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FETA: Towards Specializing Foundational Models for Expert Task Applications., , , , , , , , , and 3 other author(s). NeurIPS, (2022)LaSO: Label-Set Operations Networks for Multi-Label Few-Shot Learning., , , , , , , and . CVPR, page 6548-6557. Computer Vision Foundation / IEEE, (2019)Delta-encoder: an effective sample synthesis method for few-shot object recognition., , , , , , , , and . NeurIPS, page 2850-2860. (2018)RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection., , , , , , , and . CVPR, page 5197-5206. Computer Vision Foundation / IEEE, (2019)Unsupervised Domain Generalization by Learning a Bridge Across Domains., , , , , , , , , and 4 other author(s). CVPR, page 5270-5280. IEEE, (2022)Multi-modal biometrics for mobile authentication., , , , , , , , , and 1 other author(s). IJCB, page 1-8. IEEE, (2014)FETA: Towards Specializing Foundation Models for Expert Task Applications., , , , , , , , , and 3 other author(s). CoRR, (2022)Scalable Detection of Spatiotemporal Encounters in Historical Movement Data., , , , and . Comput. Graph. Forum, 31 (3pt1): 915-924 (2012)Teaching Structured Vision & Language Concepts to Vision & Language Models., , , , , , , , , and . CVPR, page 2657-2668. IEEE, (2023)MAEDAY: MAE for few- and zero-shot AnomalY-Detection., , , , , , and . Comput. Vis. Image Underst., (2024)