Name
Title | ||
|---|---|---|
CoCoNet: A Collaborative Convolutional Network applied to fine-grained bird species classification |
Abstract | ||
|---|---|---|
We present an end-to-end deep network for fine-grained visual categorization called Collaborative Convolutional Network (CoCoNet). The network uses a collaborative layer after the convolutional layers to represent an image as an optimal weighted collaboration of features learned from training samples as a whole rather than one at a time. This gives CoCoNet more power to encode the fine-grained nature of the data with limited samples. We perform a detailed study of the performance with 1-stage and 2-stage transfer learning. The ablation study shows that the proposed method outperforms its constituent parts consistently. CoCoNet also outperforms few state-of-the-art competing methods. Experiments have been performed on the fine-grained bird species classification problem as a representative example, but the method may be applied to other similar tasks. We also introduce a new public dataset for fine-grained species recognition, that of Indian endemic birds and have reported initial results on it. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IVCNZ51579.2020.9290677 | 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | DocType | ISSN |
fine-grained visual categorization (FGVC),collaborative representation classifiers (CRC),bird species recognition,deep transfer learniing | Conference | 2151-2191 |
ISBN | Citations | PageRank |
978-1-7281-8580-4 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tapabrata Chakraborti | 1 | 18 | 5.06 |
| brendan mccane | 2 | 223 | 33.05 |
| Steven Mills | 3 | 186 | 18.39 |
| Umapada Pal | 4 | 1477 | 139.32 |