  Street scene change detection continues to capture researchers' interests in
the computer vision community. It aims to identify the changed regions of the
paired street-view images captured at different times. The state-of-the-art
network based on the encoder-decoder architecture leverages the feature maps at
the corresponding level between two channels to gain sufficient information of
changes. Still, the efficiency of feature extraction, feature correlation
calculation, even the whole network requires further improvement. This paper
proposes the temporal attention and explores the impact of the dependency-scope
size of temporal attention on the performance of change detection. In addition,
based on the Temporal Attention Module (TAM), we introduce a more efficient and
light-weight version - Dynamic Receptive Temporal Attention Module (DRTAM) and
propose the Concurrent Horizontal and Vertical Attention (CHVA) to improve the
accuracy of the network on specific challenging entities. On street scene
datasets `GSV', `TSUNAMI' and `VL-CMU-CD', our approach gains excellent
performance, establishing new state-of-the-art scores without bells and
whistles, while maintaining high efficiency applicable in autonomous vehicles.
