SNF-Feat: Semantic-Guided Negative-Sample-Free Representation Learning for Local Feature Extraction

Local feature extraction constitutes a foundational module crucial for numerous downstream tasks of computer vision. Its primary challenge lies in the generation of discriminative feature representations. Prior methodologies have employed contrastive learning within their pipelines, yet have encountered limitations stemming from inherent conflicts within their training data, including the ambiguity of negative samples and the distortion of positive samples. In this study, we propose a semantic-guided negative-sample-free method for local feature learning, denoted as SNF-Feat. Our framework entails dense patch-level representation learning without reliance on negative samples, aiming to ensure that descriptors derived from transformed views of the same local area exhibit predictive capability towards each other. To assess the impact of positive sample distortion, we harness high-level semantic information to derive point-wise loss weights. Furthermore, we establish a self-supervised feature learning paradigm that extends our utilization of datasets. Experimental results demonstrate the superior performance of our method across a range of typical datasets and tasks in comparison to state-of-the-art approaches.