First IEEE Workshop on Coding for Machines
July 10, 2023, Brisbane, Australia
in conjunction with IEEE ICME 2023
Call for Papers
Multimedia signals – speech, audio, images, video, point clouds, light fields, … – have traditionally been acquired, processed, and compressed for human use. However, it is estimated that in the near future, the majority of Internet connections will be machine-to-machine (M2M). So, increasingly, the data communicated across networks is primarily intended for automated machine analysis. Applications include remote monitoring, surveillance, and diagnostics, autonomous driving and navigation, smart homes / buildings / neighborhoods / cities, and so on. This necessitates rethinking of traditional compression and pre-/post-processing methods to facilitate efficient machine-based analysis of multimedia signals. As a result, standardization efforts such as MPEG VCM (Video Coding for Machines) and JPEG AI have been launched.
Both the theory and early design examples have shown that significant bit savings for a given inference accuracy are possible compared to traditional human-oriented coding approaches. However, a number of open issues remain. These include a thorough understanding of the tradeoffs involved in coding for machines, coding for multiple machine tasks, as well as combined human-machine use, model architectures, software and hardware optimization, error resilience, privacy, security, and others. The proposed workshop is intended to bring together researchers from academia, industry, and government who are working on related problems, provide a snapshot of the current research and standardization efforts in the area, and generate ideas for future work. We welcome papers on the following and related topics:
Theories and frameworks for coding for machines
Standards related to coding for machines
Methods for feature compression
End-to-end approaches for coding for machines
Compression for human-and-machine use
Compressed-domain multimedia analysis (understanding, translation, classification, object detection, segmentation, pose estimation, etc.)
Compressed-domain multimedia processing (denoising, super-resolution, enhancement, …)
Datasets for coding for machines
Error resilience in coding for machines
Privacy and security in coding for machines
Important dates
Paper submission: 30-Mar-23
https://cmt3.research.microsoft.com/ICMEW2023
(See submission instructions below)
Acceptance notification: 24-Apr-23
Camera-ready papers: 1-May-23
Submission instructions
ICME and its workshops are in-person events. Please read the Presentation guarantee section at https://www.2023.ieeeicme.org/author-info.php
Click on "Create new submission..."
Choose "CfM - Coding for Machines"
Follow the remaining instructions
Organizers
Ying Liu, Santa Clara University, USA
Heming Sun, Waseda University, Japan
Hyomin Choi, InterDigital, USA
Fengqing Maggie Zhu, Purdue University, USA
Jiangtao Wen, Tsinghua University, China
Ivan V. Bajić, Simon Fraser University, Canada
Technical Program Committee
Balu Adsumilli, Google/YouTube, USA
Nilesh Ahuja, Intel Labs, USA
João Ascenso, Instituto Superior Técnico, Portugal
Zhihao Duan, Purdue University, USA
Yuxing (Erica) Han, Tsinghua University, China
Wei Jiang, Futurewei, USA
Hari Kalva, Florida Atlantic University, USA
André Kaup, Friedrich-Alexander University Erlangen-Nuremberg, Germany
Xiang Li, Google, USA
Weisi Lin, Nanyang Technological University, Singapore
Jiaying Liu, Peking University, China
Ambarish Natu, Australian Government
Saeed Ranjbar Alvar, Huawei, Canada
Donggyu Sim, Kwangwoon University, Korea
Shiqi Wang, City University of Hong Kong
Li Zhang, ByteDance, USA