2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)最新文献_第6页

SIUNet: Sparsity Invariant U-Net for Edge-Aware Depth Completion SIUNet:边缘感知深度补全的稀疏不变U-Net

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00577

A. Ramesh, F. Giovanneschi, M. González-Huici

引用次数: 1

AdvisIL - A Class-Incremental Learning Advisor AdvisIL -一个班级增量学习顾问

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00243

Eva Feillet, Grégoire Petit, Adrian-Stefan Popescu, M. Reyboz, C. Hudelot

{"title":"AdvisIL - A Class-Incremental Learning Advisor","authors":"Eva Feillet, Grégoire Petit, Adrian-Stefan Popescu, M. Reyboz, C. Hudelot","doi":"10.1109/WACV56688.2023.00243","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00243","url":null,"abstract":"Recent class-incremental learning methods combine deep neural architectures and learning algorithms to handle streaming data under memory and computational constraints. The performance of existing methods varies depending on the characteristics of the incremental process. To date, there is no other approach than to test all pairs of learning algorithms and neural architectures on the training data available at the start of the learning process to select a suited algorithm-architecture combination. To tackle this problem, in this article, we introduce AdvisIL, a method which takes as input the main characteristics of the incremental process (memory budget for the deep model, initial number of classes, size of incremental steps) and recommends an adapted pair of learning algorithm and neural architecture. The recommendation is based on a similarity between the user-provided settings and a large set of pre-computed experiments. AdvisIL makes class-incremental learning easier, since users do not need to run cumbersome experiments to design their system. We evaluate our method on four datasets under six incremental settings and three deep model sizes. We compare six algorithms and three deep neural architectures. Results show that AdvisIL has better overall performance than any of the individual combinations of a learning algorithm and a neural architecture. AdvisIL’s code is available at https://github.com/EvaJF/AdvisIL.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131350214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Heightfields for Efficient Scene Reconstruction for AR 用于AR高效场景重建的高度场

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00580

Jamie Watson, S. Vicente, Oisin Mac Aodha, Clément Godard, G. Brostow, Michael Firman

引用次数: 0

Fine Gaze Redirection Learning with Gaze Hardness-aware Transformation 基于注视硬度感知变换的精细注视重定向学习

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00346

Sangjin Park, D. Kim, B. Song

{"title":"Fine Gaze Redirection Learning with Gaze Hardness-aware Transformation","authors":"Sangjin Park, D. Kim, B. Song","doi":"10.1109/WACV56688.2023.00346","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00346","url":null,"abstract":"The gaze redirection is a task to adjust the gaze of a given face or eye image toward the desired direction and aims to learn the gaze direction of a face image through a neural network-based generator. Considering that the prior arts have learned coarse gaze directions, learning fine gaze directions is very challenging. In addition, explicit discriminative learning of high-dimensional gaze features has not been reported yet. This paper presents solutions to overcome the above limitations. First, we propose the feature-level transformation which provides gaze features corresponding to various gaze directions in the latent feature space. Second, we propose a novel loss function for discriminative learning of gaze features. Specifically, features with insignificant or irrelevant effects on gaze (e.g., head pose and appearance) are set as negative pairs, and important gaze features are set as positive pairs, and then pair-wise similarity learning is performed. As a result, the proposed method showed a redirection error of only 2° for the Gaze-Capture dataset. This is a 10% better performance than a state-of-the-art method, i.e., STED. Additionally, the rationale for why latent features of various attributes should be discriminated is presented through activation visualization. Code is available at https://github.com/san9569/Gaze-Redir-Learning","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130213340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast Differentiable Transient Rendering for Non-Line-of-Sight Reconstruction 非视距重建的快速可微分瞬态渲染

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00308

Markus Plack, C. Callenberg, M. Schneider, M. Hullin

{"title":"Fast Differentiable Transient Rendering for Non-Line-of-Sight Reconstruction","authors":"Markus Plack, C. Callenberg, M. Schneider, M. Hullin","doi":"10.1109/WACV56688.2023.00308","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00308","url":null,"abstract":"Research into non-line-of-sight imaging problems has gained momentum in recent years motivated by intriguing prospective applications in e.g. medicine and autonomous driving. While transient image formation is well understood and there exist various reconstruction approaches for non-line-of-sight scenes that combine efficient forward renderers with optimization schemes, those approaches suffer from runtimes in the order of hours even for moderately sized scenes. Furthermore, the ill-posedness of the inverse problem often leads to instabilities in the optimization.Inspired by the latest advances in direct-line-of-sight inverse rendering that have led to stunning results for reconstructing scene geometry and appearance, we present a fast differentiable transient renderer that accelerates the inverse rendering runtime to minutes on consumer hardware, making it possible to apply inverse transient imaging on a wider range of tasks and in more time-critical scenarios. We demonstrate its effectiveness on a series of applications using various datasets and show that it can be used for self-supervised learning.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128725960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

D2F2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation 基于渐进式领域自适应的弱监督目标检测的学习对象建议

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00011

Yuting Wang, Ricardo Guerrero, V. Pavlovic

{"title":"D2F2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation","authors":"Yuting Wang, Ricardo Guerrero, V. Pavlovic","doi":"10.1109/WACV56688.2023.00011","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00011","url":null,"abstract":"Weakly-supervised object detection (WSOD) models attempt to leverage image-level annotations in lieu of accurate but costly-to-obtain object localization labels. This oftentimes leads to substandard object detection and lo-calization at inference time. To tackle this issue, we propose D2F2WOD, a Dual-Domain Fully-to-Weakly Supervised Object Detection framework that leverages synthetic data, annotated with precise object localization, to supplement a natural image target domain, where only image-level labels are available. In its warm-up domain adaptation stage, the model learns a fully-supervised object detector (FSOD) to improve the precision of the object proposals in the target domain, and at the same time learns target-domain-specific and detection-aware proposal features. In its main WSOD stage, a WSOD model is specifically tuned to the target domain. The feature extractor and the object proposal generator of the WSOD model are built upon the fine-tuned FSOD model. We test D2F2WOD on five dual-domain image benchmarks. The results show that our method results in consistently improved object detection and localization compared with state-of-the-art methods.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"56 42","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Generative Alignment of Posterior Probabilities for Source-free Domain Adaptation 无源域自适应的后验概率生成对齐

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00411

S. Chhabra, Hemanth Venkateswara, Baoxin Li

引用次数: 2

Computer Vision for Ocean Eddy Detection in Infrared Imagery 红外图像中海洋涡流检测的计算机视觉

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00633

Evangelos Moschos, Alisa Kugusheva, Paul Coste, A. Stegner

{"title":"Computer Vision for Ocean Eddy Detection in Infrared Imagery","authors":"Evangelos Moschos, Alisa Kugusheva, Paul Coste, A. Stegner","doi":"10.1109/WACV56688.2023.00633","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00633","url":null,"abstract":"Reliable and precise detection of ocean eddies can significantly improve the monitoring of the ocean surface and subsurface dynamics, besides the characterization of local hydrographical and biological properties, or the concentration pelagic species. Today, most of the eddy detection algorithms operate on satellite altimetry gridded observations, which provide daily maps of sea surface height and surface geostrophic velocity. However, the reliability and the spatial resolution of altimetry products is limited by the strong spatio-temporal averaging of the mapping procedure. Yet, the availability of high-resolution satellite imagery makes real-time object detection possible at a much finer scale, via advanced computer vision methods. We propose a novel eddy detection method via a transfer learning schema, using the ground truth of high-resolution ocean numerical models to link the characteristic streamlines of eddies with their signature (gradients, swirls, and filaments) on Sea Surface Temperature (SST). A trained, multi-task convolutional neural network is then employed to segment infrared satellite imagery of SST in order to retrieve the accurate position, size, and form of each detected eddy. The EddyScan-SST is an operational oceanographic module that provides, in real-time, key information on the ocean dynamics to maritime stakeholders.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126905283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Couplformer: Rethinking Vision Transformer with Coupling Attention 耦合器:重新思考视觉变压器与耦合注意

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00641

Hai Lan, Xihao Wang, Hao Shen, Peidong Liang, Xian Wei

{"title":"Couplformer: Rethinking Vision Transformer with Coupling Attention","authors":"Hai Lan, Xihao Wang, Hao Shen, Peidong Liang, Xian Wei","doi":"10.1109/WACV56688.2023.00641","DOIUrl":"https://doi.org/10.1109/WACV56688.2023.00641","url":null,"abstract":"With the development of the self-attention mechanism, the Transformer model has demonstrated its outstanding performance in the computer vision domain. However, the massive computation brought from the full attention mechanism became a heavy burden for memory consumption. Sequentially, the limitation of memory consumption hinders the deployment of the Transformer model on the embedded system where the computing resources are limited. To remedy this problem, we propose a novel memory economy attention mechanism named Couplformer, which decouples the attention map into two sub-matrices and generates the alignment scores from spatial information. Our method enables the Transformer model to improve time and memory efficiency while maintaining expressive power. A series of different scale image classification tasks are applied to evaluate the effectiveness of our model. The result of experiments shows that on the ImageNet-1K classification task, the Couplformer can significantly decrease 42% memory consumption compared with the regular Transformer. Meanwhile, it accesses sufficient accuracy requirements, which outperforms 0.56% on Top-1 accuracy and occupies the same memory footprint. Besides, the Couplformer achieves state-of-art performance in MS COCO 2017 object detection and instance segmentation tasks. As a result, the Couplformer can serve as an efficient backbone in visual tasks and provide a novel perspective on deploying attention mechanisms for researchers.","PeriodicalId":270631,"journal":{"name":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Robust and Efficient Alignment of Calcium Imaging Data through Simultaneous Low Rank and Sparse Decomposition 基于低秩和稀疏同时分解的钙成像数据鲁棒高效对齐

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2023-01-01 DOI: 10.1109/WACV56688.2023.00198

Junmo Cho, Seungjae Han, Eun-Seo Cho, Kijung Shin, Young-Gyu Yoon

引用次数: 0