Pith. sign in

REVIEW

Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1604.01729 v2 pith:WSFZW4ZN submitted 2016-04-06 cs.CL cs.CV

classification cs.CLcs.CV
keywords descriptionlargetextcorporaimprovingknowledgelanguagelinguistic
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality.

Discussion (0). Sign in to comment.

Pith tools