2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)最新文献_第9页

X-MIR: EXplainable Medical Image Retrieval X-MIR:可解释医学图像检索

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00161

Brian Hu, Bhavan Kumar Vasu, Anthony J. Hoogs

{"title":"X-MIR: EXplainable Medical Image Retrieval","authors":"Brian Hu, Bhavan Kumar Vasu, Anthony J. Hoogs","doi":"10.1109/WACV51458.2022.00161","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00161","url":null,"abstract":"Despite significant progress in the past few years, machine learning systems are still often viewed as \"black boxes,\" which lack the ability to explain their output decisions. In high-stakes situations such as healthcare, there is a need for explainable AI (XAI) tools that can help open up this black box. In contrast to approaches which largely tackle classification problems in the medical imaging domain, we address the less-studied problem of explainable image retrieval. We test our approach on a COVID-19 chest X-ray dataset and the ISIC 2017 skin lesion dataset, showing that saliency maps help reveal the image features used by models to determine image similarity. We evaluated three different saliency algorithms, which were either occlusion-based, attention-based, or relied on a form of activation mapping. We also develop quantitative evaluation metrics that allow us to go beyond simple qualitative comparisons of the different saliency algorithms. Our results have the potential to aid clinicians when viewing medical images and addresses an urgent need for interventional tools in response to COVID-19. The source code is publicly available at: https://gitlab.kitware.com/brianhhu/x-mir.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133179525","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: 28

Self-supervised Test-time Adaptation on Video Data 视频数据的自监督测试时间适应

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00266

Fatemeh Azimi, Sebastián M. Palacio, Federico Raue, Jörn Hees, Luca Bertinetto, A. Dengel

{"title":"Self-supervised Test-time Adaptation on Video Data","authors":"Fatemeh Azimi, Sebastián M. Palacio, Federico Raue, Jörn Hees, Luca Bertinetto, A. Dengel","doi":"10.1109/WACV51458.2022.00266","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00266","url":null,"abstract":"In typical computer vision problems revolving around video data, pre-trained models are simply evaluated at test time, without adaptation. This general approach clearly cannot capture the shifts that will likely arise between the distributions from which training and test data have been sampled. Adapting a pre-trained model to a new video en-countered at test time could be essential to avoid the potentially catastrophic effects of such shifts. However, given the inherent impossibility of labeling data only available at test-time, traditional \"fine-tuning\" techniques cannot be lever-aged in this highly practical scenario. This paper explores whether the recent progress in test-time adaptation in the image domain and self-supervised learning can be lever-aged to adapt a model to previously unseen and unlabelled videos presenting both mild (but arbitrary) and severe covariate shifts. In our experiments, we show that test-time adaptation approaches applied to self-supervised methods are always beneficial, but also that the extent of their effectiveness largely depends on the specific combination of the algorithms used for adaptation and self-supervision, and also on the type of covariate shift taking place.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132446911","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: 14

Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography 弱监督卷积神经网络在脑血管造影血管分割中的应用

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00328

Arvind Vepa, Andy Choi, Noor Nakhaei, Wonjun Lee, Noah Stier, Andrew Vu, Greyson Jenkins, Xiaoyan Yang, Manjot Shergill, Moira Desphy, K. Delao, M. Levy, Cristopher Garduno, Lacy Nelson, Wan-Ching Liu, Fan Hung, F. Scalzo

{"title":"Weakly-Supervised Convolutional Neural Networks for Vessel Segmentation in Cerebral Angiography","authors":"Arvind Vepa, Andy Choi, Noor Nakhaei, Wonjun Lee, Noah Stier, Andrew Vu, Greyson Jenkins, Xiaoyan Yang, Manjot Shergill, Moira Desphy, K. Delao, M. Levy, Cristopher Garduno, Lacy Nelson, Wan-Ching Liu, Fan Hung, F. Scalzo","doi":"10.1109/WACV51458.2022.00328","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00328","url":null,"abstract":"Automated vessel segmentation in cerebral digital subtraction angiography (DSA) has significant clinical utility in the management of cerebrovascular diseases. Although deep learning has become the foundation for state-of-the-art image segmentation, a significant amount of labeled data is needed for training. Furthermore, due to domain differences, pre-trained networks cannot be applied to DSA data out-of-the-box. To address this, we propose a novel learning framework, which utilizes an active contour model for weak supervision and low-cost human-in-the-loop strategies to improve weak label quality. Our study produces several significant results, including state-of-the-art results for cerebral DSA vessel segmentation, which exceed human annotator quality, and an analysis of annotation cost and model performance trade-offs when utilizing weak supervision strategies. For comparison purposes, we also demonstrate our approach on the Digital Retinal Images for Vessel Extraction (DRIVE) dataset. Additionally, we will be publicly releasing code to reproduce our methodology and our dataset, the largest known high-quality annotated cerebral DSA vessel segmentation dataset.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132428115","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: 5

SWAG-V: Explanations for Video using Superpixels Weighted by Average Gradients swagv:使用平均梯度加权的超像素视频解释

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00164

Thomas Hartley, K. Sidorov, Christopher Willis, A. D. Marshall

引用次数: 4

Fast and Efficient Restoration of Extremely Dark Light Fields 快速和有效的恢复极端黑暗的光场

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00321

Mohit Lamba, K. Mitra

{"title":"Fast and Efficient Restoration of Extremely Dark Light Fields","authors":"Mohit Lamba, K. Mitra","doi":"10.1109/WACV51458.2022.00321","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00321","url":null,"abstract":"The ability of Light Field (LF) cameras to capture the 3D geometry of a scene in a single photographic exposure has become central to several applications ranging from passive depth estimation to post-capture refocusing and view synthesis. But these LF applications break down in extreme low-light conditions due to excessive noise and poor image photometry. Existing low-light restoration techniques are inappropriate because they either do not leverage LF’s multi-view perspective or have enormous time and memory complexity. We propose a three-stage network that is simultaneously fast and accurate for real world applications. Our accuracy comes from the fact that our three stage architecture utilizes global, local and view-specific information present in low-light LFs and fuse them using an RNN inspired feedforward network. We are fast because we restore multiple views simultaneously and so require less number of forward passes. Besides these advantages, our network is flexible enough to restore a m × m LF during inference even if trained for a smaller n × n (n < m) LF without any finetuning. Extensive experiments on real low-light LF demonstrate that compared to the current state-of-the-art, our model can achieve up to 1 dB higher restoration PSNR, with 9× speedup, 23% smaller model size and about 5× lower floating-point operations.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116346339","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

SC-UDA: Style and Content Gaps aware Unsupervised Domain Adaptation for Object Detection 面向对象检测的风格和内容差距感知的无监督域自适应

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00113

Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen

{"title":"SC-UDA: Style and Content Gaps aware Unsupervised Domain Adaptation for Object Detection","authors":"Fuxun Yu, Di Wang, Yinpeng Chen, Nikolaos Karianakis, Tong Shen, Pei Yu, Dimitrios Lymberopoulos, Sidi Lu, Weisong Shi, Xiang Chen","doi":"10.1109/WACV51458.2022.00113","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00113","url":null,"abstract":"Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA) is a promising approach to adapt detectors for new domains/environments without any expensive label cost. Previous mainstream UDA works for object detection usually focused on image-level and/or feature-level adaptation by using adversarial learning methods. In this work, we show that such adversarial-based methods can only reduce domain style gap, but cannot address the domain content gap that is also important for object detectors. To overcome this limitation, we propose the SC-UDA framework to concurrently reduce both gaps: We propose fine-grained domain style transfer to reduce the style gaps with finer image details preserved for detecting small objects; Then we leverage the pseudo label-based self-training to reduce content gaps; To address pseudo label error accumulation during self-training, novel optimizations are proposed, including uncertainty-based pseudo labeling and imbalanced mini-batch sampling strategy. Experiment results show that our approach consistently outperforms prior state-of-the-art methods (up to 8.6%, 2.7% and 2.5% mAP on three UDA benchmarks).","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122992057","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: 17

Deep Feature Prior Guided Face Deblurring 深度特征先验引导人脸去模糊

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00096

S. Jung, Tae Bok Lee, Y. S. Heo

{"title":"Deep Feature Prior Guided Face Deblurring","authors":"S. Jung, Tae Bok Lee, Y. S. Heo","doi":"10.1109/WACV51458.2022.00096","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00096","url":null,"abstract":"Most recent face deblurring methods have focused on utilizing facial shape priors such as face landmarks and parsing maps. While these priors can provide facial geometric cues effectively, they are insufficient to contain local texture details that act as important clues to solve face deblurring problem. To deal with this, we focus on estimating the deep features of pre-trained face recognition networks (e.g., VGGFace network) that include rich information about sharp faces as a prior, and adopt a generative adversarial network (GAN) to learn it. To this end, we propose a deep feature prior guided network (DFPGnet) that restores facial details using the estimated the deep feature prior from a blurred image. In our DFPGnet, the generator is divided into two streams including prior estimation and deblurring streams. Since the estimated deep features of the prior estimation stream are learned from the VGGFace network which is trained for face recognition not for deblurring, we need to alleviate the discrepancy of feature distributions between the two streams. Therefore, we present feature transform modules at the connecting points of the two streams. In addition, we propose a channel-attention feature discriminator and prior loss, which encourages the generator to focus on more important channels for deblurring among the deep feature prior during training. Experimental results show that our method achieves state-of-the-art performance both qualitatively and quantitatively.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124951899","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: 6

Lane-Level Street Map Extraction from Aerial Imagery 基于航拍图像的巷级街道地图提取

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00156

Songtao He, Harinarayanan Balakrishnan

引用次数: 16

Learning Temporal Video Procedure Segmentation from an Automatically Collected Large Dataset 从自动收集的大数据集学习视频过程的时间分割

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00279

Lei Ji, Chenfei Wu, Daisy Zhou, Kun Yan, Edward Cui, Xilin Chen, Nan Duan

{"title":"Learning Temporal Video Procedure Segmentation from an Automatically Collected Large Dataset","authors":"Lei Ji, Chenfei Wu, Daisy Zhou, Kun Yan, Edward Cui, Xilin Chen, Nan Duan","doi":"10.1109/WACV51458.2022.00279","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00279","url":null,"abstract":"Temporal Video Segmentation (TVS) is a fundamental video understanding task and has been widely researched in recent years. There are two subtasks of TVS: Video Action Segmentation (VAS) and Video Procedure Segmentation (VPS): VAS aims to recognize what actions happen in-side the video while VPS aims to segment the video into a sequence of video clips as a procedure. The VAS task inevitably relies on pre-defined action labels and is thus hard to scale to various open-domain videos. To overcome this limitation, the VPS task tries to divide a video into several category-independent procedure segments. However, the existing dataset for the VPS task is small (2k videos) and lacks diversity (only cooking domain). To tackle these problems, we collect a large and diverse dataset called TIPS, specifically for the VPS task. TIPS contains 63k videos including more than 300k procedure segments from instructional videos on YouTube, which covers plenty of how-to areas such as cooking, health, beauty, parenting, gardening, etc. We then propose a multi-modal Transformer with Gaussian Boundary Detection (MT-GBD) model for VPS, with the backbone of the Transformer and Convolution. Furthermore, we propose a new EIOU metric for the VPS task, which helps better evaluate VPS quality in a more comprehensive way. Experimental results show the effectiveness of our proposed model and metric.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125158199","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

SporeAgent: Reinforced Scene-level Plausibility for Object Pose Refinement SporeAgent:增强场景级别的物体姿态优化合理性

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00027

Dominik Bauer, T. Patten, M. Vincze

引用次数: 6