From c55c57b325490be1eb8c8748c2c44c1fbe9d82db Mon Sep 17 00:00:00 2001 From: Adnen Abdessaied Date: Wed, 14 Jun 2023 21:52:52 +0200 Subject: [PATCH] Update 'README.md' --- README.md | 36 ++++++++++++++++++++++-------------- 1 file changed, 22 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 90d7061..6626f2b 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,25 @@ -# VLCN -This repository contains the official code of the paper: +
+

Video Language Co-Attention with Fast-Learning Feature Fusion for VideoQA

-**Video Language Co-Attention with Fast-Learning Feature Fusion for VideoQA** [[PDF](https://aclanthology.org/2022.repl4nlp-1.15.pdf)] -[Adnen Abdessaied](https://adnenabdessaied.de), [Ekta Sood](https://perceptualui.org/people/sood/), [Andreas Bulling](https://perceptualui.org/people/bulling/) -**Poster** -Representation Learning for NLP (RepL4NLP) @ ACL 2022 / Dublin, Ireland. +**[Adnen Abdessaied][1],   [Ekta Sood][2],   [Andreas Bulling][3]**
+**Published at [Relp4NLP @ ACL 2022][4] 🇮🇪 [[Paper][5]]**
+
+ +# Citation If you find our code useful or use it in your own projects, please cite our paper: -``` -@inproceedings{abdessaied22_repl4NLP, - author = {Abdessaied, Adnen and Sood, Ekta and Bulling, Andreas}, - title = {Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for VideoQA}, - booktitle = {Proc. of the 7th Workshop on Representation Learning for NLP (RepL4NLP) @ ACL2022}, - year = {2022}, - pages = {1--12} +```bibtex +@inproceedings{abdessaied-etal-2022-video, + title = "Video Language Co-Attention with Multimodal Fast-Learning Feature Fusion for {V}ideo{QA}", + author = "Abdessaied, Adnen and Sood, Ekta and Bulling, Andreas", + booktitle = "Proceedings of the 7th Workshop on Representation Learning for NLP", + year = "2022", + url = "https://aclanthology.org/2022.repl4nlp-1.15", + pages = "143--155", } ``` + # Abstract We propose the Video Language CoAttention Network (VLCN) – a novel memory-enhanced model for Video Question Answering (VideoQA). Our model combines two original contributions: A multimodal fast-learning feature fusion (FLF) block and a mechanism that uses selfattended language features to separately guide neural attention on both static and dynamic visual features extracted from individual video frames and short video clips. When trained from scratch, VLCN achieves competitive results with the state of the art on both MSVD-QA and MSRVTT-QA with 38.06% and 36.01% test accuracies, respectively. Through an ablation study, we further show that FLF improves generalization across different VideoQA datasets and performance for question types that are notoriously challenging in current datasets, such as long questions that require deeper reasoning as well as questions with rare answers @@ -145,7 +148,12 @@ We thank the Vision and Language Group@ MIL for their [MCAN](https://github.com/ # Contributors -- [Adnen Abdessaied](https://adnenabdessaied.de) +- [Adnen Abdessaied][1] For any questions or enquiries, don't not hesitate to contact the above contributor. +[1]: https://adnenabdessaied.de +[2]: https://perceptualui.org/people/sood/ +[3]: https://perceptualui.org/people/bulling/ +[4]: https://sites.google.com/view/repl4nlp2022/ +[5]: https://aclanthology.org/2022.repl4nlp-1.15.pdf