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Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
[article]
2022
arXiv
pre-print
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-to-text decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack
arXiv:2205.10747v4
fatcat:o5llpuzsirezhiccqcffac7m5a