Contextual Object Detection with Multimodal Large Language Models

Author:

Zang YuhangORCID,Li Wei,Han Jun,Zhou Kaiyang,Loy Chen Change

Publisher

Springer Science and Business Media LLC

Reference87 articles.

1. Alayrac, J. B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., & Simonyan, K. (2022). Flamingo: a visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35, 23716–23736.

2. Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018). Bottom-up and top-down attention for image captioning and visual question answering. In Proceedings of the IEEE conference on computer vision and pattern recognition.

3. Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, CL., & Parikh, D. (2015). VQA: Visual question answering. In Proceedings of the IEEE international conference on computer vision.

4. Bansal, A., Sikka, K., Sharma, G., Chellappa, R., & Divakaran, A. (2018). Zero-shot object detection. In Proceedings of the European conference on computer vision (ECCV).

5. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & Agarwal, S. (2020). Language models are few-shot learners. In Advances in neural information processing systems.

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