Name
Title | ||
|---|---|---|
Hybrid Convolutional-Transformer framework for drone-based few-shot weakly supervised object detection |
Abstract | ||
|---|---|---|
Drone delivery is becoming a new trend in the logistics system, but few researches are developed in this field. Locating the target buildings in the drone camera is a crucial technique. However, it is difficult to collect extensive drone-view images and their bounding box annotations for supervised training. Therefore, we address this problem by formulating it as a weakly supervised task and using small amount of category labels as supervision. To extract representative features of cross-view and cross-device images, we propose a Hybrid Convolutional-Transformer (HCT) framework for detection given the very few image-level annotated images. To better evaluate the proposed method in the realistic drone delivery task, we build a drone-view object detection dataset based on the University-1652 benchmark by annotating bounding boxes of target buildings. Extensive experimental results demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.compeleceng.2022.108154 | Computers and Electrical Engineering |
Keywords | DocType | Volume |
Vision Transformer,Few-shot learning,Weakly supervised learning,Object detection | Journal | 102 |
ISSN | Citations | PageRank |
0045-7906 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengming Li | 1 | 2 | 1.45 |
| Linsong Xue | 2 | 0 | 0.68 |
| Lin Feng | 3 | 40 | 11.62 |
| Cuili Yao | 4 | 0 | 0.34 |
| Dong Wang | 5 | 1351 | 186.07 |