Progressive Unsupervised Learning of Local Descriptors

Abstract

Training tuple construction is a crucial step in unsupervised local descriptor learning. Existing approaches perform this step relying on heuristics, which suffer from inaccurate supervision signals and struggle to achieve the desired performance. To address the problem, this work presents DescPro, an unsupervised approach that progressively explores both accurate and informative training tuples for model optimization without using heuristics. Specifically, DescPro consists of a Robust Cluster Assignment (RCA) method to infer pairwise relationships by clustering reliable samples with the increasingly powerful CNN model, and a Similarity-weighted Positive Sampling (SPS) strategy to select informative positive pairs for training tuple construction. Extensive experimental results show that, with the collaboration of the above two modules, DescPro can outperform state-of-the-art unsupervised local descriptors and even rival competitive supervised ones on standard benchmarks.