A survey on deep learning-based fine-grained object classification and semantic segmentation

By Bo Zhao, Jiashi Feng, Xiao Wu, Shuicheng Yan. International Journal of Automation and Computing, 2017

307 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a65d672715e2c58a01d693b48b9f14b68d4916bf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Yu Qiao
4. Yi Yang
5. Ming-Hsuan Yang
6. Andrea Vedaldi
7. Xiaogang Wang
8. Chunhua Shen

Citations in the corpus

• Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images (2018, unknown): “…computer vision, attention mechanisms are applied to a variety of problems, including image classification (Jetley et al., 2018; Wang et al., 2017a; Zhao et al., 2017), segmentation (Ren and Zemel, 2016), action recognition (Liu et al., 2017; Pei et al., 2016; Wang et al., 2017b), image captioning…”
• xView: Objects in Context in Overhead Imagery (2018, background): “Fine grained object detection is a difficult task and an ongoing area of research [30, 27, 14].”
• CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models (2021, background): “(2019), object classification (Zhao et al., 2017) and scene graph classification (Xu et al.”
• Understanding Deep Learning Techniques for Image Segmentation (2019, background): “Earlier studies [185] have shown the potential of deep learning–based approaches.”
• Fine-Grained Image Analysis With Deep Learning: A Survey (2021, background): “Our approach is significantly different to existing surveys [25], [26] that focus solely on the problem of fine-grained recognition/classification, which as we argue only constitutes part of the larger study of FGIA.”
• Object-Part Attention Model for Fine-Grained Image Classification (2017, methodology): “These methods can be divided into three groups [26]: ensemble of networks based methods, visual attention based methods and part detection based methods.”
• BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition (2019, background): “Note that, besides the extreme imbalance, the iNaturalist datasets also face the fine-grained problem [32, 35, 30, 31].”
• Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images (2020, background): “However, these general models are not satisfactory for CT images classification because CT images are fine-grained that have low interclass variances, and thus are difficult to distinguish [28].”