Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

Abstract

The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection = recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few = one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection = recognition systems using ZSL.

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“…where M ∈ R 3×4 is the transformation matrix with m ij elements in Equation (16), that maps the world coordinate points into the image points based on the camera location and the reference frame, provided by the Camera Intrinsic Matrix K (Equation (15)), Rotation Matrix R (Equation 13) and the Translation Matrix T (Equation (14)).…”

“…In such situations, Artificial Intelligence can play an important role in facilitating social distancing monitoring. Computer Vision, as a sub-field of Artificial Intelligence, has been very successful in solving various complex health care problems and has shown its potential in chest CT-Scan or X-ray based COVID-19 recognition [16,17] and can contribute to Social-distancing monitoring as well. Besides, deep neural networks enable us to extract complex features from the data so that we can provide a more accurate understanding of the images by analysing and classifying these features.…”

Complex Human Action Recognition Using a Hierarchical Feature Reduction and Deep Learning-Based Method

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DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

“…In such situations, Artificial Intelligence can play an important role in facilitating social distancing monitoring. Computer vision, as a subset of Artificial Intelligence, has been very successful in solving various complex health care problems and has shown its potential in Chest CT-Scan or X-Ray based COVID-19 recognition 11,12 . Besides, deep neural networks enable us to extract complex features from the data so that we can provide a more accurate understanding of the images by analysing and classifying these features.…”