Contrastive Learning for Large-scale Color-Name Dataset: Tackling Sparsity with Negative Sampling

Large-scale color datasets exhibit significant sparsity in name-color correspondences, substantially impeding the effectiveness of conventional methodologies. We propose a contrastive learning-based framework for color name generation and recommendation that addresses sparsity through negative sampling, supporting two core tasks: color-to-name recommendation and name-to-color generation. Our framework employs a multi-task contrastive learning architecture comprising three key components: (1) a pre-trained Transformer-based name encoder, (2) an RGB encoder, and (3) an RGB generator. The framework utilizes negative sampling to construct positive-negative pairs, contrasting RGB encoder outputs with positive and negative name embeddings. We adopt a multi-objective optimization strategy incorporating binary cross-entropy loss for neural collaborative filtering, and mean squared error loss for name-to-RGB mapping. Experimental results demonstrate substantial improvements over baseline methods, achieving 71.26% Top-10 accuracy in color-to-name recommendation and reducing CIELAB distance error to 26.61 in name-to-color generation.