Name
Abstract | ||
|---|---|---|
We tackle the problem of analyzing and retrieving technical drawings. First, we introduce DeepPatent, a new large-scale dataset for recognition and retrieval of design patent drawings. The dataset provides more than 350,000 design patent drawings for the purpose of image retrieval. Unlike existing datasets, DeepPatent provides fine-grained image retrieval associations within the collection of drawings and does not rely on cross-domain associations for supervision. We develop a baseline deep learning model, named Patent-Net, based on best practices for training retrieval models for static images. We demonstrate the superior performance of PatentNet when trained on our fine-grained associations of DeepPatent against other deep learning approaches and classic computer vision descriptors. With the introduction of this new dataset, and benchmark algorithms, we demonstrate that the analysis and retrieval of technical drawings remains an open challenge in computer vision; and that patent drawing retrieval provides a real-world testbench to spur research. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/WACV51458.2022.00063 | 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) |
Keywords | DocType | ISSN |
Datasets, Evaluation and Comparison of Vision Algorithms Image/Video Indexing and Retrieval | Conference | 2472-6737 |
ISBN | Citations | PageRank |
978-1-6654-0916-2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michal Kucer | 1 | 0 | 0.34 |
| Diane Oyen | 2 | 0 | 0.34 |
| Juan Castorena | 3 | 0 | 0.34 |
| Jian Wu | 4 | 0 | 2.37 |