[1] J. Bai, S. Bai, S. Yang, S. Wang, S. Tan, P. Wang, J. Lin, C. Zhou, and J. Zhou. Qwen-vl: A frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966, 2023.
[2] L. Cai, Z. Zhang, Y. Zhu, L. Zhang, M. Li, and X. Xue. Bigdetection: A large-scale benchmark for improved object detector pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4777-4787, 2022.
[3] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko. End-to-end object detection with transformers. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16, pages 213-229. Springer, 2020.
[4] K. Chen, Z. Zhang, W. Zeng, R. Zhang, F. Zhu, and R. Zhao. Shikra: Unleashing multimodal llm's referential dialogue magic. arXiv preprint arXiv:2306.15195, 2023.
[5] Y.-C. Chen, L. Li, L. Yu, A. El Kholy, F. Ahmed, Z. Gan, Y. Cheng, and J. Liu. Uniter: Universal image-text representation learning. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXX, pages 104–120. Springer, 2020.
[6] Z. Chen, P. Wang, L. Ma, K.-Y. K. Wong, and Q. Wu. Cops-ref: A new dataset and task on compositional referring expression comprehension. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10086-10095, 2020.
[7] J. Deng, Z. Yang, T. Chen, W. Zhou, and H. Li. Transvg: End-to-end visual grounding with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1769-1779, 2021.
[8] Z.-Y. Dou, A. Kamath, Z. Gan, P. Zhang, J. Wang, L. Li, Z. Liu, C. Liu, Y. LeCun, N. Peng, et al. Coarse-to-fine vision-language pre-training with fusion in the backbone. Advances in neural information processing systems, 35:32942-32956, 2022.
[9] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 2010.
[10] G. Ghiasi, X. Gu, Y. Cui, and T.-Y. Lin. Scaling open-vocabulary image segmentation with image-level labels. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXVI, pages 540-557. Springer, 2022.
[11] X. Gu, T.-Y. Lin, W. Kuo, and Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In International Conference on Learning Representations, 2022.
[12] A. Gupta, P. Dollar, and R. Girshick. Lvis: A dataset for large vocabulary instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5356-5364, 2019.
[13] R. Hu, M. Rohrbach, J. Andreas, T. Darrell, and K. Saenko. Modeling relationships in referential expressions with compositional modular networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1115-1124, 2017.
[14] A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021.
[15] S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg. Referitgame: Referring to objects in photographs of natural scenes. In EMNLP, pages 787-798, 2014.
[16] I. Krasin, T. Lin, T. Duerig, P. Krahenbihl, A. Gupta, C. Burgess, and V. Ferrari. OpenImages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://github.com/openimages, 2017.