IEEE Transactions on Multimedia

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IEEE Transactions on Multimedia

The scope of the Periodical is the various aspects of research in multimedia technology and applications of multimedia, including, but not limited to, circuits, networking, signal processing, systems, software, and systems integration, as represented by the Fields of Interest of the sponsors.

Reproducible research

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication's figures and tables on a website; see the supplemental materials section of the information for authors. It gives other researchers easier access to the work, and facilitates fair comparisons.

Multimedia content

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Center.

     

 

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TMM Volume 26 | 2024

End-to-End Instance-Level Human Parsing by Segmenting Persons

TMM Volume 26 | 2024
TMM Articles

Instance-level human parsing is aimed at separately partitioning the human body into different semantic parts for each individual, which remains a challenging task due to human appearance/pose variation, occlusion and complex backgrounds. Most state-of-the-art methods follow the “parsing-by-detection” paradigm, which relies on a trained detector to localize persons and then sequentially performs single-person parsing for each person. However, this paradigm is closely related to the detector, and the runtime is proportional to the number of persons in an image.

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Orientation-Aware Pedestrian Attribute Recognition Based on Graph Convolution Network

TMM Volume 26 | 2024
TMM Articles

Pedestrian attribute recognition (PAR) aims to generate a structured description of pedestrians and plays an important role in surveillance. Current work focusing on 2D images can achieve decent performance when there is no variation in the captured pedestrian orientation. However, the performance of these works cannot be maintained in scenarios when the orientation of pedestrians is ignored. 

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ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries

TMM Volume 26 | 2024
TMM Articles

Zero-shot learning (ZSL) has received extensive attention recently especially in areas of fine-grained object recognition, retrieval, and image captioning. Due to the complete lack of training samples and high requirement of defense transferability, the ZSL model learned is particularly vulnerable against adversarial attacks. Recent work also showed adversarially robust generalization requires more data.

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Federated Adversarial Domain Hallucination for Privacy-Preserving Domain Generalization

TMM Volume 26 | 2024
TMM Articles

Domain generalization aims to reduce the vulnerability of deep neural networks in the out-of-domain distribution scenario. With the recent and increasing data privacy concerns, federated domain generalization, where multiple domains are distributed on different local clients, has become an important research problem and brings new challenges for learning domain-invariant information from separated domains. 

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