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arXiv · 2511.08399

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

Hua Ye·Hang Ding·Siyuan Chen·Yiyang Jiang·Changyuan Zhang·Xuan Zhang
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Abstract

Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum with Local Attention (BACL), a lightweight add-on that turns these borderline cases into a curriculum signal. A Boundary-aware Negative Sampler gradually raises difficulty, while a Contrastive Local Attention loss highlights where the mismatch occurs. The two modules are fully differentiable and work with any off-the-shelf dual encoder. Theory predicts a fast O(1/n) error rate; practice shows up to +32% R@1 over CLIP and new SOTA on four large-scale benchmarks, all without extra labels.

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Hua Ye, Hang Ding, Siyuan Chen, Yiyang Jiang, Changyuan Zhang, Xuan Zhang. 2026-01-13. Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment. https://arxiv.org/abs/2511.08399

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