2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)最新文献_第5页

GraDual: Graph-based Dual-modal Representation for Image-Text Matching 渐进式:基于图的图像-文本匹配双模态表示

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00252

Siqu Long, S. Han, Xiaojun Wan, Josiah Poon

{"title":"GraDual: Graph-based Dual-modal Representation for Image-Text Matching","authors":"Siqu Long, S. Han, Xiaojun Wan, Josiah Poon","doi":"10.1109/WACV51458.2022.00252","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00252","url":null,"abstract":"Image-text retrieval task is a challenging task. It aims to measure the visual-semantic correspondence between an image and a text caption. This is tough mainly because the image lacks semantic context information as in its corresponding text caption, and the text representation is very limited to fully describe the details of an image. In this paper, we introduce Graph-based Dual-modal Representations (GraDual), including Vision-Integrated Text Embedding (VITE) and Context-Integrated Visual Embedding (CIVE), for image-text retrieval. The GraDual improves the coverage of each modality by exploiting textual context semantics for the image representation, and using visual features as a guidance for the text representation. To be specific, we design: 1) a dual-modal graph representation mechanism to solve the lack of coverage issue for each modality. 2) an intermediate graph embedding integration strategy to enhance the important pattern across other modality global features. 3) a dual-modal driven crossmodal matching network to generate a filtered representation of another modality. Extensive experiments on two benchmark datasets, MS-COCO and Flickr30K, demonstrates the superiority of the proposed GraDual in comparison to state-of-the-art methods.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"52 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":"128209193","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: 21

Consistent Cell Tracking in Multi-frames with Spatio-Temporal Context by Object-Level Warping Loss 基于对象级翘曲损失的多帧时空背景下的一致细胞跟踪

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00182

Junya Hayashida, Kazuya Nishimura, Ryoma Bise

引用次数: 3

RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation 时序场景生成的深度强化学习算法

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00278

Azimkhon Ostonov, Peter Wonka, D. Michels

引用次数: 3

Registration of Human Point Set using Automatic Key Point Detection and Region-aware Features 基于自动关键点检测和区域感知特征的人体点集配准

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00232

A. Maharjan, Xiaohui Yuan

{"title":"Registration of Human Point Set using Automatic Key Point Detection and Region-aware Features","authors":"A. Maharjan, Xiaohui Yuan","doi":"10.1109/WACV51458.2022.00232","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00232","url":null,"abstract":"Non-rigid point set registration is challenging when point sets have large deformations and different numbers of points. Examples of such point sets include human point sets representing complex human poses captured by different types of depth cameras. In this work, we present a probabilistic, non-rigid registration method to deal with these issues. Two regularization terms are used: key point correspondences and local neighborhood preservation. Our method detects key points in the point sets based on geodesic distance. Correspondences are established using a new cluster-based, region-aware feature descriptor. This feature descriptor encodes the association of a cluster to the left-right (symmetry) or upper-lower regions of the point sets. We use the Stochastic Neighbor Embedding (SNE) constraint to preserve the local neighborhood of the point set. Experimental results on challenging 3D human poses demonstrate that our method outperforms the state-of-the-art methods. Our method achieved highly competitive performance with a slight increase of error by 3.9% in comparison with the method using manually specified key point correspondences.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"11 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120816034","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

Self-Supervised Shape Alignment for Sports Field Registration 运动场地登记的自监督形状对准

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00382

F. Shi, P. Marchwica, J. A. G. Higuera, Michael Jamieson, Mehrsan Javan, P. Siva

引用次数: 8

Re-Compose the Image by Evaluating the Crop on More Than Just a Score 通过评估不仅仅是一个分数的作物来重新构图图像

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00056

Yang Cheng, Qian Lin, J. Allebach

{"title":"Re-Compose the Image by Evaluating the Crop on More Than Just a Score","authors":"Yang Cheng, Qian Lin, J. Allebach","doi":"10.1109/WACV51458.2022.00056","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00056","url":null,"abstract":"Image re-composition has always been regarded as one of the most important steps during the post-processing of a photo. The quality of an image re-composition mainly depends on a person’s taste in aesthetics, which is not an effortless task for those who have no abundant experience in photography. Besides, while re-composing one image does not require much of a person’s time, it could be quite time-consuming when there are hundreds of images to be recomposed. To solve these problems, we propose a method that automates the process of re-composing an image to the desired aspect ratio. Although there already exist many image re-composition methods, they only provide a score to their predicted best crop but fail to explain why the score is high or low. Conversely, we succeed in designing an explainable method by introducing a novel 10-layer aesthetic score map, which represents how the position of the saliency in the original uncropped image, relative to that of the crop region, contributes to the overall score of the crop, so that the crop is not just represented by a single score. We conducted experiments to show that the proposed score map boosts the performance of our algorithm, which achieves a state-of-the-art performance on both public and our own datasets.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"96 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":"132002263","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: 2

Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation 基于负集成学习的无源无监督域自适应噪声标签清除

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00043

Waqar Ahmed, Pietro Morerio, Vittorio Murino

{"title":"Cleaning Noisy Labels by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptation","authors":"Waqar Ahmed, Pietro Morerio, Vittorio Murino","doi":"10.1109/WACV51458.2022.00043","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00043","url":null,"abstract":"Conventional Unsupervised Domain Adaptation (UDA) methods presume source and target domain data to be simultaneously available during training. Such an assumption may not hold in practice, as source data is often inaccessible (e.g., due to privacy reasons). On the contrary, a pre-trained source model is usually available, which performs poorly on target due to the well-known domain shift problem. This translates into a significant amount of misclassifications, which can be interpreted as structured noise affecting the inferred target pseudo-labels. In this work, we cast UDA as a pseudo-label refinery problem in the challenging source-free scenario. We propose Negative Ensemble Learning (NEL) technique, a unified method for adaptive noise filtering and progressive pseudo-label refinement. NEL is devised to tackle noisy pseudo-labels by enhancing diversity in ensemble members with different stochastic (i) input augmentation and (ii) feedback. The latter is achieved by leveraging the novel concept of Disjoint Residual Labels, which allow propagating diverse information to the different members. Eventually, a single model is trained with the refined pseudo-labels, which leads to a robust performance on the target domain. Extensive experiments show that the proposed method achieves state-of-the-art performance on major UDA benchmarks, such as Digit5, PACS, Visda-C, and DomainNet, without using source data samples at all.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"83 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":"132793342","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: 9

AirCamRTM: Enhancing Vehicle Detection for Efficient Aerial Camera-based Road Traffic Monitoring AirCamRTM:增强基于航空摄像机的高效道路交通监控的车辆检测

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00349

R. Makrigiorgis, Nicolas Hadjittoouli, C. Kyrkou, T. Theocharides

引用次数: 3

QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog 限定语:问题引导的自关注多模态融合网络，用于视听场景感知对话

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00256

Muchao Ye, Quanzeng You, Fenglong Ma

{"title":"QUALIFIER: Question-Guided Self-Attentive Multimodal Fusion Network for Audio Visual Scene-Aware Dialog","authors":"Muchao Ye, Quanzeng You, Fenglong Ma","doi":"10.1109/WACV51458.2022.00256","DOIUrl":"https://doi.org/10.1109/WACV51458.2022.00256","url":null,"abstract":"Audio video scene-aware dialog (AVSD) is a new but more challenging visual question answering (VQA) task because of the higher complexity of feature extraction and fusion brought by the additional modalities. Although recent methods have achieved early success in improving feature extraction technique for AVSD, the technique of feature fusion still needs further investigation. In this paper, inspired by the success of self-attention mechanism and the importance of understanding questions for VQA answering, we propose a question-guided self-attentive multi-modal fusion network (QUALIFIER) to improve the AVSD practice in the stage of feature fusion and answer generation. Specifically, after extracting features and learning a comprehensive feature for each modality, we first use the designed self-attentive multi-modal fusion (SMF) module to aggregate each feature with the correlated information learned from others. Later, by prioritizing the question feature, we concatenate it with each fused feature to guide the generation of a natural language response to the question. As for experimental results, QUALIFIER shows better performance than other baseline methods in the large-scale AVSD dataset named DSTC7. Additionally, the human evaluation and ablation study results also demonstrate the effectiveness of our network architecture.","PeriodicalId":297092,"journal":{"name":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","volume":"12 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":"114205545","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

Fast Nonlinear Image Unblending 快速非线性图像解混

2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Pub Date : 2022-01-01 DOI: 10.1109/WACV51458.2022.00325

Daichi Horita, K. Aizawa, Ryohei Suzuki, Taizan Yonetsuji, Huachun Zhu

引用次数: 2