Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment

The introduction of Boundary-Aware Curriculum with Local Attention (BACL) marks a significant advancement in the field of multimodal models, which traditionally treat all negative pairs uniformly, often overlooking nuanced differences. By focusing on ambiguous negatives, BACL utilizes a Boundary-aware Negative Sampler to gradually increase difficulty and a Contrastive Local Attention loss to pinpoint mismatches. This innovative approach not only theoretically predicts a rapid O(1/n) error rate but also showcases practical improvements, achieving up to a 32% increase in retrieval accuracy over the widely used CLIP model. Furthermore, BACL sets new state-of-the-art benchmarks across four large-scale datasets, demonstrating its effectiveness without the need for extra labeling. This development is crucial for enhancing the performance of AI systems in tasks requiring multimodal understanding, paving the way for more accurate and efficient models.