attention_mechanisms_VPR

In this paper, we investigate the integration of attention mechanisms guided by semantic segmentation into a VPR network which uses the Generalized Constructive Loss (GCL) function. Our objective was to identify the impact of this approach on the network’s performance and to assess its effec�tiveness in enhancing the overall network. To derive informative features from input images, we employ the DeepLabv3+ model. We take two distinct approaches: the first involves generating a mask that highlights significant object categories crucial for VPR, such as stationary objects, while the second approach incorporates all semantic features as input to the VPR network, with their weighting determined by the network itself. While our results may have fallen slightly below the benchmark, our findings shed light on the potential avenues for improvement and offer valuable insights into the utilization of attention mechanisms in VPR systems.