GT-Vision-Lab/VQA_LSTM_CNN: papers linking to it

GitHub · 386 stars · 131 forks · Lua · created 2015 · github.com/GT-Vision-Lab/VQA_LSTM_CNN

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Research papers in the 480 tracked journals that link to GT-Vision-Lab/VQA_LSTM_CNN (https://github.com/GT-Vision-Lab/VQA_LSTM_CNN), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the GitHub repository GT-Vision-Lab/VQA_LSTM_CNN in their Crossref records, the first in 2018; most of them (1) in 2023.
By category: AI (3); the journals with the most are International Journal of Computer Vision (3).

By year#

20182023

Papers linking to GT-Vision-Lab/VQA_LSTM_CNN by publication year
Year	Papers
2018	1
2019	1
2023	1

Journals#

Journals whose papers link to GT-Vision-Lab/VQA_LSTM_CNN most
Journal	Papers
International Journal of Computer Vision	3

Papers linking to GT-Vision-Lab/VQA_LSTM_CNN, most cited first
Published	Title	Journal	Authors	Citations	Link
2019-10-11	Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization	International Journal of Computer Vision	Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das et al.	6,898	cites it
2018-09-11	Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering	International Journal of Computer Vision	Yash Goyal, Tejas Khot, Aishwarya Agrawal et al.	486	cites it
2023-08-28	Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering	International Journal of Computer Vision	Jingjing Jiang, Ziyi Liu, Nanning Zheng	22	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). GT-Vision-Lab/VQA_LSTM_CNN: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-gt-vision-lab-vqa-lstm-cnn

@misc{scholar4ai-code-github-gt-vision-lab-vqa-lstm-cnn,
  author = {{Scholar4AI}},
  title = {GT-Vision-Lab/VQA_LSTM_CNN: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-gt-vision-lab-vqa-lstm-cnn}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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