Papers with video question answering datasets

2 papers
Contrastive Video-Language Learning with Fine-grained Frame Sampling (2022.aacl-main)

Copied to clipboard

Challenge: despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck.
Approach: They propose a fine-grained contrastive objective for video frame sampling to improve cross-modal correspondence.
Outcome: The proposed approach achieves state-of-the-art performance on YouCookII with long videos.
LifeQA: A Real-life Dataset for Video Question Answering (2020.lrec-1)

Copied to clipboard

Challenge: Existing video question answering datasets consist of movies and TV shows, but they are not representative of our day-to-day lives.
Approach: They propose a benchmark dataset for video question answering that focuses on day-to-day situations.
Outcome: The proposed dataset analyzes the challenging but realistic aspects of LifeQA . it consists of video clips and over 2.3k multiple-choice questions .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations