[9] Chen, Z., Li, G., Wan, X.: Align, reason and learn: Enhancing medical vision-and-language pre-training with knowledge. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 5152-5161 (2022) 3
[10] Cheng, M., Sun, Y., Wang, L., Zhu, X., Yao, K., Chen, J., Song, G., Han, J., Liu, J., Ding, E., Wang, J.: Vista: Vision and scene text aggregation for cross-modal retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5184-5193 (June 2022) 3
[11] Dzabraev, M., Kalashnikov, M., Komkov, S., Petiushko, A.: Mdmmt: Multidomain multimodal transformer for video retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3354-3363 (2021) 3
[12] Gao, C., Xu, J., Zou, Y., Huang, J.B.: Drg: Dual relation graph for human-object interaction detection. In: European Conference on Computer Vision. pp. 696-712. Springer (2020) 1, 3, 7, 8
[13] Gao, C., Zou, Y., Huang, J.B.: ican: Instance-centric attention network for human-object interaction detection. arXiv preprint arXiv:1808.10437 (2018) 3
[14] Gkioxari, G., Girshick, R., Dollár, P., He, K.: Detecting and recognizing human-object interactions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8359-8367 (2018) 1, 3
[15] Gupta, S., Malik, J.: Visual semantic role labeling. arXiv preprint arXiv:1505.04474 (2015) 1, 6, 9, 10, 14, 15
[16] Hou, Z., Peng, X., Qiao, Y., Tao, D.: Visual compositional learning for human-object interaction detection. In: European Conference on Computer Vision. pp. 584-600. Springer (2020) 7, 8
[17] Hou, Z., Yu, B., Qiao, Y., Peng, X., Tao, D.: Affordance transfer learning for human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 495-504 (2021) 7, 8
[18] Hou, Z., Yu, B., Qiao, Y., Peng, X., Tao, D.: Detecting human-object interaction via fabricated compositional learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14646-14655 (2021) 1, 3, 7, 8
[19] Iftekhar, A., Chen, H., Kundu, K., Li, X., Tighe, J., Modolo, D.: What to look at and where: Semantic and spatial refined transformer for detecting human-object interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5353-5363 (2022) 2, 3, 7
[20] Kim, B., Choi, T., Kang, J., Kim, H.J.: Uniondet: Union-level detector towards real-time human-object interaction detection. In: European Conference on Computer Vision. pp. 498-514. Springer (2020) 3
[21] Kim, B., Lee, J., Kang, J., Kim, E.S., Kim, H.J.: Hotr: End-to-end human-object interaction detection with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 74-83 (2021) 8
[22] Kim, S., Jung, D., Cho, M.: Relational context learning for human-object interaction detection. arXiv preprint arXiv:2304.04997 (2023) 7
[23] Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 (2023) 5, 6, 10, 13
[24] Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.N., et al.: Grounded language-image pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10965–10975 (2022) 3
[25] Li, Y.L., Liu, X., Lu, H., Wang, S., Liu, J., Li, J., Lu, C.: Detailed 2d-3d joint representation for human-object interaction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10166–10175 (2020) 7
[26] Li, Y.L., Liu, X., Wu, X., Li, Y., Lu, C.: Hoi analysis: Integrating and decomposing human-object interaction. Advances in Neural Information Processing Systems 33, 5011-5022 (2020) 7, 8
[27] Li, Y.L., Zhou, S., Huang, X., Xu, L., Ma, Z., Fang, H.S., Wang, Y., Lu, C.: Transferable interactiveness knowledge for human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3585-3594 (2019) 1, 3, 16
[28] Li, Z., Zou, C., Zhao, Y., Li, B., Zhong, S.: Improving human-object interaction detection via phrase learning and label composition. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 1509-1517 (2022) 2, 3, 8, 13, 14
[29] Liao, Y., Liu, S., Wang, F., Chen, Y., Qian, C., Feng, J.: Ppdm: Parallel point detection and matching for real-time human-object interaction detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 482-490 (2020) 1, 3, 7