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This paper presents a novel hierarchical alignment model (HAM) that learns multi-granularity visual and linguistic representations in an end-to-end manner.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 1907–1915
1915
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P. Wang, Q. Wu, J. Cao, C. Shen, L. Gao, and A. v. d. Hengel, “Neighbourhood watch: Referring expression comprehension via language-guided graph attention networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 1960–1968
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P. Young, A. Lai, M. Hodosh, and J. Hockenmaier, “From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions,” Transactions of the Association for Computational Linguistics , vol. 2, pp. 67–78, 2014
2014
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S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg, “Referitgame: Referring to objects in photographs of natural scenes,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , 2014, pp. 787–798
2014
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J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) , 2014, pp. 1532–1543
2014
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2014
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S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
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B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2641–2649
2015
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S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 567–576
2015
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J. Mao, J. Huang, A. Toshev, O. Camburu, A. L. Yuille, and K. Murphy, “Generation and comprehension of unambiguous object descriptions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 11–20
2016
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L. Yu, P. Poirson, S. Yang, A. C. Berg, and T. L. Berg, “Modeling context in referring expressions,” in European Conference on Computer Vision . Springer, 2016, pp. 69–85
2016
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S. Song and J. Xiao, “Deep sliding shapes for amodal 3d object detection in rgb-d images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 808–816
2016
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I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3d semantic parsing of large-scale indoor spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1534–1543
2016
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2016
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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5828–5839
2017
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
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Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “Making the v in vqa matter: Elevating the role of image understanding in visual question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 6904–6913
2017
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A. Das, S. Kottur, K. Gupta, A. Singh, D. Yadav, J. M. Moura, D. Parikh, and D. Batra, “Visual dialog,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 326–335
2017
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2017
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2018
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A. Das, S. Datta, G. Gkioxari, S. Lee, D. Parikh, and D. Batra, “Embodied question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1–10
2018
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L. Yu, Z. Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and T. L. Berg, “Mattnet: Modular attention network for referring expression comprehension,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 1307–1315
2018
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J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander, “Joint 3d proposal generation and object detection from view aggregation,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 1–8
2018
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M. Liang, B. Yang, S. Wang, and R. Urtasun, “Deep continuous fusion for multi-sensor 3d object detection,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 641–656
2018
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B. Yang, W. Luo, and R. Urtasun, “Pixor: Real-time 3d object detection from point clouds,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2018, pp. 7652–7660
2018
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B. Xu and Z. Chen, “Multi-level fusion based 3d object detection from monocular images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2345–2353
2018
Cited alongside, same era.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4490–4499
2018
Cited alongside, same era.
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas, “Frustum pointnets for 3d object detection from rgb-d data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 918–927
2018
Cited alongside, same era.
2018
Cited alongside, same era.
D. Z. Chen, Q. Wu, M. Nießner, and A. X. Chang, “D3net: A speaker-listener architecture for semi-supervised dense captioning and visual grounding in rgb-d scans,” 2021
2021
Later among the works it cites.
Z. Liu, Z. Zhang, Y. Cao, H. Hu, and X. Tong, “Group-free 3d object detection via transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2949–2958
2021
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Z. Chen, A. Gholami, M. Nießner, and A. X. Chang, “Scan2cap: Context-aware dense captioning in rgb-d scans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3193–3203
2021
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 8821–8831
2021
Later among the works it cites.
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C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” in proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9277–9286
2019
Cited alongside, same era.
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9404–9413
2019
Cited alongside, same era.
W. Zhang, B. Wang, L. Ma, and W. Liu, “Reconstruct and represent video contents for captioning via reinforcement learning,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 12, pp. 3088–3101, 2019
2019
Cited alongside, same era.
Z. Yang, B. Gong, L. Wang, W. Huang, D. Yu, and J. Luo, “A fast and accurate one-stage approach to visual grounding,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4683–4693
2019
Cited alongside, same era.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 770–779
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Z. Chen, A. X. Chang, and M. Nießner, “Scanrefer: 3d object localization in rgb-d scans using natural language,” in European Conference on Computer Vision . Springer, 2020, pp. 202–221
2020
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2021
2021
Later among the works it cites.
B. Huang, D. Lian, W. Luo, and S. Gao, “Look before you leap: Learning landmark features for one-stage visual grounding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 888–16 897
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 8748–8763
2021
Later among the works it cites.
D. He, Y. Zhao, J. Luo, T. Hui, S. Huang, A. Zhang, and S. Liu, “Transrefer3d: Entity-and-relation aware transformer for fine-grained 3d visual grounding,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 2344–2352
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Kim, B. Son, and I. Kim, “Vilt: Vision-and-language transformer without convolution or region supervision,” in International Conference on Machine Learning . PMLR, 2021, pp. 5583–5594
2021
Later among the works it cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 10 012–10 022
2021
Later among the works it cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Cai, L. Zhao, J. Zhang, L. Sheng, and D. Xu, “3djcg: A unified framework for joint dense captioning and visual grounding on 3d point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 464–16 473
2022
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J. Luo, J. Fu, X. Kong, C. Gao, H. Ren, H. Shen, H. Xia, and S. Liu, “3d-sps: Single-stage 3d visual grounding via referred point progressive selection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 454–16 463
2022
Closest in time.
A. Jain, N. Gkanatsios, I. Mediratta, and K. Fragkiadaki, “Bottom up top down detection transformers for language grounding in images and point clouds,” in European Conference on Computer Vision (ECCV) , 2022
2022
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J. Roh, K. Desingh, A. Farhadi, and D. Fox, “Languagerefer: Spatial-language model for 3d visual grounding,” in Conference on Robot Learning . PMLR, 2022, pp. 1046–1056
2022
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S. Huang, Y. Chen, J. Jia, and L. Wang, “Multi-view transformer for 3d visual grounding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 15 524–15 533
2022
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M. Li, L. Chen, Y. Duan, Z. Hu, J. Feng, J. Zhou, and J. Lu, “Bridge-prompt: Towards ordinal action understanding in instructional videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 19 880–19 889
2022
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J. Chen, W. Luo, W. Zhang, and L. Ma, “Explore inter-contrast between videos via composition for weakly supervised temporal sentence grounding,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, pp. 267–275, Jun. 2022
2022
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H. Ran, J. Liu, and C. Wang, “Surface representation for point clouds,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 942–18 952
2022
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D. Lian, D. Zhou, J. Feng, and X. Wang, “Scaling & shifting your features: A new baseline for efficient model tuning,” in Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
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2022
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Y. Qian, W. Luo, D. Lian, X. Tang, P. Zhao, and S. Gao, “Svip: Sequence verification for procedures in videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 19 890–19 902
2022
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J. Yang, J. Duan, S. Tran, Y. Xu, S. Chanda, L. Chen, B. Zeng, T. Chilimbi, and J. Huang, “Vision-language pre-training with triple contrastive learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 15 671–15 680
2022
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J. Chen, W. Luo, W. Zhang, and L. Ma, “Explore inter-contrast between videos via composition for weakly supervised temporal sentence grounding,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 1, 2022, pp. 267–275
2022
Closest in time.