Zero-Shot Learning for Computer Vision Applications

Human beings possess the remarkable ability to recognize unseen concepts by integrating their visual perception of known concepts with some high-level descriptions. However, the best-performing deep learning frameworks today are supervised learners that struggle to recognize concepts without training on their labeled visual samples. Zero-shot learning (ZSL) has recently emerged as a solution that mimics humans and leverages multimodal information to transfer knowledge from seen to unseen concepts. This study aims to emphasize the practicality of ZSL, unlocking its potential across four different applications in computer vision, namely -- object recognition, object detection, action recognition, and human-object interaction detection. Several task-specific challenges are identified and addressed in the presented research hypotheses. Zero-shot frameworks are proposed to attain state-of-the-art performance, elucidating some future research directions as well.