SOURCE-LINKED INTELLIGENCE
TeMo: Temperature Modulation for Multimodal Contrastive Learning
Contrastive learning approaches achieve strong performance by training models to bring similar samples closer while pushing dissimilar samples apart. A crucial component of contrastive learning is the temperature hyperparameter $τ$, which controls the penalty strength applied to negative samples. However, most existing methods either fix this hyperparameter or learn a global value during training. In this paper, we introduce TeMo, Temperature Modulation framework, a similarity-based modulation approach that adaptively adjusts the temperature for each positive-negative pair according to their s
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- arXiv · AI, language, vision and robotics · 2026-09-07T14:23:02.000Z
First collected: 2026-09-20T20:32:20.942Z. This is not the publication date.