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Recent advances in visual reasoning (VR), particularly with the aid of Large Vision-Language Models (VLMs), show promise but require access to large-scale datasets and face challenges such as high computational costs and limited generalization capabilities.
2013
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Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L.: Modeling context in referring expressions. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. pp. 69–85. Springer (2016)
2016
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Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L.: Modeling context in referring expressions. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. pp. 69–85. Springer (2016)
2016
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Hudson, D.A., Manning, C.D.: Gqa: A new dataset for real-world visual reasoning and compositional question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6700–6709 (2019)
2019
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Marino, K., Rastegari, M., Farhadi, A., Mottaghi, R.: Ok-vqa: A visual question answering benchmark requiring external knowledge. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. p. 3195–3204 (2019), https://openaccess.thecvf.com/content_CVPR_2019/html/Marino_OK-VQA_A_Visual_Question_Answering_Benchmark_Requiring_External_Knowledge_CVPR_2019_paper.html
2019
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Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. vol. 32. Curran Associates, Inc. (2019), https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html
2019
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2019
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Zellers, R., Bisk, Y., Farhadi, A., Choi, Y.: From recognition to cognition: Visual commonsense reasoning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6720–6731 (2019)
2019
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Amizadeh, S., Palangi, H., Polozov, A., Huang, Y., Koishida, K.: Neuro-symbolic visual reasoning: Disentangling. In: International Conference on Machine Learning. pp. 279–290. PMLR (2020)
2020
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
2020
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2020
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2021
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Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al.: Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems 35
2022
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2022
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2022
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Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners. Advances in neural information processing systems 35
2022
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2022
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2022
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Schwenk, D., Khandelwal, A., Clark, C., Marino, K., Mottaghi, R.: A-okvqa: A benchmark for visual question answering using world knowledge. In: European Conference on Computer Vision. pp. 146–162. Springer (2022)
2022
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Subramanian, S., Merrill, W., Darrell, T., Gardner, M., Singh, S., Rohrbach, A.: ReCLIP: A strong zero-shot baseline for referring expression comprehension. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 5198–5215 (May 2022). https://doi.org/10.18653/v1/2022.acl-long.357
2022
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Tiong, A.M.H., Li, J., Li, B., Savarese, S., Hoi, S.C.: Plug-and-play VQA: Zero-shot VQA by conjoining large pretrained models with zero training. In: Findings of the Association for Computational Linguistics: EMNLP 2022. pp. 951–967 (Dec 2022). https://doi.org/10.18653/v1/2022.findings-emnlp.67
2022
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Sur’is, D., Menon, S., Vondrick, C.: Vipergpt: Visual inference via python execution for reasoning. 2023 IEEE/CVF International Conference on Computer Vision (ICCV) pp. 11854–11864 (2023), https://api.semanticscholar.org/CorpusID:257505358
2023
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2023
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2023
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2023
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2022
Cited alongside, same era.
Yang, Z., Gan, Z., Wang, J., Hu, X., Lu, Y., Liu, Z., Wang, L.: An empirical study of gpt-3 for few-shot knowledge-based vqa. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 3081–3089 (2022)
2022
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Yuksekgonul, M., Bianchi, F., Kalluri, P., Jurafsky, D., Zou, J.: When and why vision-language models behave like bags-of-words, and what to do about it? In: The Eleventh International Conference on Learning Representations (2022)
2022
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Zeng, Y., Zhang, X., Li, H.: Multi-grained vision language pre-training: Aligning texts with visual concepts. In: International Conference on Machine Learning. pp. 25994–26009. PMLR (2022)
2022
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Bugliarello, E., Sartran, L., Agrawal, A., Hendricks, L.A., Nematzadeh, A.: Measuring progress in fine-grained vision-and-language understanding. In: Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1559–1582. Association for Computational Linguistics, Toronto, Canada (Jul 2023). https://doi.org/10.18653/v1/2023.acl-long.87, https://aclanthology.org/2023.acl-long.87
2023
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2023
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2023
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Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P., Hoi, S.: Instructblip: Towards general-purpose vision-language models with instruction tuning (2023)
2023
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Gupta, T., Kembhavi, A.: Visual programming: Compositional visual reasoning without training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14953–14962 (2023)
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2023
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2023
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Hsieh, C.Y., Zhang, J., Ma, Z., Kembhavi, A., Krishna, R.: Sugarcrepe: Fixing hackable benchmarks for vision-language compositionality. Advances in Neural Information Processing Systems 36
2024
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2024
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2024
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