less than 2 points. This motivates us to rethink the role of self-attention in vision transformer models.

关键词： computer vision conferences Computational modeling pattern recognition

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On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders

On Disentanglement and Mutual Information in Semi-Supervised...

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

作者： Gordon Rodriguez, Elliott Columbia Univ Dept Stat New York NY 10027 USA

ISBN: (纸本)9781665448994

In the context of variational auto-encoders, learning disentangled latent variable representations remains a challenging problem. In this abstract, we consider the semi-supervised setting, in which the factors of variation are labelled for a small fraction of our samples. We examine how the quality of learned representations is affected by the dimension of the unsupervised component of the latent space. We also consider a variational lower bound for the mutual information between the data and the semi-supervised component of the latent space, and analyze its role in the context of disentangled representation learning.

关键词： computer vision conferences pattern recognition Mutual information

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 ieee/CVF conference on computer vision and pattern recognition, cvpr 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 ieee.

关键词： Autonomous driving

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 ieee/CVF conference on computer vision and pattern recognition, cvpr 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 ieee.

关键词： Multilayer neural networks

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