Fine-Grained Image Analysis with Deep Learning: A Survey (2111.06119v2)

Abstract: Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.

• The paper redefines FGIA by integrating fine-grained image recognition and retrieval to leverage shared deep learning strategies.
• It categorizes techniques into paradigms like localization-classification, end-to-end encoding, and external information integration with detailed analysis.
• It identifies future directions including robust datasets, multi-modal data usage, and enhanced interpretability to advance real-world applications.

Fine-Grained Image Analysis with Deep Learning

This paper provides a comprehensive survey on the recent advancements in fine-grained image analysis (FGIA) using deep learning methods. Fine-grained image analysis is an influential domain in computer vision, focusing on distinguishing among subordinate categories within a superior class, such as different bird species, dog breeds, or car models. Given the small inter-class and large intra-class variations inherent to fine-grained tasks, FGIA remains a challenging problem in pattern recognition.

The authors redefine the field of FGIA by encompassing both fine-grained image recognition and fine-grained image retrieval. They argue that while these two tasks have often been studied independently, they share many common techniques and challenges, suggesting a synergistic relationship between the two. This unified landscape provides a broader perspective, enabling the synthesis of methods and insights across both recognition and retrieval domains.

Fine-Grained Image Recognition

The survey categorizes existing fine-grained recognition approaches into three main paradigms:

1. Localization-Classification Subnetworks: These methods rely on identifying discriminative parts of objects and using these localized features for fine-grained recognition. Techniques include detection and segmentation, deep filter utilization, and leveraging attention mechanisms. A significant trend within this paradigm is the shift from strongly-supervised to weakly-supervised approaches, reducing dependence on dense part annotations.
2. End-to-End Feature Encoding: This paradigm focuses on learning discriminative representations directly through high-order feature interactions and specific loss functions. Methods like Bilinear CNNs and their variants are highlighted for effectively capturing second-order statistics to improve fine-grained recognition.
3. External Information: By integrating external sources such as web data or additional modalities (e.g., text descriptors), these approaches seek to augment and disambiguate fine-grained tasks. This paradigm highlights the utility of multi-modal data in improving recognition outcomes.

Fine-Grained Image Retrieval

The retrieval aspect is divided into:

1. Content-Based Image Retrieval (CBIR): Here, the objective is to return images with similar fine-grained attributes as a query, based on image content alone. The survey presents methods that employ metric learning and descriptor selection to refine retrieval processes.
2. Sketch-Based Image Retrieval (SBIR): This uses user-generated sketches as queries to retrieve corresponding images, presenting unique challenges due to the domain gap between sketches and images. Approaches in FG-SBIR emphasize learning a shared embedding space to bridge this gap.

Shared Techniques and Future Directions

The paper identifies shared techniques across both recognition and retrieval, such as deep metric learning and multi-modal matching, underscoring their complementary nature. This thematic coherence is essential for advancing FGIA through shared insights and methodologies.

The survey also discusses open questions and future directions, such as the development of large-scale, realistic datasets, applying FGIA techniques to 3D and multi-modal data, and addressing robustness and interpretability issues in fine-grained systems. The authors point out the need for more precise definitions of "fine-grained" tasks, suggesting progress in creating more realistic and complex FGIA benchmarks that simulate real-world challenges.

In conclusion, this survey presents a detailed and structured overview of FGIA, highlighting the significant strides made through deep learning. It sets the stage for future research by identifying current gaps and proposing directions in which the FGIA community can expand, with an emphasis on more holistic and scalable solutions to both theoretical and practical challenges in the domain.