Call For Papers
Important dates
Submission deadline: March 17, 2024 (11:59 PM Pacific Time)
Review starts: March 18, 2024
Review ends: April 03, 2024
Notification of acceptance: April 07, 2024
Camera-ready submission: April 12, 2024
topics of interest
The workshop targets computational efficiency of training, adaptation, and inference for large vision models including but not limited to:
Compression, quantization, conditional compute, pruning and distillation of LVMs
Efficient training of LVMs, e.g., low-rank adaptation
Efficient sampling of diffusion models, e.g., step distillation and consistency models
Efficient transformer architectures for LVMs, e.g., token pruning and merging
Efficient architectures for generation and editing of images, videos, and 3D objects
Efficient generative models, e.g., diffusion, auto-regressive and GANs
Efficient multi-modal generative models, e.g., for vision, language and audio
Deploying LVMs on low power devices e.g., smartphone
Submission Instructions
Submission link: https://cmt3.research.microsoft.com/ELVM2024
Format: We accept two forms of papers:
Short paper: short papers should not exceed 4 pages (excluding references). Short papers are intended to share exciting and novel early stage ideas. A short paper should describe ideas and have preliminary experiments to support the ideas, although extensive comparisons and analyses are not needed.
Long paper: Long papers should not exceed 8 pages (excluding references). Long papers are intended to present mature works, not only describing novel ideas but also have sufficient experiments and comparisons to support the proposed ideas.
All papers need to be formatted as per the CVPR 2024 guidelines. Please use the template made available at CVPR2024AuthorGuidelines. Papers beyond 8 pages in length (excluding references) will be desk rejected.
Publication: All accepted papers will be published as part of the CVPR'24 workshop proceedings.
Please feel free to post your questions to habibian.a.h@gmail.com.