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We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations.
DO CT OR OF, P.E.: MACHINE PERCEPTION OF THREE-DIMENSIONAL, SO LIDS. Ph.D. thesis, MASSACHUSETTS INSTITUTE OF TECHNOLOGY (1961)
1961
Earlier work this paper cites.
Minsky, M., Papert, S.: An introduction to computational geometry. Cambridge tiass., HIT 479
1969
Earlier work this paper cites.
Marr, D., Poggio, T.: Cooperative computation of stereo disparity: A cooperative algorithm is derived for extracting disparity information from stereo image pairs. Science 194
1976
Earlier work this paper cites.
Barrow, H., Tenenbaum, J., Hanson, A., Riseman, E.: Recovering intrinsic scene characteristics. Comput. vis. syst 2
1978
Earlier work this paper cites.
Harris, C., Stephens, M., et al.: A combined corner and edge detector. In: Alvey vision conference. vol. 15, pp. 10–5244. Citeseer (1988)
1988
Earlier work this paper cites.
Black, M.J., Anandan, P.: A framework for the robust estimation of optical flow. In: 1993 (4th) International Conference on Computer Vision. pp. 231–236. IEEE (1993)
1993
Earlier work this paper cites.
Wang, J.Y., Adelson, E.H.: Layered representation for motion analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. pp. 361–366. IEEE (1993)
1993
Earlier work this paper cites.
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision. vol. 2, pp. 1150–1157. Ieee (1999)
1999
Earlier work this paper cites.
Torralba, A., Oliva, A.: Depth estimation from image structure. IEEE Transactions on pattern analysis and machine intelligence 24
2002
Earlier work this paper cites.
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge university press (2003)
2003
Earlier work this paper cites.
Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. MIT press (2010)
2010
Earlier work this paper cites.
Bell, S., Bala, K., Snavely, N.: Intrinsic images in the wild. ACM Trans. on Graphics (SIGGRAPH) 33
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 2425–2433 (2015)
2015
Earlier work this paper cites.
Chen, W., Fu, Z., Yang, D., Deng, J.: Single-image depth perception in the wild. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In: CVPR (2017)
2017
Earlier work this paper cites.
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Earlier work this paper cites.
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.J., Shamma, D.A., et al.: Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision 123
2017
Earlier work this paper cites.
Acharya, M., Kafle, K., Kanan, C.: Tallyqa: Answering complex counting questions. In: AAAI (2019)
2019
Earlier work this paper cites.
Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 5356–5364 (2019)
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Zellers, R., Bisk, Y., Farhadi, A., Choi, Y.: From recognition to cognition: Visual commonsense reasoning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: CVPR (2021)
2021
Earlier work this paper cites.
Lai, Z., Purushwalkam, S., Gupta, A.: The functional correspondence problem. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 15772–15781 (2021)
2021
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: LoFTR: Detector-free local feature matching with transformers. CVPR (2021)
2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H.W., Sutton, C., Gehrmann, S., Schuh, P., Shi, K., Tsvyashchenko, S., Maynez, J., Rao, A., Barnes, P., Tay, Y., Shazeer, N., Prabhakaran, V., Reif, E., Du, N., Hutchinson, B., Pope, R., Bradbury, J., Austin, J., Isard, M., Gur-Ari, G., Yin, P., Duke, T., Levskaya, A., Ghemawat, S., Dev, S., Michalewski, H., Garcia, X., Misra, V., Robinson, K., Fedus, L., Zhou, D., Ippolito, D., Luan, D., Lim, H., Zoph, B., Spiridonov, A., Sepassi, R., Dohan, D., Agrawal, S., Omernick, M., Dai, A.M., Pillai, T.S., Pellat, M., Lewkowycz, A., Moreira, E., Child, R., Polozov, O., Lee, K., Zhou, Z., Wang, X., Saeta, B., Diaz, M., Firat, O., Catasta, M., Wei, J., Meier-Hellstern, K., Eck, D., Dean, J., Petrov, S., Fiedel, N.: Palm: Scaling language modeling with pathways (2022)
2022
Earlier work this paper cites.
Fu, X., Zhou, B., Chandratreya, I., Vondrick, C., Roth, D.: There’s a time and place for reasoning beyond the image. In: Muresan, S., Nakov, P., Villavicencio, A. (eds.) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1138–1149. Association for Computational Linguistics, Dublin, Ireland (May 2022). https://doi.org/10.18653/v1/2022.acl-long.81, https://aclanthology.org/2022.acl-long.81
2022
Cited alongside, same era.
2022
Cited alongside, same era.
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
Cited alongside, same era.
Liu, F., Emerson, G., Collier, N.: Visual spatial reasoning. Transactions of the Association for Computational Linguistics 11
2023
Later among the works it cites.
Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning (2023)
2023
Later among the works it cites.
Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning (2023)
2023
Later among the works it cites.
Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., Chen, K., Lin, D.: Mmbench: Is your multi-modal model an all-around player? (2023)
2023
Later among the works it cites.
2023
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alphaXiv is searching for related work…
2022
Cited alongside, same era.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., Zhou, D.: Chain-of-thought prompting elicits reasoning in large language models (2022)
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
Cited alongside, same era.
Ze, Y., Wang, X.: Category-level 6d object pose estimation in the wild: A semi-supervised learning approach and a new dataset. Advances in Neural Information Processing Systems 35
2022
Cited alongside, same era.
2023
Cited alongside, same era.
Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J.: Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Careaga, C., Aksoy, Y.: Intrinsic image decomposition via ordinal shading. ACM Trans. Graph. (2023)
2023
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
OpenAI: Gpt-4 technical report (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Team, I.: Internlm: A multilingual language model with progressively enhanced capabilities. https://github.com/InternLM/InternLM (2023)
2023
Later among the works it cites.
Team, M.N.: Introducing mpt-7b: A new standard for open-source, commercially usable llms (2023), www.mosaicml.com/blog/mpt-7b , accessed: 2023-05-05
2023
Later among the works it cites.
2023
Later among the works it cites.
Wang, W., Lv, Q., Yu, W., Hong, W., Qi, J., Wang, Y., Ji, J., Yang, Z., Zhao, L., Song, X., Xu, J., Xu, B., Li, J., Dong, Y., Ding, M., Tang, J.: Cogvlm: Visual expert for pretrained language models (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Introducing the next generation of claude. https://www.anthropic.com/news/claude-3-family (March 2024)
2024
Closest in time.
2024
Closest in time.
Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., Lee, Y.J.: Llava-next: Improved reasoning, ocr, and world knowledge (January 2024), https://llava-vl.github.io/blog/2024-01-30-llava-next/
2024
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Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. Advances in neural information processing systems 36
2024
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Liu, Y., Li, Z., Yang, B., Li, C., Yin, X., lin Liu, C., Jin, L., Bai, X.: On the hidden mystery of ocr in large multimodal models (2024)
2024
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2024
Closest in time.
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Unleashing the power of large-scale unlabeled data. In: CVPR (2024)
2024
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Zheng, L., Chiang, W.L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al.: Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems 36
2024
Closest in time.