VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models

Referitgame: Referring to objects in photographs of natural scenes

Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. 2014 · 2014

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Microsoft coco: Common objects in context

Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014

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Fast r-cnn

Original

R Girshick. 2015 · 2015

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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models

Bryan A. Plummer, Liwei Wang, Chris M. Cervantes, Juan C. Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. 2015 · 2015

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Cider: Consensus-based image description evaluation

Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. 2015 · 2015

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A diagram is worth a dozen images

Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, and Ali Farhadi. 2016 · 2016

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Coco-text: Dataset and benchmark for text detection and recognition in natural images

Original

Andreas Veit, Tomas Matera, Lukas Neumann, Jiri Matas, and Serge Belongie. 2016 · 2016

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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning

Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017 · 2017

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Nocaps: Novel object captioning at scale

Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, and Peter Anderson. 2019 · 2019

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LVIS: A dataset for large vocabulary instance segmentation

Agrim Gupta, Piotr Dollar, and Ross Girshick. 2019 · 2019

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Gqa: A new dataset for real-world visual reasoning and compositional question answering

Drew A Hudson and Christopher D Manning. 2019 · 2019

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Clevr-ref+: Diagnosing visual reasoning with referring expressions

Runtao Liu, Chenxi Liu, Yutong Bai, and Alan L Yuille. 2019 · 2019

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Towards vqa models that can read

Amanpreet Singh, Vivek Natarajan, Meet Shah, Yu Jiang, Xinlei Chen, Dhruv Batra, Devi Parikh, and Marcus Rohrbach. 2019 · 2019

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Docvqa: A dataset for vqa on document images

Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. 2021 · 2021

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Learning transferable visual models from natural language supervision

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021 · 2021

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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs

Original

Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. 2021 · 2021

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Flamingo: a visual language model for few-shot learning

Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al. 2022 · 2022

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Flashattention: Fast and memory-efficient exact attention with io-awareness

Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022

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Large language models are zero-shot reasoners

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022

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Chartqa: A benchmark for question answering about charts with visual and logical reasoning

Original

Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque. 2022 · 2022

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Winoground: Probing vision and language models for visio-linguistic compositionality

Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela, and Candace Ross. 2022 · 2022

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Self-consistency improves chain of thought reasoning in language models

Original

Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022

Cited alongside, same era.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022

Cited alongside, same era.

Automatic chain of thought prompting in large language models

Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi

Original

Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al. 2023 · 2023

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Svit: Scaling up visual instruction tuning

Original

Bo Zhao, Boya Wu, and Tiejun Huang. 2023 · 2023

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Minigpt-4: Enhancing vision-language understanding with advanced large language models

Original

Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023

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Graph of thoughts: Solving elaborate problems with large language models

Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyczyk, et al. 2024 · 2024

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