Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via
Coarse-to-Fine Framework
Abstract
Vehicle detection and recognition in remote sensing
images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce
a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based
vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized
zero-shot vehicle detection, which is challenging
due to the requirement of recognizing vehicles that
are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the
framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then
recognizes vehicles in a coarse-grained manner.
Additionally, the hierarchical DeepLab v3 model is
beneficially compatible to combine the generalized
zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test
comparative methods, we therefore construct a new
dataset to fill this gap of evaluation. The experimental results show that the proposed framework
yields promising results on the imperative yet diffi-
cult task of zero-shot vehicle detection and recognition