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ACM Transactions on Multimedia Computing, Communications, and Applications is a peer-reviewed journal published by ACM, covering Advanced Image and Video Retrieval Techniques, Multimodal Machine Learning Applications, Human Pose and Action Recognition. Over its lifetime it has published 2,555 papers, which have received 47,669 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Multimedia Computing, Communications, and Applications 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Multimedia Computing, Communications, and Applications style. No sign-up. |
| Publishes research in | Advanced Image and Video Retrieval Techniques Multimodal Machine Learning Applications Human Pose and Action Recognition Image and Video Quality Assessment Video Analysis and Summarization |
| ISSN | 1551-6857 |
| Citation impact (2-yr) | 4.2 |
| h-index | 86 |
| i10-index | 1,092 |
| Top institutions publishing here | University of Science and Technology of China |
| Journal website | tomm.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms,…
In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network…
The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information,…
The superiority of deeply learned pedestrian representations has been reported in very recent literature of person re-identification (re-ID). In this article, we consider the more pragmatic issue of learning a deep feature with no or only a few labels. We propose a progressive unsupervised learning (PUL) method to transfer pretrained deep representations to unseen domains.…
Hundreds of millions of people play computer games every day. For them, game content—from 3D objects to abstract puzzles—plays a major entertainment role. Manual labor has so far ensured that the quality and quantity of game content matched the demands of the playing community, but is facing new scalability challenges due to the exponential growth…
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