Parsing Objects at a Finer Granularity: A Survey

Abstract

Fine-grained visual parsing, including fine-grained part segmentation and fine-grained object recognition, has attracted considerable critical attention due to its importance in many real-world applications, e.g., agriculture, remote sensing, and space technologies. Predominant research efforts tackle these fine-grained sub-tasks following different paradigms, while the inherent relations between these tasks are neglected. Moreover, given most of the research remains fragmented, we conduct an in-depth study of the advanced work from a new perspective of learning the part relationship. In this perspective, we first consolidate recent research and benchmark syntheses with new taxonomies. Based on this consolidation, we revisit the universal challenges in fine-grained part segmentation and recognition tasks and propose new solutions by part relationship learning for these important challenges. Furthermore, we conclude several promising lines of research in fine-grained visual parsing for future research.

Survey for fine-grained part segmentation and object recognition; Accepted by Machine Intelligence Research (MIR, 2024)

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Rethinking Atrous Convolution for Semantic Image Segmentation
• Learning Transferable Visual Models From Natural Language Supervision
• BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
• Part-guided Relational Transformers for Fine-grained Visual Recognition
• Describe me if you can! Characterized Instance-level Human Parsing
• Learning to Annotate Part Segmentation with Gradient Matching
• Unsupervised Co-part Segmentation through Assembly