ICCV 2025 Workshop on Binary and Extreme Quantization for Computer Vision

Workshop Description

An open problem in deep learning is how to develop models which are more and more compact, lightweight and power efficient so that they can be effectively deployed on devices that billions of users use in their everyday life and work like cars, smart-phones, tablets, TVs etc. One of the most prominent methods for achieving all these goals is by training Binary Networks in which both the features and the weights can take only 2 values: +-1. Binarization can result in huge model compression and computational speeds; however, an open problem is how to train binary networks which maintain the same accuracy levels as their real-valued counterparts. Very recent research efforts have shown that training highly accurate Binary Networks is actually feasible opening-up the path of applying the models to a wide variety of Computer Vision problems. This workshop aims to cover both the development of novel methodologies for Binary Neural Networks and their application to Computer Vision, and bring together a diverse group of researchers working in several related areas.

Call for Papers

Authors are welcome to submit full 8-page papers or short 2-page extended abstracts on any of the following topics:

Binary and low-bit quantized models for LVLMs
Low-bit post-training quantization for LVLMs and Vision Transformers
New methodologies (optimization and objective functions) and architectures for training low-bit quantized neural networks
Applications of low-bit NNs for Computer Vision: image classification, semantic, instance & panoptic segmentation, pose estimation, object detection, 3D and video recognition, etc.
Binary and Low-bit quantization for generative models (Diffusion, Visual Autoregressive models, etc.)
Hardware implementation and on-device deployment of Low-bit NNs
Neural Architecture Search (NAS) for BNNs.
Frameworks and bare-metal implementations for binary and low-bit networks.
Federated learning with low-bit quantization.
On-device learning.

Important Dates

Paper submission deadline:	June 25th, 2025 (11:59pm PST)
Decisions:	~~TBD~~
Camera ready papers due:	~~TBD~~
Extended abstract submission:	~~TBD~~
Extended abstract decisions:	~~TBD~~
Workshop Date:	TBD

Submission Guidelines

Papers included in ICCV proceedings: Submitted (full 8-page) papers must be formatted using the ICCV 2025 template and should adhere to ICCV submission guidelines. The maximum file size for submissions is 50MB. The CMT-based review process will be double-blind. These submissions will be included in the proceedings and must contain new previously unpublished material.
Extended abstracts NOT included in ICCV proceedings: We encourage the submission of extended abstracts (2 pages plus references) that summarize previously published or unpublished work. Extended abstracts will undergo a light single-blind review process. Template for extended abstract can be found here.
- Previously published work: We welcome previously published papers from previous CV/ML conferences including ICCV 2025 which are within the scope of the workshop.
- Unpublished work: We also encourage the submission of papers that summarize work in-progress. The idea of this type of submission is the dissemination of preliminary results or methods that fall within the overall scope of the workshop.

Please upload submissions at: TBD