[38] Wufei Ma, Angtian Wang, Alan Yuille, and Adam Kortylewski. Robust category-level 6d pose estimation with coarse-to-fine rendering of neural features. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part IX, pages 492-508. Springer, 2022.
[39] Xiaojian Ma, Silong Yong, Zilong Zheng, Qing Li, Yitao Liang, Song-Chun Zhu, and Siyuan Huang. Sqa3d: Situated question answering in 3d scenes. In International Conference on Learning Representations, 2023.
[40] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu. The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision. In International Conference on Learning Representations, 2019.
[41] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12700-12710, June 2021.
[42] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xiansheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12695-12705, 2021.
[43] Songyou Peng, Kyle Genova, Chiyu "Max" Jiang, Andrea Tagliasacchi, Marc Pollefeys, and Thomas Funkhouser. Openscene: 3d scene understanding with open vocabularies. In CVPR, 2023.
[44] Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville. Film: Visual reasoning with a general conditioning layer. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
[45] Mengye Ren, Ryan Kiros, and Richard Zemel. Exploring models and data for image question answering. Advances in neural information processing systems, 28, 2015.
[46] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
[47] Leonard Salewski, A Koepke, Hendrik Lensch, and Zeynep Akata. Clevr-x: A visual reasoning dataset for natural language explanations. In International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, pages 69-88. Springer, 2022.
[48] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019.
[49] Angtian Wang, Adam Kortylewski, and Alan Yuille. Nemo: Neural mesh models of contrastive features for robust 3d pose estimation. In International Conference on Learning Representations, 2021.
[50] Yu Xiang, Roozbeh Mottaghi, and Silvio Savarese. Beyond Pascal: A benchmark for 3d object detection in the wild. In IEEE winter conference on applications of computer vision, pages 75-82. IEEE, 2014.
[51] Xu Yan, Zhihao Yuan, Yuhao Du, Yinghong Liao, Yao Guo, Zhen Li, and Shuguang Cui. Clevr3d: Compositional language and elementary visual reasoning for question answering in 3d real-world scenes. arXiv preprint arXiv:2112.11691, 2021.
[52] Shuquan Ye, Dongdong Chen, Songfang Han, and Jing Liao. 3d question answering. IEEE Transactions on Visualization and Computer Graphics, 2022.
[53] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. Clevrer: Collision events for video representation and reasoning. arXiv preprint arXiv:1910.01442, 2019.
[54] Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B Tenenbaum. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. In Advances in Neural Information Processing Systems (NIPS), 2018.
[55] Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. Deep modular co-attention networks for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6281–6290, 2019.
[56] Xiaoding Yuan, Adam Kortylewski, Yihong Sun, and Alan Yuille. Robust instance segmentation through reasoning about multi-object occlusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11141-11150, 2021.