TPAMI Special Issue: Transformer Models in Vision

[salman khan]

Dear All,

Together with Ming-Hsuan Yang (UC Merced, Google), Mubarak Shah (UCF), Niki Parmar (Google), Ashish Vaswani (Google) and Fahad Khan (MBZUAI, Linköping), we are organizing a special issue (SI) in IEEE TPAMI on "Transformer Models in Vision".

Submission Deadline: December 15, 2021.

Submission online portal: https://mc.manuscriptcentral.com/tpami-cs (authors would need to select this Special Issue)

The detailed CFP can be found at:

https://www.computer.org/digital-library/journals/tp/call-for-papers-special-issue-on-transformer-models-in-vision

This special issue seeks original contributions towards advancing the theory, architecture, and algorithmic design for transformer models in computer vision, as well as novel applications and use cases. We envision original and well-motivated adaptations of transformer models for vision tasks and efforts towards improving their accuracy, robustness, and efficiency. The special issue will provide a timely collection of recent advances to benefit the researchers and practitioners working in the broad research field of computer vision, pattern analysis, and machine intelligence. Topics of interest include (but are not limited to):

• Theoretical insights into transformer-based models
• Efficient transformer architectures, including novel mechanisms for self-attention
• Novel transformer models for spatial (image) and temporal (video) data modeling
• Visualizing and interpreting transformer networks
• Generative models for transformer networks
• Hybrid network designs combining the strengths of transformer models with convolutional and graph-based models
• Unsupervised, weakly supervised, and semi-supervised learning with transformer models
• Multi-modal learning combining visual data with text, speech, and knowledge graphs
• Leveraging multi-spectral data like satellite imagery and infrared images in transformer models for improved semantic understanding of visual content
• Transformer-based designs for low-level vision problems such as image super-resolution, deblurring, de-raining, and denoising
• Novel transformer-based methods for high-level vision problems such as object detection, segmentation, activity recognition, and pose estimation
• Transformer models for volumetric, mesh, and point-cloud data processing in 3D and 4D data regimes

I have also attached the pdf version of the special issue CFP if you would like to distribute it to different channels.

Regards,

Salman Khan

MBZUAI, ANU