ImagebindDC: Compressing Multi-modal Data with Imagebind-based Condensation

The introduction of ImageBindDC marks a significant advancement in data condensation techniques, particularly for multimodal datasets. Traditional methods often struggle to maintain the intricate dependencies between different data types, but ImageBindDC leverages a Characteristic Function (CF) loss to ensure precise statistical alignment across modalities. This innovative approach not only facilitates uni-modal and cross-modal alignment but also captures the complete multivariate structure of the data. Remarkably, models trained on just five condensed data points per class using this framework achieved lossless performance comparable to those trained on the entire NYU-v2 dataset, demonstrating an 8.2% absolute improvement in performance. Such capabilities are vital as they enable more efficient model training, reducing the computational resources required while maintaining high accuracy, thus paving the way for broader applications in AI.