SuperVLAD: Compact and Robust Image Descriptors for Visual Place Recognition Feng Lu1,2 Canming Ye1 Shuting Dong

Visual place recognition (VPR) is an essential task for multiple applications such as augmented reality and robot localization. Over the past decade, mainstream methods in the VPR area have been to use feature representation based on global aggregation, as exemplified by NetVLAD. These features are suitable for largescale 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.