SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition

These features are suitable for large-scale VPR and robust against viewpoint changes. However, the VLAD-based aggregation methods usually learn a large number of ( e.g., 64) clusters and their corresponding cluster centers, which directly leads to a high dimension of the yielded global features. More importantly, when there is a domain gap between the data in training and inference, the cluster centers determined on the training set are usually improper for inference, resulting in a performance drop. To this end, we first attempt to improve NetVLAD by removing the cluster center and setting only a small number of ( e.g., only 4) clusters.