Transformer models have demonstrated excellent performance on a diverse set of computer vision applications ranging from classification to segmentation on various data modalities such as images, videos, and 3D data. The goal of this workshop is to bring together computer vision and machine learning researchers working towards advancing the theory, architecture, and algorithmic design for vision transformer models, as well as the practitioners utilizing transformer models for novel applications and use cases.
The workshop’s motivation is to narrow the gap between the research advancements in transformer designs and applications utilizing transformers for various computer vision applications. The workshop also aims to widen the adaptation of transformer models for various vision-related industrial applications. We are interested in papers reporting their experimental results on the utilization of transformers for any application of computer vision, challenges they have faced, and their mitigation strategy on topics like, but not limited to image classification, object detection, segmentation, human-object interaction detection, scene understanding based on 3D, video, and multimodal inputs.
Inria/Google
Technical University of Munich
Technion - Israel Institute of Technology
The best paper will receive an award worth 1000 USD.
We accept paper submissions to our workshop. All submissions should follow the Neurips2022 author guidelines.
Call for paper: pdf
Paper Submission Due: September 22th, 2022
Extended Paper Submission Due: October 03rd, 2022
Notification to Authors: October 19th, 2022
Extended Notification to Authors: October 23rd, 2022
Camera-ready Deadline: October 27th, 2022