3 papers
cs.CV2026
Beyond Task-Driven Features for Object Detection
Meilun Zhou, Alina Zare
Task-driven features learned by modern object detectors optimize end task loss yet often capture shortcut correlations that fail to reflect underlying annotation structure. Such re…
cs.CV2026
Task-Guided Multi-Annotation Triplet Learning for Remote Sensing Representations
Meilun Zhou, Alina Zare
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 ac…
cs.CV2025
Multi-Task Learning with Multi-Annotation Triplet Loss for Improved Object Detection
Meilun Zhou, Aditya Dutt, Alina Zare
Triplet loss traditionally relies only on class labels and does not use all available information in multi-task scenarios where multiple types of annotations are available. This pa…