Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks

• URL: http://arxiv.org/abs/2104.11200v1
• Date: Thu, 22 Apr 2021 17:32:14 GMT
• Title: Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
• Authors: Yuansheng Hua, Lichao Moua, Jianzhe Lin, Konrad Heidler, Xiao Xiang Zhu
• Abstract summary: We propose a prototype-based memory network to recognize multiple scenes in a single image. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset.
• Score: 14.218223473363276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Therefore, in this paper, we propose to take a step forward to a more practical and challenging task, namely multi-scene recognition in single images. Moreover, we note that manually yielding annotations for such a task is extraordinarily time- and labor-consuming. To address this, we propose a prototype-based memory network to recognize multiple scenes in a single image by leveraging massive well-annotated single-scene images. The proposed network consists of three key components: 1) a prototype learning module, 2) a prototype-inhabiting external memory, and 3) a multi-head attention-based memory retrieval module. To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory. Afterwards, a multi-head attention-based memory retrieval module is devised to retrieve scene prototypes relevant to query multi-scene images for final predictions. Notably, only a limited number of annotated multi-scene images are needed in the training phase. To facilitate the progress of aerial scene recognition, we produce a new multi-scene aerial image (MAI) dataset. Experimental results on variant dataset configurations demonstrate the effectiveness of our network. Our dataset and codes are publicly available.

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