On this week's Journal Club session, Shamim Ibne Shahid will talk about the book "Computer Vision and Image Analysis for Industry 4.0".

Eight years ago, since the Omniglot data was first released, very few papers have addressed the original Omniglot challenge, which is to carry out within-alphabet one-shot classification tasks as opposed to selecting the test samples between the al- phabets. Most researchers have made the task easier by introducing new splits in the dataset and have taken advantage of significant sample and class augmentation. Amongst the deep learning models that have adopted the Omniglot challenge as it is, the Recursive Cortical network has the highest performance of 92.75%. In this presentation , I will talk about a new similarity function to aid in the training procedure of matching network, which helps achieve 95.75% classification accuracy on the Omniglot challenge without requiring any data augmentation.

Reference:

• N.Siddique, M. S. Arefin, M. A. R. Ahad, and M. A. A. Dewan, "Computer Vision and Image Analysis for Industry 4.0". CRC Press, 2023.

Date: 2023/12/08
Time: 14:00
Location: online