Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks. Features are learned via prediction of grounded relationships between visual content and automatic speech recognition (ASR) tokens. However, prior pretraining work has been limited to only instructional videos , a domain that, a priori, we expect  to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted. Because instructional videos make up only a fraction of the web's diverse video content, we ask: can similar models be trained on broader corpora? And, if so, what types of videos are "grounded" and what types are not? We examine the diverse YouTube8M corpus, first verifying that it contains many non-instructional videos via crowd labeling. We pretrain a representative model on YouTube8M and study its success and failure cases. We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set still results in representations that generalize to both non-instructional and instructional domains.
Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks. Features are learned via prediction of grounded relationships between visual content and automatic speech recognition (ASR) tokens. However, prior pretraining work has been limited to only instructional videos ; a priori, we expect this domain to be relatively "easy:" speakers in instructional videos will often reference the literal objects/actions being depicted. We ask: can similar models be trained on more diverse video corpora? And, if so, what types of videos are "grounded" and what types are not? We fit a representative pretraining model to the diverse YouTube8M dataset, and study its success and failure cases. We find that visual-textual grounding is indeed possible across previously unexplored video categories, and that pretraining on a more diverse set results in representations that generalize to both non-instructional and instructional domains.
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Because instructional videos make up only a fraction of the web's diverse video content, we
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fit a representative pretraining model to the diverse
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corpus, first verifying that it contains many non-instructional videos via crowd labeling. We pretrain a representative model on YouTube8M
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