Multi-granularity attention in attention for person re-identification in aerial images

In marrying with Unmanned Aerial Vehicles (UAVs), the person re-identification (re-ID) techniques are further strengthened in terms of mobility. However, the simple hybridization brings unavoidable scale diversity and occlusions caused by the altitude and attitude variations during the flight of UAVs. To harmoniously blend the two techniques, in this research, we argue that the pedestrian should be globally perceived regardless of the scale variation, and the internal occlusions should also be well suppressed. For this purpose, we propose a novel Multi-granularity Attention in Attention (MGAiA) network to satisfy the raised demands for the aerial-based re-ID. Specifically, a novel multi-granularity attention (MGA) module is designed to supply the feature extraction model with a global awareness to explore the discriminative knowledge within scale variations. Subsequently, an Attention in Attention (AiA) mechanism is proposed to generate attention scores for measuring the importance of the different granularity, thereby proactively reducing the negative efforts caused by occlusions. We carry out comprehensive experiments on two large-scale UAV-based datasets including PRAI-1581 and P-DESTRE, as well as the transfer learning from three popular ground-based re-ID datasets CUHK03, Market-1501, and CUHK-SYSU to quantify the effectiveness of the proposed method.