Grounded Multi-Hop VideoQA in Long-Form Egocentric Videos

Bleu: a method for automatic evaluation of machine translation

Papineni, K.; Roukos, S.; Ward, T.; and Zhu, W.-J. 2002 · 2002

Earlier work this paper cites.

Rouge: A package for automatic evaluation of summaries

Lin, C.-Y. 2004 · 2004

Earlier work this paper cites.

METEOR: An automatic metric for MT evaluation with improved correlation with human judgments

Banerjee, S.; and Lavie, A. 2005 · 2005

Earlier work this paper cites.

MovieQA: Understanding Stories in Movies through Question-Answering

Tapaswi, M.; Zhu, Y.; Stiefelhagen, R.; Torralba, A.; Urtasun, R.; and Fidler, S. 2016 · 2016

Earlier work this paper cites.

How2: A Large-scale Dataset for Multimodal Language Understanding

Sanabria, R.; Caglayan, O.; Palaskar, S.; Elliott, D.; Barrault, L.; Specia, L.; and Metze, F. 2018 · 2018

Earlier work this paper cites.

HotpotQA: A dataset for diverse, explainable multi-hop question answering

Original

Yang, Z.; Qi, P.; Zhang, S.; Bengio, Y.; Cohen, W. W.; Salakhutdinov, R.; and Manning, C. D. 2018 · 2018

Earlier work this paper cites.

Are we asking the right questions in MovieQA?

Jasani, B.; Girdhar, R.; and Ramanan, D. 2019 · 2019

Earlier work this paper cites.

HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips

Miech, A.; Zhukov, D.; Alayrac, J.-B.; Tapaswi, M.; Laptev, I.; and Sivic, J. 2019 · 2019

Earlier work this paper cites.

Sentence-bert: Sentence embeddings using siamese bert-networks

Reimers, N.; and Gurevych, I. 2019 · 2019

Earlier work this paper cites.

ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Yu, Z.; Xu, D.; Yu, J.; Yu, T.; Zhao, Z.; Zhuang, Y.; and Tao, D. 2019 · 2019

Earlier work this paper cites.

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Ho, X.; Nguyen, A.-K. D.; Sugawara, S.; and Aizawa, A. 2020 · 2020

Earlier work this paper cites.

spaCy: Industrial-strength natural language processing in python

Honnibal, M.; Montani, I.; Van Landeghem, S.; Boyd, A.; et al. 2020 · 2020

Earlier work this paper cites.

Action genome: Actions as compositions of spatio-temporal scene graphs

Ji, J.; Krishna, R.; Fei-Fei, L.; and Niebles, J. C. 2020 · 2020

Earlier work this paper cites.

AGQA: A Benchmark for Compositional Spatio-Temporal Reasoning

Grunde-McLaughlin, M.; Krishna, R.; and Agrawala, M. 2021 · 2021

Earlier work this paper cites.

STAR: A Benchmark for Situated Reasoning in Real-World Videos

Wu, B.; Yu, S.; Chen, Z.; Tenenbaum, J. B.; and Gan, C. 2021 · 2021

Earlier work this paper cites.

NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions

Xiao, J.; Shang, X.; Yao, A.; and Chua, T.-S. 2021 · 2021

Earlier work this paper cites.

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval

Xiong, W.; Li, X.; Iyer, S.; Du, J.; Lewis, P.; Wang, W. Y.; Mehdad, Y.; Yih, S.; Riedel, S.; Kiela, D.; et al. 2021 · 2021

Cited alongside, same era.

Flamingo: a visual language model for few-shot learning

Alayrac, J.-B.; Donahue, J.; Luc, P.; Miech, A.; Barr, I.; Hasson, Y.; Lenc, K.; Mensch, A.; Millican, K.; Reynolds, M.; et al. 2022 · 2022

Cited alongside, same era.

Where did i leave my keys?-episodic-memory-based question answering on egocentric videos

Bärmann, L.; and Waibel, A. 2022 · 2022

Cited alongside, same era.

Ego4D: Around the World in 3,000 Hours of Egocentric Video

Grauman, K.; Westbury, A.; Byrne, E.; Chavis, Z.; Furnari, A.; Girdhar, R.; Hamburger, J.; Jiang, H.; Liu, M.; Liu, X.; Martin, M.; Nagarajan, T.; Radosavovic, I.; Ramakrishnan, S. K.; Ryan, F.; Sharma, J.; Wray, M.; Xu, M.; Xu, E. Z.; Zhao, C.; Bansal, S.; Batra, D.; Cartillier, V.; Crane, S.; Do, T.; Doulaty, M.; Erapalli, A.; Feichtenhofer, C.; Fragomeni, A.; Fu, Q.; Gebreselasie, A.; Gonzalez, C.; Hillis, J.; Huang, X.; Huang, Y.; Jia, W.; Khoo, W.; Kolar, J.; Kottur, S.; Kumar, A.; Landini, F.; Li, C.; Li, Y.; Li, Z.; Mangalam, K.; Modhugu, R.; Munro, J.; Murrell, T.; Nishiyasu, T.; Price, W.; Puentes, P. R.; Ramazanova, M.; Sari, L.; Somasundaram, K.; Southerland, A.; Sugano, Y.; Tao, R.; Vo, M.; Wang, Y.; Wu, X.; Yagi, T.; Zhao, Z.; Zhu, Y.; Arbelaez, P.; Crandall, D.; Damen, D.; Farinella, G. M.; Fuegen, C.; Ghanem, B.; Ithapu, V. K.; Jawahar, C. V.; Joo, H.; Kitani, K.; Li, H.; Newcombe, R.; Oliva, A.; Park, H. S.; Rehg, J. M.; Sato, Y.; Shi, J.; Shou, M. Z.; Torralba, A.; Torresani, L.; Yan, M.; and Malik, J. 2022 · 2022

Llama 3 Model Card

AI@Meta. 2024 · 2024

Closest in time.

MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens

Original

Ataallah, K.; Shen, X.; Abdelrahman, E.; Sleiman, E.; Zhu, D.; Ding, J.; and Elhoseiny, M. 2024 · 2024

Closest in time.

Grounded Question-Answering in Long Egocentric Videos

Di, S.; and Xie, W. 2024 · 2024

Closest in time.

Mixtral of experts

Original

Jiang, A. Q.; Sablayrolles, A.; Roux, A.; Mensch, A.; Savary, B.; Bamford, C.; Chaplot, D. S.; Casas, D. d. l.; Hanna, E. B.; Bressand, F.; et al. 2024 · 2024

Closest in time.

Lisa: Reasoning segmentation via large language model

Lai, X.; Tian, Z.; Chen, Y.; Li, Y.; Yuan, Y.; Liu, S.; and Jia, J. 2024 · 2024

Closest in time.