Test Whats new message.
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Showing 1-25 of 25
Sort By
EIC Message
Object Detection Consistency
Why Accuracy is Not Enough: The Need for Consistency in Object Detection
Caleb Tung;Abhinav Goel;Fischer Bordwell;Nick Eliopoulos;Xiao Hu;Yung-Hsiang Lu;George K. Thiruvathukal
Publication Year: 2022,Page(s):8 - 16
Cited by: Papers (2)
Object detectors are vital to many modern computer vision applications. However, even state-of-the-art object detectors are not perfect. On two images that look similar to human eyes, the same detector can make different predictions because of small image distortions like camera sensor noise and lighting changes. This problem is called inconsistency. Existing accuracy metrics do not properly accou...Show More
Cross-Modality
Deep Multigraph Hierarchical Enhanced Semantic Representation for Cross-Modal Retrieval
Lei Zhu;Chengyuan Zhang;Jiayu Song;Shichao Zhang;Chunwei Tian;Xinghui Zhu
Publication Year: 2022,Page(s):17 - 26
Cited by: Papers (8)
The main challenge of cross-modal retrieval is how to efficiently realize cross-modal semantic alignment and reduce the heterogeneity gap. However, existing approaches either ignore the multigrained semantic knowledge learning from different modalities, or fail to learn consistent relation distributions of semantic details in multimodal instances. To this end, this article proposes a novel end-to-...Show More
DIBR 3-D videos
DIBR Zero-Watermarking Based on Invariant Feature and Geometric Rectification
Xiyao Liu;Yayun Zhang;Sibo Du;Jian Zhang;Ming Jiang;Hui Fang
Publication Year: 2022,Page(s):27 - 37
Cited by: Papers (3)
Despite the success of watermarking technique for protecting depth image-based rendering (DIBR) 3-D videos, existing methods still can hardly ensure the robustness against geometric attacks, lossless video quality, and distinguishability between different videos simultaneously. In this article, we propose a novel zero-watermarking scheme to address this challenge. Specifically, we design CT-SVD fe...Show More
DIBR Zero-Watermarking Based on Invariant Feature and Geometric Rectification
Xiyao Liu;Yayun Zhang;Sibo Du;Jian Zhang;Ming Jiang;Hui Fang
Year: 2022 | Volume: 29 | Issue: 3
Image-Sentence Retrieval
Comprehensive Framework of Early and Late Fusion for Image–Sentence Retrieval
Publication Year: 2022,Page(s):38 - 47
Cited by: Papers (3)
Image–text retrieval is one challenging task to bridge the modality gap between vision and language. Although the mainstream late fusion schemes could facilitate intramodality correlations, it would result in heavy burden of computation resources and insufficient intermodal alignment. In this work, we propose comprehensive framework of early and late fusion (CFELF), a universal framework to collab...Show More
Comprehensive Framework of Early and Late Fusion for Image–Sentence Retrieval
Year: 2022 | Volume: 29 | Issue: 3
Multimedia Health Applications
Multimedia Monitoring System of Obstructive Sleep Apnea via a Deep Active Learning Model
Fei Teng;Dian Wang;Yue Yuan;Haibo Zhang;Amit Kumar Singh;Zhihan Lv
Publication Year: 2022,Page(s):48 - 56
Cited by: Papers (5)
Obstructive sleep apnea (OSA) is one of the most commonsleep-related breathing disorders. Nearly 1 billion people worldwide suffer from it, causing serious health effects and social burden. However, traditional monitoring systems often fall short in terms of cost and accessibility. In this article, we first propose a deep active learning model to detect OSA events from electrocardiogram (ECG). We ...Show More
Representation Learning
Adversarial Adaptive Interpolation in Autoencoders for Dually Regularizing Representation Learning
Guanyue Li;Xiwen Wei;Si Wu;Zhiwen Yu;Sheng Qian;Hau-San Wong
Publication Year: 2022,Page(s):57 - 68
Cited by: Papers (1)
Linear interpolation in the latent space may induce mismatch between the constructed data and the distribution a model was trained on. In this article, we propose an Adversarial Adaptive Interpolation-based AutoEncoder (AdvAI-AE). To constrain the interpolation path on the underlying manifold, an additional interpolation correction module is trained to offset the deviation between the linearly int...Show More
Zero-Shot Learning
Beyond Normal Distribution: More Factual Feature Generation Network for Generalized Zero-Shot Learning
Publication Year: 2022,Page(s):69 - 79
Cited by: Papers (3)
Due to the prosperous development of generative models, research works have achieved great success on the generalized zero-shot learning (GZSL) task. In most generative methods of GZSL, researchers try to utilize attributes and normally distributed noise to generate visual features, which ignores whether the normal distribution can perfectly represent all categories. Therefore, in this article, we...Show More
Image Denoising with Convolutional Neural Networks
A Robust Image Denoising Method With Multiview Texture-Aware Convolutional Neural Networks
Publication Year: 2022,Page(s):80 - 90
Cited by: Papers (3)
Image denoising is an issue of intensive research in the image processing community. As the wave of deep learning advances, image denoising with convolutional neural networks has recently made drastic progress, however, they usually can not recover atypical details from noisy images. Motivated by the above, we propose a multiview texture-aware convolutional neural network named MVTANet, which comp...Show More
A Robust Image Denoising Method With Multiview Texture-Aware Convolutional Neural Networks
Year: 2022 | Volume: 29 | Issue: 3
Graph Convolutional Network
Context- and Knowledge-Aware Graph Convolutional Network for Multimodal Emotion Recognition
Yahui Fu;Shogo Okada;Longbiao Wang;Lili Guo;Yaodong Song;Jiaxing Liu;Jianwu Dang
Publication Year: 2022,Page(s):91 - 100
Cited by: Papers (13)
This work proposes an approach for emotion recognition in conversation that leverages context modeling, knowledge enrichment, and multimodal (text and audio) learning based on a graph convolutional network (GCN). We first construct two distinctive graphs for modeling the contextual interaction and knowledge dynamic. We then introduce an affective lexicon into knowledge graph building to enrich the...Show More
Department: Industry and Standards
The MPEG Immersive Video Standard—Current Status and Future Outlook
Vinod Kumar Malamal Vadakital;Adrian Dziembowski;Gauthier Lafruit;Franck Thudor;Gwangsoon Lee;Patrice Rondao Alface
Publication Year: 2022,Page(s):101 - 111
Cited by: Papers (20)
The MPEG immersive video (MIV) standard is the latest addition to the MPEG-I suite of standards. It focuses on the representation and coding of immersive media. MIV is designed to support virtual and extended reality applications that require six degrees of freedom visual interaction with the rendered scene. Edition-1 of MIV is now in its final phase of standardization. Leveraging conventional 2-D...Show More
Contact Information
Contact Information
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.