Deep Learning for Fine-Grained Image Analysis: A Survey

TL;DR

This survey reviews recent deep learning techniques for fine-grained image analysis, covering recognition, retrieval, and generation, along with datasets and applications, highlighting future research directions.

Contribution

It systematically categorizes recent deep learning approaches in FGIA and discusses datasets, applications, and open problems, providing a comprehensive overview.

Findings

Deep learning has significantly advanced FGIA performance.

The survey categorizes FGIA into recognition, retrieval, and generation.

Identifies key datasets and future research challenges.

Abstract

Computer vision (CV) is the process of using machines to understand and analyze imagery, which is an integral branch of artificial intelligence. Among various research areas of CV, fine-grained image analysis (FGIA) is a longstanding and fundamental problem, and has become ubiquitous in diverse real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, \eg, species of birds or models of cars. The small inter-class variations and the large intra-class variations caused by the fine-grained nature makes it a challenging problem. During the booming of deep learning, recent years have witnessed remarkable progress of FGIA using deep learning techniques. In this paper, we aim to give a survey on recent advances of deep learning based FGIA techniques in a systematic way. Specifically, we organize the existing studies of FGIA techniques into three major categories: fine-grained image recognition, fine-grained image retrieval and fine-grained image generation. In addition, we also cover some other important issues of FGIA, such as publicly available benchmark datasets and its related domain specific applications. Finally, we conclude this survey by highlighting several directions and open problems which need be further explored by the community in the future.