Few-Shot Deep Hashing for Fine-Grained Visual Retrieval Using Self-Supervised Semantic Excavation Networks

Abstract

The explosive growth of visual data across domains such as biodiversity monitoring, e-commerce, and autonomous systems demands retrieval engines capable of distinguishing subtle inter-class differences among fine-grained categories while operating under severe label scarcity. Few-shot deep hashing has emerged as a promising paradigm to embed images into compact binary codes that preserve semantic similarity and support efficient large-scale search with limited labeled examples. This paper presents a systems-oriented examination of few-shot deep hashing for fine-grained visual retrieval, centering on self-supervised semantic excavation networks that extract rich discriminative structures without exhaustive supervision. We delineate the architectural choices that synthesize self-supervised pretext tasks, asymmetric hash coding, and meta-learning protocols into an integrated framework, and we analyze the structural trade-offs involving code length, retrieval accuracy, computational overhead, and resilience to data shift. Beyond algorithmic design, we investigate the broader system landscape, including cloud-edge deployment topologies, approximate nearest neighbor indexing, resource sustainability, and the socio-technical governance of fairness, privacy, and transparency. Through conceptual modeling and cross-domain illustrations, we argue that robust and equitable visual retrieval cannot be achieved by optimizing hashing objectives alone; it requires a holistic infrastructure that accounts for data curation biases, energy budgets, and regulatory compliance. The article concludes by outlining a forward-looking agenda for responsible few-shot hashing systems, emphasizing the need for interdisciplinary collaboration across machine learning, systems engineering, and policy-making.