LifeQA: A Real-life Dataset for Video Question Answering

By Santiago Castro, Mahmoud Azab, Jonathan C. Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, Rada Mihalcea. 2020

44 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks, Question Answering & Reading Comprehension

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

1. Jitendra Malik
2. Devshree Patel
3. Ratnam Parikh
4. Yesha Shastri
5. Alexei A. Efros
6. Luc Van Gool
7. Martial Hebert
8. Radu Timofte

In Question Answering & Reading Comprehension

1. Devshree Patel
2. Ratnam Parikh
3. Yesha Shastri

Citations in the corpus

• Just Ask: Learning to Answer Questions from Millions of Narrated Videos (2020, neutral): “As result, current VideoQA datasets [11, 16, 17, 21, 27, 33, 38, 43, 46, 60, 69, 74, 84, 90, 91, 92, 93] are limited in size, as the largest, TGIF-QA [33], contains only 72K annotated clips (see Figure 2 for more details).”
• Recent Advances in Video Question Answering: A Review of Datasets and Methods (2021, background): “LifeQA [33] 275 2328 Manual Daily life videos Multiple-choice”
• EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding (2023, background): “…[15], complex web video understanding [63], situated reasoning [50], spatiotemporal reasoning [28], social intelligence [65], dynamic neuro-symbolic reasoning [62], external knowledge-based reasoning [16] and many more [37, 61, 42, 10, 9, 13, 45, 56, 30, 31, 8, 64, 11, 32, 59, 66, 52, 24].”
• Zero-Shot Video Question Answering via Frozen Bidirectional Language Models (2022, unknown): “[10] Santiago Castro, Mahmoud Azab, Jonathan Stroud, Cristina Noujaim, Ruoyao Wang, Jia Deng, and Rada Mihalcea.”