2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)最新文献_第3页

Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation 协同网络:用于精细到精细医学图像分割的协同区域轮廓驱动网络

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00177

Anran Liu, Xiangsheng Huang, Tong Li, Pengcheng Ma

{"title":"Co-Net: A Collaborative Region-Contour-Driven Network for Fine-to-Finer Medical Image Segmentation","authors":"Anran Liu, Xiangsheng Huang, Tong Li, Pengcheng Ma","doi":"10.1109/WACV51458.2022.00177","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00177","url":null,"abstract":"In this paper, a fine-to-finer segmentation task is investigated driven by region and contour features collaboratively on Glomerular Electron-Dense Deposits (GEDD) in view of the complementary nature of these two types of features. To this end, a novel network (Co-Net) is presented to dynamically use fine saliency segmentation to guide finer segmentation on boundaries. The whole architecture contains double mutually boosted decoders sharing one common encoder. Specifically, a new structure named Global-guided Interaction Module (GIM) is designed to effectively control the information flow and reduce redundancy in the cross-level feature fusion process. At the same time, the global features are used in it to make the features of each layer gain access to richer context, and a fine segmentation map is obtained initially; Discontinuous Boundary Supervision (DBS) strategy is applied to pay more attention to discontinuity positions and modifying segmentation errors on boundaries. At last, Selective Kernel (SK) is used for dynamical aggregation of the region and contour features to obtain a finer segmentation. Our proposed approach is evaluated on an independent GEDD dataset labeled by pathologists and also on open polyp datasets to test the generalization. Ablation studies show the effectiveness of different modules. On all datasets, our proposal achieves high segmentation accuracy and surpasses previous methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"15 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":"123010863","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

Modeling Aleatoric Uncertainty for Camouflaged Object Detection 伪装目标检测的任意不确定性建模

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00267

Jiawei Liu, Jing Zhang, N. Barnes

{"title":"Modeling Aleatoric Uncertainty for Camouflaged Object Detection","authors":"Jiawei Liu, Jing Zhang, N. Barnes","doi":"10.1109/WACV51458.2022.00267","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00267","url":null,"abstract":"Aleatoric uncertainty captures noise within the observations. For camouflaged object detection, due to similar appearance of the camouflaged foreground and the back-ground, it’s difficult to obtain highly accurate annotations, especially annotations around object boundaries. We argue that training directly with the \"noisy\" camouflage map may lead to a model of poor generalization ability. In this paper, we introduce an explicitly aleatoric uncertainty estimation technique to represent predictive uncertainty due to noisy labeling. Specifically, we present a confidence-aware camouflaged object detection (COD) framework using dynamic supervision to produce both an accurate camouflage map and a reliable \"aleatoric uncertainty\". Different from existing techniques that produce deterministic prediction following the point estimation pipeline, our framework formalises aleatoric uncertainty as probability distribution over model output and the input image. We claim that, once trained, our confidence estimation network can evaluate the pixel-wise accuracy of the prediction without relying on the ground truth camouflage map. Extensive results illustrate the superior performance of the proposed model in explaining the camouflage prediction. Our codes are available at https://github.com/Carlisle-Liu/OCENet","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"60 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":"121240749","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: 22

PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations PICA:基于点向实例和质心对齐的少镜头域自适应目标检测

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00047

Chaoliang Zhong, Jiexi Wang, Chengang Feng, Ying Zhang, Jun Sun, Yasuto Yokota

{"title":"PICA: Point-wise Instance and Centroid Alignment Based Few-shot Domain Adaptive Object Detection with Loose Annotations","authors":"Chaoliang Zhong, Jiexi Wang, Chengang Feng, Ying Zhang, Jun Sun, Yasuto Yokota","doi":"10.1109/WACV51458.2022.00047","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00047","url":null,"abstract":"In this work, we focus on supervised domain adaptation for object detection in few-shot loose annotation setting, where the source images are sufficient and fully labeled but the target images are few-shot and loosely annotated. As annotated objects exist in the target domain, instance level alignment can be utilized to improve the performance. Traditional methods conduct the instance level alignment by semantically aligning the distributions of paired object features with domain adversarial training. Although it is demonstrated that point-wise surrogates of distribution alignment provide a more effective solution in few-shot classification tasks across domains, this point-wise alignment approach has not yet been extended to object detection. In this work, we propose a method that extends the point-wise alignment from classification to object detection. Moreover, in the few-shot loose annotation setting, the background ROIs of target domain suffer from severe label noise problem, which may make the point-wise alignment fail. To this end, we exploit moving average centroids to mitigate the label noise problem of background ROIs. Meanwhile, we exploit point-wise alignment over instances and centroids to tackle the problem of scarcity of labeled target instances. Hence this method is not only robust against label noises of background ROIs but also robust against the scarcity of labeled target objects. Experimental results show that the proposed instance level alignment method brings significant improvement compared with the baseline and is superior to state-of-the-art methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"25 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":"133433044","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

Channel Pruning via Lookahead Search Guided Reinforcement Learning 基于前瞻搜索引导强化学习的信道修剪

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00357

Z. Wang, Chengcheng Li

引用次数: 11

Learned Event-based Visual Perception for Improved Space Object Detection 基于学习事件的视觉感知改进空间目标检测

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00336

Nikolaus Salvatore, Justin Fletcher

{"title":"Learned Event-based Visual Perception for Improved Space Object Detection","authors":"Nikolaus Salvatore, Justin Fletcher","doi":"10.1109/WACV51458.2022.00336","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00336","url":null,"abstract":"The detection of dim artificial Earth satellites using ground-based electro-optical sensors, particularly in the presence of background light, is technologically challenging. This perceptual task is foundational to our understanding of the space environment, and grows in importance as the number, variety, and dynamism of space objects increases. We present a hybrid image- and event-based architecture that leverages dynamic vision sensing technology to detect resident space objects in geosynchronous Earth orbit. Given the asynchronous, one-dimensional image data supplied by a dynamic vision sensor, our architecture applies conventional image feature extractors to integrated, two-dimensional frames in conjunction with point-cloud feature extractors, such as PointNet, in order to increase detection performance for dim objects in scenes with high background activity. In addition, an end-to-end event-based imaging simulator is developed to both produce data for model training as well as approximate the optimal sensor parameters for event-based sensing in the context of electrooptical telescope imagery. Experimental results confirm that the inclusion of point-cloud feature extractors increases recall for dim objects in the high-background regime.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"33 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":"133694593","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

Inpaint2Learn: A Self-Supervised Framework for Affordance Learning Inpaint2Learn:一个功能学习的自我监督框架

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00383

Lingzhi Zhang, Weiyu Du, Shenghao Zhou, Jiancong Wang, Jianbo Shi

引用次数: 4

FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization 基于分解卷积的细粒度特征映射稀疏计算用于推理优化

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00369

Zirui Xu, Fuxun Yu, Chenxi Liu, Zhe Wu, Hongcheng Wang, Xiang Chen

{"title":"FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization","authors":"Zirui Xu, Fuxun Yu, Chenxi Liu, Zhe Wu, Hongcheng Wang, Xiang Chen","doi":"10.1109/WACV51458.2022.00369","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00369","url":null,"abstract":"Many works focus on the model’s static parameter optimization (e.g., filters and weights) for CNN inference acceleration. Compared to parameter sparsity, feature map sparsity is per-input related which has better adaptability. The practical sparsity patterns are non-structural and randomly located on feature maps with non-identical shapes. However, the existing feature map sparsity works take computing efficiency as the primary goal, thereby they can only remove structural sparsity and fail to match the above characteristics. In this paper, we develop a novel sparsity computing scheme called FalCon, which can well adapt to the practical sparsity patterns while still maintaining efficient computing. Specifically, we first propose a decomposed convolution design that enables a fine-grained computing unit for sparsity. Additionally, a decomposed convolution computing optimization paradigm is proposed to convert the sparse computing units to practical acceleration. Extensive experiments show that FalCon achieves at most 67.30% theoretical computation reduction with a neglected accuracy drop while accelerating CNN inference by 37%.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"354 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":"115928335","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

C-VTON: Context-Driven Image-Based Virtual Try-On Network C-VTON:上下文驱动的基于图像的虚拟试戴网络

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00226

Benjamin Fele, Ajda Lampe, P. Peer, Vitomir Štruc

{"title":"C-VTON: Context-Driven Image-Based Virtual Try-On Network","authors":"Benjamin Fele, Ajda Lampe, P. Peer, Vitomir Štruc","doi":"10.1109/WACV51458.2022.00226","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00226","url":null,"abstract":"Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing techniques are currently still limited in the quality of the try-on results they are able to produce from input images of diverse characteristics. In this work, we propose a Context-Driven Virtual Try-On Network (C-VTON) that addresses these limitations and convincingly transfers selected clothing items to the target subjects even under challenging pose configurations and in the presence of self-occlusions. At the core of the C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns the target clothing with the pose of the person in the input images, and (ii) a powerful image generator that utilizes various types of contextual information when synthesizing the final try-on result. C-VTON is evaluated in rigorous experiments on the VITON and MPV datasets and in comparison to state-of-the-art techniques from the literature. Experimental results show that the proposed approach is able to produce photo-realistic and visually convincing results and significantly improves on the existing state-of-the-art.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"54 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":"122555908","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: 23

3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language 3DRefTransformer:在现实世界场景中使用自然语言的细粒度对象识别

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00068

Ahmed Abdelreheem, Ujjwal Upadhyay, Ivan Skorokhodov, Rawan Al Yahya, Jun Chen, Mohamed Elhoseiny

{"title":"3DRefTransformer: Fine-Grained Object Identification in Real-World Scenes Using Natural Language","authors":"Ahmed Abdelreheem, Ujjwal Upadhyay, Ivan Skorokhodov, Rawan Al Yahya, Jun Chen, Mohamed Elhoseiny","doi":"10.1109/WACV51458.2022.00068","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00068","url":null,"abstract":"In this paper, we study fine-grained 3D object identification in real-world scenes described by a textual query. The task aims to discriminatively understand an instance of a particular 3D object described by natural language utterances among other instances of 3D objects of the same class appearing in a visual scene. We introduce the 3DRefTransformer net, a transformer-based neural network that identifies 3D objects described by linguistic utterances in real-world scenes. The network’s input is 3D object segmented point cloud images representing a real-world scene and a language utterance that refers to one of the scene objects. The goal is to identify the referred object. Compared to the state-of-the-art models that are mostly based on graph convolutions and LSTMs, our 3DRefTrans-former net offers two key advantages. First, it is an end-to-end transformer model that operates both on language and 3D visual objects. Second, it has a natural ability to ground textual terms in the utterance to the learning representation of 3D objects in the scene. We further incorporate object pairwise spatial relation loss and contrastive learning during model training. We show in our experiments that our model improves the performance upon the current SOTA significantly on Referit3D Nr3D and Sr3D datasets. Code and Models will be made publicly available at https://vision-cair.github.io/3dreftransformer/.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"9 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":"125647411","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: 12

Leveraging Test-Time Consensus Prediction for Robustness against Unseen Noise 利用测试时间一致性预测对不可见噪声的鲁棒性

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00362

Anindya Sarkar, Anirban Sarkar, V. Balasubramanian

引用次数: 3