First IEEE Workshop on Coding for Machines
July 10, 2023, Brisbane, Australia
in conjunction with IEEE ICME 2023
Technical Program Highlights
Keynote lecture: Prof. Yao Wang, New York University
Tutorial on CompressAI and CompressAI-vision
Technical sessions
Detailed program coming soon...
Keynote lecture
Learnt compression for visual analytics on the edge
This talk will discuss how to compress images to optimize for visual analytics tasks such as object detection and image classification, and its application for edge-assisted visual computing. We will contrast two different approaches: image compression at the mobile followed by decompression and visual analytics at the server, vs. splitting the deep learning computing between the mobile and the server and compressing the intermediate analytics features. We illustrate effective approaches for compressing analytics features and end-to-end training to optimize the rate-analytics trade-off. We demonstrate that split computing not only yields superior rate-analytics performance but also substantially reduces the mobile computing time. We further describe how to generate and code the analytics features progressively, to facilitate adaptation to the bandwidth between the mobile and the edge, and the battery status at the mobile device.
Prof. Yao Wang
New York University
Yao Wang is a Professor at New York University Tandon School of Engineering, with a joint appointment in Departments of Electrical and Computer Engineering and Biomedical Engineering. She is also Associate Dean for Faculty Affairs for NYU Tandon since June 2019. Her research areas include video coding and streaming, multimedia signal processing, computer vision, and medical imaging. She is the leading author of a textbook titled Video Processing and Communications, and has published over 300 papers in journals and conference proceedings. She received New York City Mayor's Award for Excellence in Science and Technology in the Young Investigator Category in year 2000. She was elected Fellow of the IEEE in 2004 for contributions to video processing and communications. She received the IEEE Communications Society Leonard G. Abraham Prize Paper Award in the Field of Communications Systems in 2004, and the IEEE Communications Society Multimedia Communication Technical Committee Best Paper Award in 2011. She was a keynote speaker at the 2010 International Packet Video Workshop, INFOCOM Workshop on Contemporary Video in 2014, the 2018 Picture Coding Symposium, the 2020 ACM Multimedia Systems Conference (MMSys’20), and the 2022 Picture Coding Symposium. She received the NYU Tandon Distinguished Teacher Award in 2016.
Workshop scope
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 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
Acceptance notification: 24-Apr-23
Camera-ready papers: 1-May-23
Workshop date: 10-Jul-23
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