Second IEEE Workshop on Coding for Machines
July 15-19, 2024, Niagara Falls, Canada
in conjunction with IEEE ICME 2024
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), MPEG FCM (Feature 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
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, etc.)
Datasets for coding for machines
Error resilience in coding for machines
Privacy and security in coding for machines
Submission instructions
Click on "+Create new submission..."
Select ICME2024 Workshop-VCM
Follow the submission instructions
Important dates
Paper submission: 6 Apr 2024
Acceptance notification: 3 May 2024
Camera-ready papers: TBA
Organizers
Fengqing Maggie Zhu, Purdue University, USA
Heming Sun, Yokohama National University, Japan
Hyomin Choi, InterDigital, USA
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
Saeed Ranjbar Alvar, Huawei, Canada
Shiqi Wang, City University of Hong Kong
Shurun Wang, Alibaba DAMO Academy, China
Li Zhang, ByteDance, USA