Comput. Vis. Image Underst.最新文献_第5页

Progressive Scene Text Erasing with Self-Supervision 具有自我监督的渐进场景文本擦除

Comput. Vis. Image Underst. Pub Date : 2022-07-23 DOI: 10.48550/arXiv.2207.11469

Xiangcheng Du, Zhao Zhou, Yingbin Zheng, Xingjiao Wu, Tianlong Ma, Cheng Jin

引用次数: 2

An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation 基于少镜头骨架的时间动作分割的有效框架

Comput. Vis. Image Underst. Pub Date : 2022-07-20 DOI: 10.48550/arXiv.2207.09925

Leiyang Xu, Qianqian Wang, Xiaotian Lin, Lin Yuan

{"title":"An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation","authors":"Leiyang Xu, Qianqian Wang, Xiaotian Lin, Lin Yuan","doi":"10.48550/arXiv.2207.09925","DOIUrl":"https://doi.org/10.48550/arXiv.2207.09925","url":null,"abstract":"Temporal action segmentation (TAS) aims to classify and locate actions in the long untrimmed action sequence. With the success of deep learning, many deep models for action segmentation have emerged. However, few-shot TAS is still a challenging problem. This study proposes an efficient framework for the few-shot skeleton-based TAS, including a data augmentation method and an improved model. The data augmentation approach based on motion interpolation is presented here to solve the problem of insufficient data, and can increase the number of samples significantly by synthesizing action sequences. Besides, we concatenate a Connectionist Temporal Classification (CTC) layer with a network designed for skeleton-based TAS to obtain an optimized model. Leveraging CTC can enhance the temporal alignment between prediction and ground truth and further improve the segment-wise metrics of segmentation results. Extensive experiments on both public and self-constructed datasets, including two small-scale datasets and one large-scale dataset, show the effectiveness of two proposed methods in improving the performance of the few-shot skeleton-based TAS task.","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90475265","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

SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection 重新审视视频异常检测的自监督多任务学习

Comput. Vis. Image Underst. Pub Date : 2022-07-16 DOI: 10.48550/arXiv.2207.08003

Antonio Bărbălău, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, J. Dueholm, B. Ramachandra, Kamal Nasrollahi, F. Khan, T. Moeslund, M. Shah

{"title":"SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection","authors":"Antonio Bărbălău, Radu Tudor Ionescu, Mariana-Iuliana Georgescu, J. Dueholm, B. Ramachandra, Kamal Nasrollahi, F. Khan, T. Moeslund, M. Shah","doi":"10.48550/arXiv.2207.08003","DOIUrl":"https://doi.org/10.48550/arXiv.2207.08003","url":null,"abstract":"A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-supervised multi-task learning framework, proposing several updates to the original method. First, we study various detection methods, e.g. based on detecting high-motion regions using optical flow or background subtraction, since we believe the currently used pre-trained YOLOv3 is suboptimal, e.g. objects in motion or objects from unknown classes are never detected. Second, we modernize the 3D convolutional backbone by introducing multi-head self-attention modules, inspired by the recent success of vision transformers. As such, we alternatively introduce both 2D and 3D convolutional vision transformer (CvT) blocks. Third, in our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps through knowledge distillation, solving jigsaw puzzles, estimating body pose through knowledge distillation, predicting masked regions (inpainting), and adversarial learning with pseudo-anomalies. We conduct experiments to assess the performance impact of the introduced changes. Upon finding more promising configurations of the framework, dubbed SSMTL++v1 and SSMTL++v2, we extend our preliminary experiments to more data sets, demonstrating that our performance gains are consistent across all data sets. In most cases, our results on Avenue, ShanghaiTech and UBnormal raise the state-of-the-art performance bar to a new level.","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79033053","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: 19

SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification SiaTrans:基于深度图像分类的RGB-D显著目标检测Siamese变压器网络

Comput. Vis. Image Underst. Pub Date : 2022-07-09 DOI: 10.48550/arXiv.2207.04224

Xin Jia, Changlei Dongye, Yan-Tsung Peng

{"title":"SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification","authors":"Xin Jia, Changlei Dongye, Yan-Tsung Peng","doi":"10.48550/arXiv.2207.04224","DOIUrl":"https://doi.org/10.48550/arXiv.2207.04224","url":null,"abstract":"RGB-D SOD uses depth information to handle challenging scenes and obtain high-quality saliency maps. Existing state-of-the-art RGB-D saliency detection methods overwhelmingly rely on the strategy of directly fusing depth information. Although these methods improve the accuracy of saliency prediction through various cross-modality fusion strategies, misinformation provided by some poor-quality depth images can affect the saliency prediction result. To address this issue, a novel RGB-D salient object detection model (SiaTrans) is proposed in this paper, which allows training on depth image quality classification at the same time as training on SOD. In light of the common information between RGB and depth images on salient objects, SiaTrans uses a Siamese transformer network with shared weight parameters as the encoder and extracts RGB and depth features concatenated on the batch dimension, saving space resources without compromising performance. SiaTrans uses the Class token in the backbone network (T2T-ViT) to classify the quality of depth images without preventing the token sequence from going on with the saliency detection task. Transformer-based cross-modality fusion module (CMF) can effectively fuse RGB and depth information. And in the testing process, CMF can choose to fuse cross-modality information or enhance RGB information according to the quality classification signal of the depth image. The greatest benefit of our designed CMF and decoder is that they maintain the consistency of RGB and RGB-D information decoding: SiaTrans decodes RGB-D or RGB information under the same model parameters according to the classification signal during testing. Comprehensive experiments on nine RGB-D SOD benchmark datasets show that SiaTrans has the best overall performance and the least computation compared with recent state-of-the-art methods.","PeriodicalId":10549,"journal":{"name":"Comput. Vis. Image Underst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76098488","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

PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning PoseGU:基于新型人体姿态生成器和无偏学习的三维人体姿态估计

Comput. Vis. Image Underst. Pub Date : 2022-07-07 DOI: 10.48550/arXiv.2207.03618

S. Guan, Haiyan Lu, Linchao Zhu, Gengfa Fang

引用次数: 2

Rich global feature guided network for monocular depth estimation 单目深度估计的富全局特征引导网络

Comput. Vis. Image Underst. Pub Date : 2022-07-01 DOI: 10.2139/ssrn.4057946

Bingyuan Wu, Yongxiong Wang

引用次数: 4

CERVI: collaborative editing of raster and vector images CERVI:协同编辑光栅和矢量图像

Comput. Vis. Image Underst. Pub Date : 2022-06-23 DOI: 10.1007/s00371-022-02522-1

Ulrike Bath, Sumit Shekhar, Julian Egbert, Julian Schmidt, Amir Semmo, J. Döllner, Matthias Trapp

引用次数: 1

Neural network adaption for depth sensor replication 深度传感器复制的神经网络自适应

Comput. Vis. Image Underst. Pub Date : 2022-06-23 DOI: 10.1007/s00371-022-02531-0

Christian Kunert, Tobias Schwandt, Christon-Ragavan Nadar, W. Broll

引用次数: 1

Physically-admissible polarimetric data augmentation for road-scene analysis 物理上允许的偏振数据增强道路场景分析

Comput. Vis. Image Underst. Pub Date : 2022-06-01 DOI: 10.48550/arXiv.2206.07431

Cyprien Ruffino, Rachel Blin, Samia Ainouz, G. Gasso, Romain H'erault, F. Mériaudeau, S. Canu

引用次数: 0

Accurate and efficient salient object detection via position prior attention 通过位置优先注意准确有效地检测显著目标

Comput. Vis. Image Underst. Pub Date : 2022-06-01 DOI: 10.2139/ssrn.4081836

Jin Zhang, Qiuwei Liang, Yanjiao Shi

引用次数: 5