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Cross-Modal Fine-Tuning: Align then Refine
Junhong Shen · Liam Li · Lucio Dery · Corey Staten · Mikhail Khodak · Graham Neubig · Ameet Talwalkar

Thu Jul 27 06:32 PM -- 06:40 PM (PDT) @ Ballroom A

Fine-tuning large-scale pretrained models has led to tremendous progress in well-studied modalities such as vision and NLP. However, similar gains have not been observed in many other modalities due to a lack of relevant pretrained models. In this work, we propose ORCA, a general cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA adapts to a target task via an align-then-refine workflow: given the target input, ORCA first learns an embedding network that aligns the embedded feature distribution with the pretraining modality. The pretrained model is then fine-tuned on the embedded data to exploit the knowledge shared across modalities. Through extensive experiments, we show that ORCA obtains state-of-the-art results on 3 benchmarks containing over 60 datasets from 12 modalities, outperforming a wide range of hand-designed, AutoML, general-purpose, and task-specific cross-modal methods. We highlight the importance of data alignment via a series of ablation studies and exemplify ORCA's utility in data-limited regimes.

Author Information

Junhong Shen (Carnegie Mellon University)
Liam Li (Hewlett Packard Enterprise)
Lucio Dery (Carnegie Mellon University)
Corey Staten (Ohio State University, Columbus)
Mikhail Khodak (CMU)
Graham Neubig (Carnegie Mellon University)
Ameet Talwalkar (Carnegie Mellon University)

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