The First Workshop on Efficient and On-Device Generation (EDGE) at CVPR 2024 will focus on the latest advancements of generative AI in the computer vision domain, with an emphasis on efficiencies across multiple aspects. We encourage techniques that enable generative models to be trained more efficiently and/or run on resource-constrained devices, such as mobile phones and edge devices. Through these efforts, we envision a future where these permeating generative AI capabilities become significantly more accessible with virtuous scalability and plateauing carbon footprint.

The topics involved in the workshop include but are not limited to:

• Training efficiency aspect:
• Methods for reducing the memory footprint of generative models.
• Using knowledge distillation to support model compression.
• Design of efficient computational modules and algorithms for generative models
• Hardware-aware training acceleration and energy efficient computing.
• Online training under edge constraints.
• Parameter efficient tuning.
• Inference efficiency aspect:
• Techniques for reducing the computational complexity of generative models, such as model compression, quantization, sparsification, and distillation.
• Advanced sampling techniques for fast inference.
• Approaches for efficient deployment and distribution generative models across multiple devices or edge devices.
• Hardware-aware inference acceleration and energy efficient computing.
• Data efficiency aspect:
• Few-shot diffusion model.
• Generative model finetuning.
• Data efficiency for generative models and/or foundation models.
• Evaluation efficiency aspect:
• Efficient evaluation of generative models.
• Methods for benchmarking the efficiency of generative models.
• Challenges in evaluating the efficiency of generative models in diverse applications.
• Application-specific efficiency aspect:
• Applications of efficient generative AI in computer vision, such as image generation, style transfer, and super-resolution.
• Efficient personalization and post-generation editing.
• Real-time and efficient neural rendering.
• Efficient novel view synthesis.
• Interdisciplinary approaches that combine efficient generative AI with other fields, such as natural language processing, robotics, and human-computer interaction.
• Efficient generative models for video synthesis.
• Efficient interplay between image generation, video generation, and language generation such as efficient text2img, img2text, text2video, video2text, img2video, video2img, etc.
• Other related topics:
• Challenges and opportunities.
• Exploratory direction.
• New evaluation metrics.
• Privacy considerations.

Format: Submissions must use the CVPR 2024 Author Kit for Latex/Word Zip file and follow the CVPR 2024 author instructions and submission policies. Submissions need to be anonymized. The workshop considers two submission tracks:

• Long Paper: submissions are limited to 8 pages excluding references;
• Extended Abstract: submissions are limited to 4 pages excluding references.

Only long papers will be included in the CVPR 2024 proceedings.

Submission Site: https://cmt3.research.microsoft.com/CVPREDGE2024

Submission Deadline: March 22, 2024 (AOE) March 27, 2024 (AOE)

Workshop Date: June 17, 2024 (Full Day)