Research on Understanding and Improving Deep Hashing Retrieval under Incremental Data

Purpose-The rapid growth of industries such as smart cities, intelligent transportation, and the Internet of Things has led to an explosion of big data, making data retrieval a pivotal basic technology for numerous practical applications. The objective is to facilitate faster and more accurate retrieval of relevant data to realize intelligent data services. Among these, deep hash-based cross-modal data retrieval with incremental learning has garnered significant attention for its vast application potential and has also captured the interest of industry professionals and technologists.Design/methodology/approach-This study approaches the understanding and enhancement of data retrieval models from the angles of data, characteristics, and coding within practical application systems. The further comprehension of the retrieval model is primarily exemplified by: 1. A deeper understanding of dynamic depth models. Deep models are often viewed as black boxes, yet dynamically changing data necessitates the further integration of multiple deep retrieval models. This project aims to investigate and comprehend the connectivity of dynamic depth models, drawing from the concept of mode connectivity in loss landscapes within existing deep networks, to provide a more robust theoretical explanation for data retrieval models under dynamic changes. 2. A more profound understanding of the retrieval capabilities of hash coding. The loss of information during the hash binary coding process is a significant bottleneck contributing to the decline in retrieval performance. This project endeavors to define the retrieval capability from a quantitative perspective and to elucidate the reasons behind the weaker retrieval ability of hash coding, thereby enabling researchers to gain a more nuanced understanding. Based on these insights, the enhancement of the retrieval model is primarily reflected in: The improvement of the feature expression ability of the dynamic depth model, exploring linear connectivity, attention-based connectivity, and non-linear connectivity methods; and the enhancement of the retrieval ability of hash coding, researching approaches from Bayesian methods and segmented coding, among others.Findings-In conclusion, deep Hashing holds clear scientific significance and promising application prospects, with the potential to support critical practical applications such as human-machine dialogue, intelligent transportation, smart healthcare, and government governance.Practical impliactions-The multimodal hash retrieval technique and object keypoint positioning technique examined in this paper can be utilized to automatically identify object keypoints (e.g., locating the eyes, nose, and chin in a facial image).This task is a fundamental technology for various computer applications, including face recognition, object tracking, and pose estimation. The precise positioning of object keypoints is a fundamental problem with far-reaching social and economic impacts, applicable to fine portrait analysis technology, commodity search, security systems, image recognition, machine translation, identity verification, intelligent case processing, intelligent transportation, intelligent finance, intelligent education, intelligent manufacturing, and big data governance, among other systems.Originality/value-With the advent of new generations of technological revolutions in artificial intelligence and big data, any breakthrough in related technologies portends immeasurable commercial value and societal benefits (such as chatGPT and GPT-4o technologies).