AI RESEARCH
Task-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations
arXiv CS.CV
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ArXi:2604.03837v1 Announce Type: new Prior multi-task triplet loss methods relied on static weights to balance supervision between various types of annotation. However, static weighting requires tuning and does not account for how tasks interact when shaping a shared representation. To address this, the proposed task-guided multi-annotation triplet loss removes this dependency by selecting triplets through a mutual-information criteria that identifies triplets most informative across tasks.