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检索条件"任意字段=2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005"

共 6545 条记录，以下是211-220 订阅

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OutfitTransformer: Outfit Representations for Fashion Recommendation

OutfitTransformer: Outfit Representations for Fashion Recomm...

引用

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

作者： Sarkar, Rohan Bodla, Navaneeth Vasileva, Mariya Lin, Yen-Liang Beniwal, Anurag Lu, Alan Medioni, Gerard Purdue Univ W Lafayette IN 47907 USA Amazon Seattle WA 98109 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Predicting outfit compatibility and retrieving complementary items are critical components for a fashion recommendation system. We present a scalable framework, Out-fitTransformer, that learns compatibility of the entire out-fit and supports large-scale complementary item retrieval. We model outfits as an unordered set of items and leverage self-attention mechanism to learn the relationships between items. We train the framework using a proposed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification. The generated target item embedding is then used to retrieve compatible items that match the outfit. Experimental results demonstrate that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.

关键词： computer vision conferences Task analysis Recommender systems pattern matching

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Dancing under the stars: video denoising in starlight

Dancing under the stars: video denoising in starlight

引用

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

作者： Monakhova, Kristina Richter, Stephan R. Waller, Laura Koltun, Vladlen Uc Berkeley United States Intel Labs

Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux illumination). In this paper, we dem... 详细信息

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

关键词： Semiconductor device modeling Noise reduction Lighting Stars Cameras Quality assessment pattern recognition

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Perceptual in-Loop Filter for Image and Video Compression

Perceptual in-Loop Filter for Image and Video Compression

引用

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

作者： Wang, Huairui Ren, Guangjie Ouyang, Tong Zhang, Junxi Han, Wenwei Liu, Zizheng Chen, Zhenzhong Wuhan Univ Wuhan Peoples R China Tencent Media Lab Shenzhen Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we introduce our hybrid image and video compression scheme enhanced by CNN-optimized in-loop filter. Specifically, a Structure Preserving in-Loop Filter (SPiLF) is incorporated in the hybrid video codec Enhanced Compression Model (ECM), where two branches, i.e., gradient branch and pixel branch, are developed based on the dense residual unit (DRU). To provide pleasant visual quality, the Generative adversarial networks (GAN) loss and LPIPS loss are further considered. Therefore, the proposal is mainly focusing on perceptual-friendly image compression for human vision, whilst video compression could be further investigated. The experiments show that the proposed method achieves advanced visual quality when compared to the traditional methods.

关键词： Visualization computer vision Image coding conferences Focusing Video compression Generative adversarial networks

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Temporal Driver Action Localization using Action Classification Methods

Temporal Driver Action Localization using Action Classificat...

引用

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

作者： Alyahya, Munirah Alghannam, Shahad Alhussan, Taghreed Saudi Technol & Secur Comprehens Control Co Riyadh Saudi Arabia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Driver distraction recognition is an essential computer vision task that can play a key role in increasing traffic safety and reducing traffic accidents. In this paper, we propose a temporal driver action localization (TDAL) framework for classifying driver distraction actions, as well as identifying the start and end time of a given driver action. The TDAL framework consists of three stages: preprocessing, 'which takes untrimmed video as input and generates multiple clips;action classification, which classifies the clips;and finally, the classifier output is sent to the temporal action localization to generate the start and end times of the distracted actions. The proposed framework achieves an F1 score of 27.06% on Track 3A2 dataset of NVIDIA AI City 2022 Challenge. The findings show that the TDAL framework contributes to fine-grained driver distraction recognition and paves the way for the development of smart and safe transportation. Code will be available soon.

关键词： Location awareness computer vision Urban areas Data preprocessing Transportation Classification algorithms Safety

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Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

Revisiting the Receptive Field of Conv-GRU in DROID-SLAM

引用

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

作者： Bangunharcana, Antyanta Kim, Soohyun Kim, Kyung-Soo Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

This work focuses on improving the Conv-GRU-based optical flow update within a DROID-SLAM framework. Prior optical flow models typically follow a UNet or coarse-to-fine architecture in order to extract long-range cross-correlation and context cues. This helps flow estimation in the presence of large motion and challenging image regions, e.g., textureless regions. We propose modifications to the Conv-GRU module which follows the rationale of these prior models by integrating (Atrous) Spatial Pyramid Pooling and global self-attention into the Conv-GRU block. By enlarging the receptive field through the aforementioned modifications, the model is able to integrate information from a larger context window, thus improving the robustness even when given inputs that comprise challenging image regions. We show empirically through extensive experiments the gain in accuracy through these modifications.

关键词： computer vision Simultaneous localization and mapping conferences Computational modeling Estimation computer architecture Robustness

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Multimodal Shape Completion via Implicit Maximum Likelihood Estimation

Multimodal Shape Completion via Implicit Maximum Likelihood ...

引用

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

作者： Arora, Himanshu Mishra, Saurabh Peng, Shichong Li, Ke Mahdavi-Amiri, Ali Simon Fraser Univ Burnaby BC Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Shape completion is the problem of completing partial input shapes such as partial scans. This problem finds important applications in computer vision and robotics due to issues such as occlusion or sparsity in real-world data. However, most of the existing research related to shape completion has been focused on completing shapes by learning a one-to-one mapping which limits the diversity and creativity of the produced results. We propose a novel multimodal shape completion technique that is effectively able to learn a one-to-many mapping and generates diverse complete shapes. Our approach is based on the conditional Implicit Maximum Likelihood Estimation (IMLE) technique wherein we condition our inputs on partial 3D point clouds. We extensively evaluate our approach by comparing it to various baselines both quantitatively and qualitatively. We show that our method is superior to alternatives in terms of completeness and diversity of shapes.

关键词： Point cloud compression Maximum likelihood estimation computer vision Three-dimensional displays Shape conferences pattern recognition

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DNAS:A Decoupled Global Neural Architecture Search Method

DNAS:A Decoupled Global Neural Architecture Search Method

引用

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

作者： Xu, Kepeng He, Gang Xidian Univ Xian Peoples R China Kuaishou Technol Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) can automatically design model architecture with better performance. Current researchers have searched for local architecture similar to block, then stacked to construct entire models, or searched the entire model based on a manually designed benchmark module. There is no method to directly search the architecture of the global(entire) model at the operation level. The purpose of this article is to search the entire model directly in the operation level search space. We analyzed the search space of past methods which searching for local architectures, then a working mode for global model architecture search named CAM is proposed. Proposed CAM decouples the architectural parameters of the entire model which can complete the entire model architecture search with few architecture parameters. In the experiment, the test error 2.68 % in CIFAR-10 is obtained by the proposed method at the global architecture level, which can compare with the stage-of-art local architecture search methods.

关键词： Analytical models computer vision Search methods conferences computer architecture Benchmark testing pattern recognition

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3D Room Layout Recovery Generalizing across Manhattan and Non-Manhattan Worlds

3D Room Layout Recovery Generalizing across Manhattan and No...

引用

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

作者： Jia, Haijing Yi, Hong Fujiki, Hirochika Zhang, Hengzhi Wang, Wei Odamaki, Makoto Ricoh Software Res Ctr Beijing Co Ltd Beijing Peoples R China Ricoh Co Ltd Tokyo Japan

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent 3D room layout recovery approaches mostly concentrate on Manhattan layouts, where the vertical walls are orthogonal with respect to each other, even though there are many rooms with non-Manhattan layouts in the real world. This paper presents a room layout recovery method generalizing across Manhattan and non-Manhattan worlds. Without introducing additional supervision, we extend current Manhattan layout recovery methods by predicting an extra surface normal feature, which is further used for an adaptive post-processing to reconstruct layouts of arbitrary shapes. Experimental results show that our method has a great improvement on non-Manhattan layouts while being capable of generalizing across Manhattan and non-Manhattan layouts.

关键词： Surface reconstruction computer vision Three-dimensional displays Shape conferences Layout Transformers

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Pseudo-label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection

Pseudo-label Generation and Various Data Augmentation for Se...

引用

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

作者： Yu, Jun Zhang, Liwen Du, Shenshen Chang, Hao Lu, Keda Zhang, Zhong Yu, Ye Wang, Lei Ling, Qiang Univ Sci & Technol China Hefei Anhui Peoples R China Hefei ZhanDa Intelligence Technol Co Ltd Hefei Anhui Peoples R China Hefei Univ Technol Hefei Anhui Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Semi-supervised learning is a highly researched problem, but existing semi-supervised object detection frameworks are based on RGB images, and existing pre-trained models cannot be used for hyperspectral images. To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images. Next, in order to make full use of the unlabeled training set, we generate pseudo-labels with the model trained in the first stage and mix the obtained pseudo-labels with the labeled training set. Then, a large number of strong data augmentation methods are added to make the final model better. We achieve the SOTA, with an AP of 26.35, on the Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC) in the cvpr 2022 Perception Beyond the Visible Spectrum Workshop, and win the first place in this Challenge.

关键词： Training computer vision conferences Object detection Semisupervised learning Data models pattern recognition

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DAtRNet: Disentangling Fashion Attribute Embedding for Substitute Item Retrieval

DAtRNet: Disentangling Fashion Attribute Embedding for Subst...

引用

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

作者： Bhattacharya, Gaurab Kilari, Nikhil Gubbi, Jayavardhana Lakshmi, Bagya, V Pal, Arpan Balamuralidhar, P. TCS Res Mumbai Maharashtra India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Interactive substitute recommendation for fashion products improves the online retail customer experience. Traditional fashion search platforms incorporate product metadata between the query products and the products to be retrieved. In this paper, we propose DAtRNet, an attribute representation network to disentangle the features in the query product. It is used to recommend attribute-aware substitute items based on the conditional similarity of the retrieved products. The proposed architecture relies on attribute-level similarity providing a fine-grained recommendation. In addition, a concurrent axial attention mechanism is proposed that generates global information embedding and adaptively re-calibrates the soft attention masks. Overall, the end-to-end framework enables the system to disentangle the attribute features and independently deals with them to enhance its flexibility towards one or multiple attributes. The proposed method outperforms the state-ofthe-art by a significant margin.

关键词： computer vision conferences computer architecture Metadata Search problems Feature extraction pattern recognition

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