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We introduce CogVLM, a powerful open-source visual language foundation model.
Referitgame: Referring to objects in photographs of natural scenes
Kazemzadeh, S., Ordonez, V., Matten, M., and Berg, T · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Vqa: Visual question answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., and Parikh, D · 2015
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An improved non-monotonic transition system for dependency parsing
Honnibal, M. and Johnson, M · 2015
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Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., and Lazebnik, S · 2015
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Generation and comprehension of unambiguous object descriptions
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A. L., and Murphy, K · 2016
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Modeling context in referring expressions
Yu, L., Poirson, P., Yang, S., Berg, A. C., and Berg, T. L · 2016
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Visual7w: Grounded question answering in images
Zhu, Y., Groth, O., Bernstein, M., and Fei-Fei, L · 2016
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An analysis of visual question answering algorithms
Kafle, K. and Kanan, C · 2017
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Openimages: A public dataset for large-scale multi-label and multi-class image classification
Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Veit, A., et al · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al · 2017
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Referring expression generation and comprehension via attributes
Liu, J., Wang, L., and Yang, M.-H · 2017
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Nocaps: Novel object captioning at scale
Agrawal, H., Desai, K., Wang, Y., Chen, X., Jain, R., Johnson, M., Batra, D., Parikh, D., Lee, S., and Anderson, P · 2019
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Murel: Multimodal relational reasoning for visual question answering
Cadene, R., Ben-Younes, H., Cord, M., and Thome, N · 2019
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Ok-vqa: A visual question answering benchmark requiring external knowledge
Marino, K., Rastegari, M., Farhadi, A., and Mottaghi, R · 2019
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Ocr-vqa: Visual question answering by reading text in images
Mishra, A., Shekhar, S., Singh, A. K., and Chakraborty, A · 2019
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Answer them all! toward universal visual question answering models
Shrestha, R., Kafle, K., and Kanan, C · 2019
Earlier work this paper cites.
Towards vqa models that can read
Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., and Rohrbach, M · 2019
Cited alongside, same era.
Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Glu variants improve transformer
Shazeer, N · 2020
Cited alongside, same era.
Textcaps: a dataset for image captioning with reading comprehension
Sidorov, O., Hu, R., Rohrbach, M., and Singh, A · 2020
Cited alongside, same era.
Openflamingo: An open-source framework for training large autoregressive vision-language models
Awadalla, A., Gao, I., Gardner, J., Hessel, J., Hanafy, Y., Zhu, W., Marathe, K., Bitton, Y., Gadre, S., Sagawa, S., Jitsev, J., Kornblith, S., Koh, P. W., Ilharco, G., Wortsman, M., and Schmidt, L · 2023
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Qwen-vl: A frontier large vision-language model with versatile abilities
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., and Zhou, J · 2023
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Introducing our multimodal models, 2023
Bavishi, R., Elsen, E., Hawthorne, C., Nye, M., Odena, A., Somani, A., and Taşırlar, S · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., et al · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Dai, W., Li, J., Li, D., Tiong, A. M. H., Zhao, J., Wang, W., Li, B., Fung, P., and Hoi, S · 2023
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Cornia, M., Baraldi, L., Fiameni, G., and Cucchiara, R · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
Multimodal few-shot learning with frozen language models
Tsimpoukelli, M., Menick, J. L., Cabi, S., Eslami, S., Vinyals, O., and Hill, F · 2021
Cited alongside, same era.
Simvlm: Simple visual language model pretraining with weak supervision
Wang, Z., Yu, J., Yu, A. W., Dai, Z., Tsvetkov, Y., and Cao, Y · 2021
Cited alongside, same era.
Vinvl: Revisiting visual representations in vision-language models
Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., and Gao, J · 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
Cited alongside, same era.
Coyo-700m: Image-text pair dataset
Byeon, M., Park, B., Kim, H., Lee, S., Baek, W., and Kim, S · 2022
Cited alongside, same era.
Closest in time.
Dreamllm: Synergistic multimodal comprehension and creation
Dong, R., Han, C., Peng, Y., Qi, Z., Ge, Z., Yang, J., Zhao, L., Sun, J., Zhou, H., Wei, H., et al · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al · 2023
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Obelics: An open web-scale filtered dataset of interleaved image-text documents, 2023
Laurençon, H., Saulnier, L., Tronchon, L., Bekman, S., Singh, A., Lozhkov, A., Wang, T., Karamcheti, S., Rush, A. M., Kiela, D., Cord, M., and Sanh, V · 2023
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Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., and Gao, J · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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mplug-owl2: Revolutionizing multi-modal large language model with modality collaboration
Ye, Q., Xu, H., Ye, J., Yan, M., Liu, H., Qian, Q., Zhang, J., Huang, F., and Zhou, J · 2023
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Ferret: Refer and ground anything anywhere at any granularity
You, H., Zhang, H., Gan, Z., Du, X., Zhang, B., Wang, Z., Cao, L., Chang, S.-F., and Yang, Y · 2023
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Mm-vet: Evaluating large multimodal models for integrated capabilities
Yu, W., Yang, Z., Li, L., Wang, J., Lin, K., Liu, Z., Wang, X., and Wang, L · 2023
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Yue, X., Ni, Y., Zhang, K., Zheng, T., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., et al · 2023
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Llavar: Enhanced visual instruction tuning for text-rich image understanding, 2023
Zhang, Y., Zhang, R., Gu, J., Zhou, Y., Lipka, N., Yang, D., and Sun, T · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
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