Empowering Zero-Shot Object Detection: A Human-in-the-Loop Strategy for Unveiling Unseen Realms in Visual Data

Abstract

This paper delves into the paradigm of zero-shot object detection, a fundamental challenge in computer vision. Traditional approaches encounter limitations in recognizing novel objects, prompting the exploration of innovative strategies. The paper introduces a transformative Human-in-the-Loop (HITL) strategy, synergizing machine learning with human intelligence to revolutionize the recognition and localization of unseen objects in visual data. The Human-in-the-Loop strategy comprises a deep learning base, involving cutting-edge models like convolutional neural networks (CNNs), human-in-the-loop iterations with strategic input from annotators, and adaptive model refinement based on human annotations. Insights from diverse case studies are integrated, providing a nuanced understanding of the Human-in-the-Loop strategy’s effectiveness. The discussion examines the strengths and limitations of the Human-in-the-Loop strategy, addressing scalability and applicability across domains. The exploration of adaptive model refinement, exemplified by classical works and recent developments, underscores its pivotal role in enhancing adaptability to diverse objects. Case studies, such as Human-in-the-Loop in population health and the application of digital twins, are included. By synergizing machine learning and human expertise, it aims to redefine the landscape of object recognition. The comprehensive discussion and case studies underscore the potential impact of the Human-in-the-Loop strategy on advancing computer vision capabilities, paving the way for future developments in the field.