2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)最新文献_第4页

Non-local Attention Improves Description Generation for Retinal Images 非局部注意改进视网膜图像描述生成

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00331

Jia-Hong Huang, Ting-Wei Wu, C. Yang, Zenglin Shi, I-Hung Lin, J. Tegnér, M. Worring

{"title":"Non-local Attention Improves Description Generation for Retinal Images","authors":"Jia-Hong Huang, Ting-Wei Wu, C. Yang, Zenglin Shi, I-Hung Lin, J. Tegnér, M. Worring","doi":"10.1109/WACV51458.2022.00331","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00331","url":null,"abstract":"Automatically generating medical reports from retinal images is a difficult task in which an algorithm must generate semantically coherent descriptions for a given retinal image. Existing methods mainly rely on the input image to generate descriptions. However, many abstract medical concepts or descriptions cannot be generated based on image information only. In this work, we integrate additional information to help solve this task; we observe that early in the diagnosis process, ophthalmologists have usually written down a small set of keywords denoting important information. These keywords are then subsequently used to aid the later creation of medical reports for a patient. Since these keywords commonly exist and are useful for generating medical reports, we incorporate them into automatic report generation. Since we have two types of inputs expert-defined unordered keywords and images - effectively fusing features from these different modalities is challenging. To that end, we propose a new keyword-driven medical report generation method based on a non-local attention-based multi-modal feature fusion approach, TransFuser, which is capable of fusing features from different types of inputs based on such attention. Our experiments show the proposed method successfully captures the mutual information of keywords and image content. We further show our proposed keyword-driven generation model reinforced by the TransFuser is superior to baselines under the popular text evaluation metrics BLEU, CIDEr, and ROUGE. Trans-Fuser Github: https://github.com/Jhhuangkay/Non-local-Attention-ImprovesDescription-Generation-for-Retinal-Images.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"37 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":"125114678","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

High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensation 具有饱和补偿但无显式运动补偿的动态场景的高动态范围成像

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00014

Haesoo Chung, N. Cho

{"title":"High Dynamic Range Imaging of Dynamic Scenes with Saturation Compensation but without Explicit Motion Compensation","authors":"Haesoo Chung, N. Cho","doi":"10.1109/WACV51458.2022.00014","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00014","url":null,"abstract":"High dynamic range (HDR) imaging is a highly challenging task since a large amount of information is lost due to the limitations of camera sensors. For HDR imaging, some methods capture multiple low dynamic range (LDR) images with altering exposures to aggregate more information. However, these approaches introduce ghosting artifacts when significant inter-frame motions are present. Moreover, although multi-exposure images are given, we have little information in severely over-exposed areas. Most existing methods focus on motion compensation, i.e., alignment of multiple LDR shots to reduce the ghosting artifacts, but they still produce unsatisfying results. These methods also rather overlook the need to restore the saturated areas. In this paper, we generate well-aligned multi-exposure features by reformulating a motion alignment problem into a simple brightness adjustment problem. In addition, we propose a coarse-to-fine merging strategy with explicit saturation compensation. The saturated areas are reconstructed with similar well-exposed content using adaptive contextual attention. We demonstrate that our method outperforms the state-of-the-art methods regarding qualitative and quantitative evaluations.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"300 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":"131897867","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

Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation 基于多尺度斑块表示学习的图像异常检测与分割

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00312

Chin-Chia Tsai, Tsung-Hsuan Wu, S. Lai

引用次数: 33

Transferable 3D Adversarial Textures using End-to-end Optimization 使用端到端优化可转移的3D对抗纹理

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00080

Camilo Pestana, Naveed Akhtar, N. Rahnavard, M. Shah, A. Mian

引用次数: 1

Novel Ensemble Diversification Methods for Open-Set Scenarios 开集场景的集成多样化新方法

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00342

Miriam Farber, Roman Goldenberg, G. Leifman, Gal Novich

引用次数: 2

FLUID: Few-Shot Self-Supervised Image Deraining 流体:少镜头自监督图像脱轨

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00049

Shyam Nandan Rai, Rohit Saluja, Chetan Arora, V. Balasubramanian, A. Subramanian, C.V. Jawahar

{"title":"FLUID: Few-Shot Self-Supervised Image Deraining","authors":"Shyam Nandan Rai, Rohit Saluja, Chetan Arora, V. Balasubramanian, A. Subramanian, C.V. Jawahar","doi":"10.1109/WACV51458.2022.00049","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00049","url":null,"abstract":"Self-supervised methods have shown promising results in denoising and dehazing tasks, where the collection of the paired dataset is challenging and expensive. However, we find that these methods fail to remove the rain streaks when applied for image deraining tasks. The method’s poor performance is due to the explicit assumptions: (i) the distribution of noise or haze is uniform and (ii) the value of a noisy or hazy pixel is independent of its neighbors. The rainy pixels are non-uniformly distributed, and it is not necessarily dependant on its neighboring pixels. Hence, we conclude that the self-supervised method needs to have some prior knowledge about rain distribution to perform the deraining task. To provide this knowledge, we hypothesize a network trained with minimal supervision to estimate the likelihood of rainy pixels. This leads us to our proposed method called FLUID: Few Shot Sel f-Supervised Image Deraining.We perform extensive experiments and comparisons with existing image deraining and few-shot image-to-image translation methods on Rain 100L and DDN-SIRR datasets containing real and synthetic rainy images. In addition, we use the Rainy Cityscapes dataset to show that our method trained in a few-shot setting can improve semantic segmentation and object detection in rainy conditions. Our approach obtains a mIoU gain of 51.20 over the current best-performing deraining method. [Project Page]","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"42 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":"128852726","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

Less Can Be More: Sound Source Localization With a Classification Model 少即是多:声源定位与分类模型

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00065

Arda Senocak, H. Ryu, Junsik Kim, In-So Kweon

引用次数: 14

Few-Shot Open-Set Recognition of Hyperspectral Images with Outlier Calibration Network 基于离群点标定网络的高光谱图像少镜头开集识别

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00215

Debabrata Pal, Valay Bundele, Renuka Sharma, Biplab Banerjee, Y. Jeppu

{"title":"Few-Shot Open-Set Recognition of Hyperspectral Images with Outlier Calibration Network","authors":"Debabrata Pal, Valay Bundele, Renuka Sharma, Biplab Banerjee, Y. Jeppu","doi":"10.1109/WACV51458.2022.00215","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00215","url":null,"abstract":"We tackle the few-shot open-set recognition (FSOSR) problem in the context of remote sensing hyperspectral image (HSI) classification. Prior research on OSR mainly considers an empirical threshold on the class prediction scores to reject the outlier samples. Further, recent endeavors in few-shot HSI classification fail to recognize outliers due to the ‘closed-set’ nature of the problem and the fact that the entire class distributions are unknown during training. To this end, we propose to optimize a novel outlier calibration network (OCN) together with a feature extraction module during the meta-training phase. The feature extractor is equipped with a novel residual 3D convolutional block attention network (R3CBAM) for enhanced spectral-spatial feature learning from HSI. Our method rejects the outliers based on OCN prediction scores barring the need for manual thresholding. Finally, we propose to augment the query set with synthesized support set features during the similarity learning stage in order to combat the data scarcity issue of few-shot learning. The superiority of the proposed model is showcased on four benchmark HSI datasets. 1","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"559 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":"114316510","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: 7

Unsupervised Sounding Object Localization with Bottom-Up and Top-Down Attention 基于自底向上和自顶向下注意的无监督探测目标定位

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00222

Jiaying Shi, Chao Ma

引用次数: 8

Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift 学习生成未知数作为开集域移位的补救措施

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00379

Mahsa Baktash, Tianle Chen, M. Salzmann

引用次数: 4