ACM Multimedia Asia最新文献_第4页

Head-Motion-Aware Viewport Margins for Improving User Experience in Immersive Video 改善沉浸式视频用户体验的头部运动感知视口边距

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490573

Mehmet N. Akcay, Burak Kara, Saba Ahsan, A. Begen, I. Curcio, Emre B. Aksu

引用次数: 4

Learning to Decompose and Restore Low-light Images with Wavelet Transform 学习用小波变换分解和恢复弱光图像

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490622

Pengju Zhang, Chaofan Zhang, Zheng Rong, Yihong Wu

{"title":"Learning to Decompose and Restore Low-light Images with Wavelet Transform","authors":"Pengju Zhang, Chaofan Zhang, Zheng Rong, Yihong Wu","doi":"10.1145/3469877.3490622","DOIUrl":"https://doi.org/10.1145/3469877.3490622","url":null,"abstract":"Low-light images often suffer from low visibility and various noise. Most existing low-light image enhancement methods often amplify noise when enhancing low-light images, due to the neglect of separating valuable image information and noise. In this paper, we propose a novel wavelet-based attention network, where wavelet transform is integrated into attention learning for joint low-light enhancement and noise suppression. Particularly, the proposed wavelet-based attention network includes a Decomposition-Net, an Enhancement-Net and a Restoration-Net. In Decomposition-Net, to benefit denoising, wavelet transform layers are designed for separating noise and global content information into different frequency features. Furthermore, an attention-based strategy is introduced to progressively select suitable frequency features for accurately restoring illumination and reflectance according to Retinex theory. In addition, Enhancement-Net is introduced for further removing degradations in reflectance and adjusting illumination, while Restoration-Net employs conditional adversarial learning to adversarially improve the visual quality of final restored results based on enhanced illumination and reflectance. Extensive experiments on several public datasets demonstrate that the proposed method achieves more pleasing results than state-of-the-art methods.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"55 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090962","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: 1

Hard-Boundary Attention Network for Nuclei Instance Segmentation 核实例分割的硬边界关注网络

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490602

Yalu Cheng, Pengchong Qiao, Hong-Ju He, Guoli Song, Jie Chen

引用次数: 0

An Embarrassingly Simple Approach to Discrete Supervised Hashing 离散监督哈希的一种令人尴尬的简单方法

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3493595

Shuguang Zhao, Bingzhi Chen, Zheng Zhang, Guangming Lu

引用次数: 1

Language Based Image Quality Assessment 基于语言的图像质量评估

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490605

L. Galteri, Lorenzo Seidenari, P. Bongini, M. Bertini, A. Bimbo

引用次数: 4

Chinese White Dolphin Detection in the Wild 在野外发现中华白海豚

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490574

Hao Zhang, Qi Zhang, P. Nguyen, Victor C. S. Lee, Antoni B. Chan

{"title":"Chinese White Dolphin Detection in the Wild","authors":"Hao Zhang, Qi Zhang, P. Nguyen, Victor C. S. Lee, Antoni B. Chan","doi":"10.1145/3469877.3490574","DOIUrl":"https://doi.org/10.1145/3469877.3490574","url":null,"abstract":"For ecological protection of the ocean, biologists usually conduct line-transect vessel surveys to measure sea species’ population density within their habitat (such as dolphins). However, sea species observation via vessel surveys consumes a lot of manpower resources and is more challenging compared to observing common objects, due to the scarcity of the object in the wild, tiny-size of the objects, and similar-sized distracter objects (e.g., floating trash). To reduce the human experts’ workload and improve the observation accuracy, in this paper, we develop a practical system to detect Chinese White Dolphins in the wild automatically. First, we construct a dataset named Dolphin-14k with more than 2.6k dolphin instances. To improve the dataset annotation efficiency caused by the rarity of dolphins, we design an interactive dolphin box annotation strategy to annotate sparse dolphin instances in long videos efficiently. Second, we compare the performance and efficiency of three off-the-shelf object detection algorithms, including Faster-RCNN, FCOS, and YoloV5, on the Dolphin-14k dataset and pick YoloV5 as the detector, where a new category (Distracter) is added to the model training to reject the false positives. Finally, we incorporate the dolphin detector into a system prototype, which detects dolphins in video frames at 100.99 FPS per GPU with high accuracy (i.e., 90.95 mAP@0.5).","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126273047","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: 0

Deep Reinforcement Learning and Docking Simulations for autonomous molecule generation in de novo Drug Design 新药物设计中自主分子生成的深度强化学习和对接模拟

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497694

Hao Liu, Qian Wang, Xiaotong Hu

引用次数: 1

Intra- and Inter-frame Iterative Temporal Convolutional Networks for Video Stabilization 用于视频稳定的帧内和帧间迭代时间卷积网络

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490608

Haopeng Xie, Liang Xiao, Huicong Wu

{"title":"Intra- and Inter-frame Iterative Temporal Convolutional Networks for Video Stabilization","authors":"Haopeng Xie, Liang Xiao, Huicong Wu","doi":"10.1145/3469877.3490608","DOIUrl":"https://doi.org/10.1145/3469877.3490608","url":null,"abstract":"Video jitter is an uncomfortable product of irregular lens motion in time sequence. How to extract motion state information in a period of continuous video frames is a major issue for video stabilization. In this paper, we propose a novel sequence model, Intra- and Inter-frame Iterative Temporal Convolutional Networks (I3TC-Net), which alternatively transfer the spatial-temporal correlation of motion within and between frames. We hypothesize that the motion state information can be represented by transmission states. Specifically, we employ combination of Convolutional Long Short-Term Memory (ConvLSTM) and embedded encoder-decoder to generate the latent stable frame, which are used to update transmission states iteratively and learn a global homography transformation effectively for each unstable frame to generate the corresponding stabilized result along the time axis. Furthermore, we create a video dataset to solve the lack of stable data and improve the training effect. Experimental results show that our method outperforms state-of-the-art results on publicly available videos, such as 5.4 points improvements in stability score. The project page is available at https://github.com/root2022IIITC/IIITC.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"42 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130679449","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: 0

Differentially Private Learning with Grouped Gradient Clipping 分组梯度裁剪的差异私有学习

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3490594

Haolin Liu, Chenyu Li, Bochao Liu, Pengju Wang, Shiming Ge, Weiping Wang

引用次数: 10

Multi-Scale Graph Convolutional Network and Dynamic Iterative Class Loss for Ship Segmentation in Remote Sensing Images 基于多尺度图卷积网络和动态迭代类损失的遥感图像船舶分割

ACM Multimedia Asia Pub Date : 2021-12-01 DOI: 10.1145/3469877.3497699

Yanru Jiang, Chengyu Zheng, Zhao-Hui Wang, Rui Wang, Min Ye, Chenglong Wang, Ning Song, Jie Nie

{"title":"Multi-Scale Graph Convolutional Network and Dynamic Iterative Class Loss for Ship Segmentation in Remote Sensing Images","authors":"Yanru Jiang, Chengyu Zheng, Zhao-Hui Wang, Rui Wang, Min Ye, Chenglong Wang, Ning Song, Jie Nie","doi":"10.1145/3469877.3497699","DOIUrl":"https://doi.org/10.1145/3469877.3497699","url":null,"abstract":"The accuracy of the semantic segmentation results of ships is of great significance to coastline navigation, resource management, and territorial protection. Although the ship semantic segmentation method based on deep learning has made great progress, there is still the problem of not exploring the correlation between the targets. In order to avoid the above problems, this paper designed a multi-scale graph convolutional network and dynamic iterative class loss for ship segmentation in remote sensing images to generate more accurate segmentation results. Based on DeepLabv3+, our network uses deep convolutional networks and atrous convolutions for multi-scale feature extraction. In particular, for multi-scale semantic features, we propose to construct a Multi-Scale Graph Convolution Network (MSGCN) to introduce semantic correlation information for pixel feature learning by GCN, which enhances the segmentation result of ship objects. In addition, we propose a Dynamic Iterative Class Loss (DICL) based on iterative batch-wise class rectification instead of pre-computing the fixed weights over the whole dataset, which solves the problem of imbalance between positive and negative samples. We compared the proposed algorithm with the most advanced deep learning target detection methods and ship detection methods and proved the superiority of our method. On a High-Resolution SAR Images Dataset [1], ship detection and instance segmentation can be implemented well.","PeriodicalId":210974,"journal":{"name":"ACM Multimedia Asia","volume":"98 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113983351","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: 0