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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Transferable Semantic Augmentation for Domain Adaptation

Transferable Semantic Augmentation for Domain Adaptation

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Shuang Xie, Mixue Gong, Kaixiong Liu, Chi Harold Wang, Yulin Li, Wei Beijing Inst Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Domain adaptation has been widely explored by transferring the knowledge from a label-rich source domain to a related but unlabeled target domain. Most existing domain adaptation algorithms attend to adapting feature representations across two domains with the guidance of a shared source-supervised classifier. However, such classifier limits the generalization ability towards unlabeled target recognition. To remedy this, we propose a Transferable Semantic Augmentation (TSA) approach to enhance the classifier adaptation ability through implicitly generating source features towards target semantics. Specifically, TSA is inspired by the fact that deep feature transformation towards a certain direction can be represented as meaningful semantic altering in the original input space. Thus, source features can be augmented to effectively equip with target semantics to train a more transferable classifier. To achieve this, for each class, we first use the inter-domain feature mean difference and target intra-class feature covariance to construct a multivariate normal distribution. Then we augment source features with random directions sampled from the distribution class-wisely. Interestingly, such source augmentation is implicitly implemented through an expected transferable cross-entropy loss over the augmented source distribution, where an upper bound of the expected loss is derived and minimized, introducing negligible computational overhead. As a light-weight and general technique, TSA can be easily plugged into various domain adaptation methods, bringing remarkable improvements. Comprehensive experiments on cross-domain benchmarks validate the efficacy of TSA.

关键词： computer vision Upper bound Target recognition Semantics Gaussian distribution Benchmark testing pattern recognition

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Progressive Semantic Segmentation

Progressive Semantic Segmentation

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chuong Huynh Anh Tuan Tran Khoa Luu Minh Hoai VinAI Res Hanoi Vietnam VinUniversity Hanoi Vietnam Univ Arkansas Fayetteville AR 72701 USA SUNY Stony Brook Stony Brook NY 11790 USA

ISBN: (纸本)9781665445092

The objective of this work is to segment high-resolution images without overloading GPU memory usage or losing the fine details in the output segmentation map. The memory constraint means that we must either downsample the big image or divide the image into local patches for separate processing. However, the former approach would lose the fine details, while the latter can be ambiguous due to the lack of a global picture. In this work, we present MagNet, a multi-scale framework that resolves local ambiguity by looking at the image at multiple magnification levels. MagNet has multiple processing stages, where each stage corresponds to a magnification level, and the output of one stage is fed into the next stage for coarse-to-fine information propagation. Each stage analyzes the image at a higher resolution than the previous stage, recovering the previously lost details due to the lossy downsampling step, and the segmentation output is progressively refined through the processing stages. Experiments on three high-resolution datasets of urban views, aerial scenes, and medical images show that MagNet consistently outperforms the state-of-the-art methods by a significant margin. Code is available at https://***/VinAIResearch/MagNet.

关键词： Image segmentation computer vision Image resolution Codes Magnetic separation Magnetic resonance imaging Semantics

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AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

AdvSim: Generating Safety-Critical Scenarios for Self-Drivin...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Jingkang Pun, Ava Tu, James Manivasagam, Sivabalan Sadat, Abbas Casas, Sergio Ren, Mengye Urtasun, Raquel Univ Toronto Toronto ON Canada Uber ATG Pittsburgh PA 15201 USA Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781665445092

As self-driving systems become better, simulating scenarios where the autonomy stack may fail becomes more important. Traditionally, those scenarios are generated for a few scenes with respect to the planning module that takes ground-truth actor states as input. This does not scale and cannot identify all possible autonomy failures, such as perception failures due to occlusion. In this paper, we propose AdvSim, an adversarial framework to generate safetycritical scenarios for any LiDAR-based autonomy system. Given an initial traffic scenario, AdvSim modifies the actors' trajectories in a physically plausible manner and updates the LiDAR sensor data to match the perturbed world. Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack. Our experiments show that our approach is general and can identify thousands of semantically meaningful safety-critical scenarios for a wide range of modern self-driving systems. Furthermore, we show that the robustness and safety of these systems can be further improved by training them with scenarios generated by AdvSim.

关键词： Training computer vision Laser radar Robustness Safety Trajectory Planning

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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...

引用

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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Research on photovoltaic power generation project progress drone identification system based on computer convolutional neural network 2

Research on photovoltaic power generation project progress d...

引用

2nd International conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Wang, Yong Min, Xiangli Chen, Guang Fu, Wenjie Fu, Jiangque Yi, Le'an Jiang, Qiao Li, Hongming China Energy Jiangxi New Energy Industry Co. Ltd Nanchang China China Energy Investment Group Jiangxi Power Co. Ltd Nanchang China Ctrl. S. China Electric Power Design Institute Co. Ltd. of China Power Engineering Consulting Group Wuhan China

ISBN: (数字)9798350386639

ISBN: (纸本)9798350386639

This study aims to develop an innovative computer convolutional neural network (CNN) UAV recognition system for the progress of photovoltaic power generation projects. Its core goal is to use the rich visual data collected from the aerial perspective of the UAV and deep learning algorithms to realize the automatic and fine identification and monitoring of the progress of photovoltaic power generation projects. During the execution of photovoltaic power plant projects, ensuring all construction phases proceed according to schedule is critically important. However, conventional methods for monitoring project progress often face limitations imposed by constraints such as human resources, time constraints, and spatial coverage, making it challenging to attain comprehensive, prompt, and precise oversight. Therefore, this study uses UAV for aerial patrol shooting to obtain high-quality, multi-angle and large-scale construction site image data, which will serve as an important input to the convolutional neural network model. Deep learning-based computer convolutional neural networks are designed to understand and parse these complex construction site images. By training a large number of marked drone images, the system can effectively extract key features and pattern recognition, including but not limited to the placement location and density of photovoltaic panels, the construction stage of the support structure, and the installation status of electrical equipment, so as to accurately quantify the real-time progress of engineering construction. In addition, the system also has the ability to dynamically predict the construction progress, according to the identified data trends, the future construction stage may encounter problems and delays for early warning, to provide strong information support for decision makers. The breakthrough of this research lies in the deep integration of cutting-edge computer vision technology and UAV application into photovoltaic power generat

关键词： Convolutional neural networks

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Towards Fair Federated Learning with Zero-Shot Data Augmentation

Towards Fair Federated Learning with Zero-Shot Data Augmenta...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hao, Weituo El-Khamy, Mostafa Lee, Jungwon Zhang, Jianyi Liang, Kevin J. Chen, Changyou Carin, Lawrence Duke Univ Durham NC 27706 USA Samsung Suwon South Korea SUNY Buffalo Buffalo NY USA

ISBN: (纸本)9781665448994

Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models, while having no access to the client data. Although it is recognized that statistical heterogeneity of the client local data yields slower global model convergence, it is less commonly recognized that it also yields a biased federated global model with a high variance of accuracy across clients. In this work, we aim to provide federated learning schemes with improved fairness. To tackle this challenge, we propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity, and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server). Empirical results on a suite of datasets demonstrate the effectiveness of our methods on simultaneously improving the test accuracy and fairness.

关键词： computer vision computer aided instruction Distance learning conferences Distributed databases Collaborative work Data models

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Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation

Smoothing the Disentangled Latent Style Space for Unsupervis...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yahui Sangineto, Enver Chen, Yajing Bao, Linchao Zhang, Haoxian Sebe, Nicu Lepri, Bruno Wang, Wei De Nadai, Marco Univ Trento Trento Italy Tencent AI Lab Shenzhen Peoples R China Fdn Bruno Kessler Povo Italy

ISBN: (纸本)9781665445092

Image-to-Image (I2I) multi-domain translation models are usually evaluated also using the quality of their semantic interpolation results. However, state-of-the-art models frequently show abrupt changes in the image appearance during interpolation, and usually perform poorly in interpolations across domains. In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation. Moreover, we propose a novel evaluation metric to properly measure the smoothness of latent style space of I2I translation models. The proposed method can be plugged in existing translation approaches, and our extensive experiments on different datasets show that it can significantly boost the quality of the generated images and the graduality of the interpolations.

关键词： Training Interpolation computer vision Smoothing methods Protocols Computational modeling Semantics

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Depth Completion with Twin Surface Extrapolation at Occlusion Boundaries

Depth Completion with Twin Surface Extrapolation at Occlusio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Imran, Saif Liu, Xiaoming Morris, Daniel Michigan State Univ E Lansing MI 48824 USA

ISBN: (纸本)9781665445092

Depth completion starts from a sparse set of known depth values and estimates the unknown depths for the remaining image pixels. Most methods model this as depth interpolation and erroneously interpolate depth pixels into the empty space between spatially distinct objects, resulting in depth-smearing across occlusion boundaries. Here we propose a multi-hypothesis depth representation that explicitly models both foreground and background depths in the difficult occlusion-boundary regions. Our method can be thought of as performing twin-surface extrapolation, rather than interpolation, in these regions. Next our method fuses these extrapolated surfaces into a single depth image leveraging the image data. Key to our method is the use of an asymmetric loss function that operates on a novel twin-surface representation. This enables us to train a network to simultaneously do surface extrapolation and surface fusion. We characterize our loss function and compare with other common losses. Finally, we validate our method on three different datasets;KITTI, an outdoor real-world dataset, NYU2, indoor real-world depth dataset and Virtual KITTI, a photo-realistic synthetic dataset with dense groundtruth, and demonstrate improvement over the state of the art.

关键词： Extrapolation Interpolation computer vision Fuses Estimation Channel estimation Robustness

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BasisNet: Two-stage Model Synthesis for Efficient Inference

BasisNet: Two-stage Model Synthesis for Efficient Inference

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Mingda Chu, Chun-Te Zhmoginov, Andrey Howard, Andrew Jou, Brendan Zhu, Yukun Zhang, Li Hwa, Rebecca Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

In this work, we present BasisNet which combines recent advancements in efficient neural network architectures, conditional computation, and early termination in a simple new form. Our approach incorporates a lightweight model to preview the input and generate input-dependent combination coefficients, which later controls the synthesis of a more accurate specialist model to make final prediction. The two-stage model synthesis strategy can be applied to any network architectures and both stages are jointly trained. We also show that proper training recipes are critical for increasing generalizability for such high capacity neural networks. On ImageNet classification benchmark, our BasisNet with MobileNets as backbone demonstrated clear advantage on accuracy-efficiency trade-off over several strong baselines. Specifically, BasisNet-MobileNetV3 obtained 80.3% top-1 accuracy with only 290M MultiplyAdd operations, halving the computational cost of previous state-of-the-art without sacrificing accuracy. With early termination, the average cost can be further reduced to 198M MAdds while maintaining accuracy of 80.0% on ImageNet.

关键词： Training computer vision Computational modeling conferences Neural networks computer architecture Predictive models

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Discovering Multi-Hardware Mobile Models via Architecture Search

Discovering Multi-Hardware Mobile Models via Architecture Se...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chu, Grace Arikan, Okan Bender, Gabriel Wang, Weijun Brighton, Achille Kindermans, Pieter-Jan Liu, Hanxiao Akin, Berkin Gupta, Suyog Howard, Andrew Google LLC Mountain View CA 94043 USA

ISBN: (纸本)9781665448994

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on multiple hardware, having different single-hardware models across the deployed hardware makes it hard to guarantee consistent outputs across hardware and duplicates engineering work for debugging and fixing. To minimize such deployment cost, we propose an alternative solution, multi-hardware models, where a single architecture is developed for multiple hardware. With thoughtful search space design and incorporating the proposed multi-hardware metrics in neural architecture search, we discover multi-hardware models that give state-of-the-art (SoTA) performance across multiple hardware in both average and worse case scenarios. For performance on individual hardware, the single multi-hardware model yields similar or better results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which was achieved by different models, while having similar performance with MobilenetV3 Large Minimalistic model on mobile CPU.(1)

关键词： computer vision conferences Graphics processing units Focusing computer architecture Debugging Extraterrestrial measurements

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