Search Results for author:
Found 5 papers, 4 papers with code
H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences
We describe an efficient hierarchical method to compute attention in the Transformer architecture.
Ranked #1 on
Language Modelling
on One Billion Word
(Validation perplexity metric)
Multimodal Pretraining for Dense Video Captioning
First, we construct and release a new dense video captioning dataset, Video Timeline Tags (ViTT), featuring a variety of instructional videos together with time-stamped annotations.
Ranked #1 on
Dense Video Captioning
on YouCook2
(ROUGE-L metric, using extra
training data)
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube
Pretraining from unlabelled web videos has quickly become the de-facto means of achieving high performance on many video understanding tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions
Instructional videos get high-traffic on video sharing platforms, and prior work suggests that providing time-stamped, subtask annotations (e. g., "heat the oil in the pan") improves user experiences.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Finally, we show that using our PG method we can optimize any of the metrics, including the proposed SPIDEr metric which results in image captions that are strongly preferred by human raters compared to captions generated by the same model but trained to optimize MLE or the COCO metrics.
