Contrastive Vision Language

Contrastive vision-language models aim to learn joint representations of images and text by contrasting similar and dissimilar pairs, enabling zero-shot and few-shot learning capabilities. Current research focuses on improving the quality and robustness of these representations, addressing limitations in handling fine-grained details, compositional language, and biases stemming from training data, often employing techniques like patch-level comparisons, hierarchical attention mechanisms, and data augmentation strategies within model architectures such as CLIP and its variants. This field is significant for its potential to improve various computer vision tasks, including image classification, segmentation, and retrieval, particularly in low-resource settings and applications requiring adaptability to diverse data distributions.