Fine-Grained Image Analysis with Deep Learning: A Survey

• URL: http://arxiv.org/abs/2111.06119v1
• Date: Thu, 11 Nov 2021 09:43:56 GMT
• Title: Fine-Grained Image Analysis with Deep Learning: A Survey
• Authors: Xiu-Shen Wei and Yi-Zhe Song and Oisin Mac Aodha and Jianxin Wu and Yuxin Peng and Jinhui Tang and Jian Yang and Serge Belongie
• Abstract summary: Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition. This paper attempts 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.
• Score: 146.22351342315233
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Deep Graph Anomaly Detection: A Survey and New Perspectives [86.84201183954016]
  Graph anomaly detection (GAD) aims to identify unusual graph instances (nodes, edges, subgraphs, or graphs) Deep learning approaches, graph neural networks (GNNs) in particular, have been emerging as a promising paradigm for GAD.
  arXiv  Detail & Related papers  (2024-09-16T03:05:11Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends [6.48586558584924]
  It aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised.
  arXiv  Detail & Related papers  (2022-09-14T03:35:25Z)
• Deep Image Deblurring: A Survey [165.32391279761006]
  Deblurring is a classic problem in low-level computer vision, which aims to recover a sharp image from a blurred input image. Recent advances in deep learning have led to significant progress in solving this problem.
  arXiv  Detail & Related papers  (2022-01-26T01:31:30Z)
• Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization [15.627971638835948]
  The Ultra-fine-grained visual categorization (Ultra-FGVC) problems have been understudied. FGVC aims at classifying objects from the same species, while the Ultra-FGVC targets at more challenging problems of classifying images at an ultra-fine granularity. The challenges for Ultra-FGVC mainly comes from two aspects: one is that the Ultra-FGVC often arises overfitting problems due to the lack of training samples. A mask-guided feature extraction and feature augmentation method is proposed in this paper to extract discriminative and informative regions of images.
  arXiv  Detail & Related papers  (2021-09-16T06:57:05Z)
• Automatic Gaze Analysis: A Survey of DeepLearning based Approaches [61.32686939754183]
  Eye gaze analysis is an important research problem in the field of computer vision and Human-Computer Interaction. There are several open questions including what are the important cues to interpret gaze direction in an unconstrained environment. We review the progress across a range of gaze analysis tasks and applications to shed light on these fundamental questions.
  arXiv  Detail & Related papers  (2021-08-12T00:30:39Z)
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [79.48829365560788]
  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios. HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
  arXiv  Detail & Related papers  (2020-11-30T15:03:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.