A Hard Negatives Mining and Enhancing Method for Multi-Modal Contrastive Learning

Contrastive learning has emerged as a dominant paradigm for understanding 3D open-world environments, particularly in the realm of multi-modalities. However, due to the nature of self-supervised learning and the limited size of 3D datasets, pre-trained models in the 3D point cloud domain often suffer from overfitting in downstream tasks, especially in zero-shot classification. To tackle this problem, we design a module to mine and enhance hard negatives from datasets, which are useful to improve the discrimination of models. This module could be seamlessly integrated into cross-modal contrastive learning frameworks, addressing the overfitting issue by enhancing the mined hard negatives during the process of training. This module consists of two key components: mining and enhancing. In the process of mining, we identify hard negative samples by examining similarity relationships between vision-vision and vision-text modalities, locating hard negative pairs within the visual domain. In the process of enhancing, we compute weighting coefficients via the similarity differences of these mined hard negatives. By enhancing the mined hard negatives while leaving others unchanged, we improve the overall performance and discrimination of models. A series of experiments demonstrate that our module can be easily incorporated into various contrastive learning frameworks, leading to improved model performance in both zero-shot and few-shot tasks.