Name
Abstract | ||
|---|---|---|
This paper aims to tackle the challenging problem of one-shot object detection. Given a query image patch whose class label is not included in the training data, the goal of the task is to detect all instances of the same class in a target image. To this end, we develop a novel co-attention and co-excitation (CoAE) framework that makes contributions in three key technical aspects. First, we propose to use the non-local operation to explore the co-attention embodied in each query-target pair and yield region proposals accounting for the one-shot situation. Second, we formulate a squeeze-and-co-excitation scheme that can adaptively emphasize correlated feature channels to help uncover relevant proposals and eventually the target objects. Third, we design a margin-based ranking loss for implicitly learning a metric to predict the similarity of a region proposal to the underlying query, no matter its class label is seen or unseen in training. The resulting model is therefore a two-stage detector that yields a strong baseline on both VOC and MS-COCO under one-shot setting of detecting objects from both seen and never-seen classes. Codes are available at https://github.com/timy90022/One-Shot-Object-Detection. |
Year | Venue | Field |
|---|---|---|
2019 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019) | Computer vision,Object detection,Computer science,Excitation,Artificial intelligence,Machine learning |
DocType | Volume | ISSN |
Conference | 32 | 1049-5258 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsieh, Ting-I | 1 | 0 | 0.34 |
| Lo, Yi-Chen | 2 | 0 | 1.01 |
| Hwann-Tzong Chen | 3 | 826 | 52.13 |
| Tyng-Luh Liu | 4 | 1384 | 85.56 |