Multi-Modal Dynamic Graph Transformer for Visual Grounding
Abstract
Visual grounding (VG) aims to align the correct regions of an image with a natural language query about that image. We found that existing VG methods are trapped by the single-stage grounding process that performs a sole evaluate-and-rank for meticulously prepared regions. Their performance depends on the density and quality of the candidate regions, and is capped by the inability to optimize the located regions continuously. To address these issues, we propose to remodel VG into a progressively optimized visual semantic alignment process. Our proposed multi-modal dynamic graph transformer (M-DGT) achieves this by building upon the dynamic graph structure with regions as nodes and their semantic relations as edges. Starting from a few randomly initialized regions, M-DGT is able to make sustainable adjustments (i.e., 2D spatial transformation and deletion) to the nodes and edges of the graph based on multi-modal information and the graph feature, thereby efficiently shrinking the graph to approach the ground truth regions. Experiments show that with an average of 48 boxes as initialization, the performance of M-DGT on the Flickr30k Entities and RefCOCO datasets outperforms existing state-of-the-art methods by a substantial margin, in terms of both accuracy and Intersect over Union (IOU) scores. Furthermore, introducing M-DGT to optimize the predicted regions of existing methods can further significantly improve their performance. The source codes are available at https://github.com/iQua/M-DGT.
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Consequently, successive works mostly exploit pretrained models for tackling various downstream tasks.
“Consequently, successive works mostly exploit pretrained models for tackling various downstream tasks [120], [263], [264], [265], [266].”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [120]
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137 · see citing works
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works
End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works
Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00075 · Cited by 6,612 · see citing works
Jaderberg, M., Simonyan, K., Zisserman, A., & Kavukcuoglu, K. (2015) · arXiv · doi:10.48550/arxiv.1506.02025 · Cited by 5,137 · see citing works
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00636 · Cited by 5,117 · see citing works
Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444 · see citing works
Stacked Cross Attention for Image-Text Matching
Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_13 · Cited by 1,402 · see citing works
Generation and Comprehension of Unambiguous Object Descriptions
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. P. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.9 · Cited by 1,211 · see citing works
ReferItGame: Referring to Objects in Photographs of Natural Scenes
Kazemzadeh, S., Ordóñez, V., Matten, M., & Berg, T. L. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1086 · Cited by 1,082 · see citing works
(2016) · doi:10.1007/978-3-319-46475-6_5 · Cited by 1,081 · see citing works
Deep Q-learning From Demonstrations
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Horgan, D., Quan, J., Sendonaris, A., Osband, I., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11757 · Cited by 889 · see citing works
MAttNet: Modular Attention Network for Referring Expression Comprehension
Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., & Berg, T. L. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00142 · Cited by 857 · see citing works
Liu, Z., & Zhou, J. (2020) · Synthesis lectures on artificial intelligence and machine learning · doi:10.1007/978-3-031-01587-8_7 · Cited by 846 · see citing works
Dual Attention Networks for Multimodal Reasoning and Matching
Nam, H., Ha, J.-W., & Kim, J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.232 · Cited by 749 · see citing works
Graph Transformer NetworksPreprint
Yun, S., Jeong, M., Kim, R., Kang, J., & Kim, H. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06455 · Cited by 747 · see citing works
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Wang, L., Li, Y., Huang, J. L., & Lazebnik, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2797921 · Cited by 538 · see citing works
Natural Language Object Retrieval
Hu, R., Xu, H., Rohrbach, M., Feng, J., Saenko, K., & Darrell, T. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.493 · Cited by 534 · see citing works
Modeling Context Between Objects for Referring Expression Understanding
Nagaraja, V., Morariu, V. I., & Davis, L. S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_48 · Cited by 450 · see citing works
A Fast and Accurate One-Stage Approach to Visual Grounding
Yang, Z., Gong, B., Wang, L., Huang, W., Yu, D., & Luo, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00478 · Cited by 431 · see citing works
TransVG: End-to-End Visual Grounding with Transformers
Deng, J., Yang, Z., Chen, T., Zhou, W., & Li, H. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00179 · Cited by 406 · see citing works
A Joint Speaker-Listener-Reinforcer Model for Referring Expressions
Yu, L., Tan, H., Bansal, M., & Berg, T. (2016) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.375 · Cited by 297 · see citing works
Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Networks
Wang, P., Wu, Q., Cao, J., Shen, C., Gao, L., & van den Hengel, A. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00206 · Cited by 280 · see citing works
Improving One-Stage Visual Grounding by Recursive Sub-query Construction
Yang, Z., Chen, T., Wang, L., & Luo, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58568-6_23 · Cited by 270 · see citing works
Iterative Visual Reasoning Beyond Convolutions
Chen, X., Li, L.-J., Fei-Fei, L., & Gupta, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00756 · Cited by 236 · see citing works
Visual Grounding via Accumulated Attention
Deng, C., Wu, Q., Wu, Q., Hu, F., Lyu, F., & Tan, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00808 · Cited by 199 · see citing works
Dense Captioning with Joint Inference and Visual Context
Yang, L., Tang, K., Yang, J., & Li, L.-J. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.214 · Cited by 174 · see citing works
Phrase Localization and Visual Relationship Detection with Comprehensive Image-Language Cues
Plummer, B. A., Mallya, A., Cervantes, C. M., Hockenmaier, J., & Lazebnik, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.213 · Cited by 167 · see citing works
Comprehension-Guided Referring Expressions
Luo, R., & Shakhnarovich, G. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.333 · Cited by 147 · see citing works
Dynamic Graph Message Passing Networks
Li, Z., Xu, D., Arnab, A., & Torr, P. H. S. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00378 · Cited by 135 · see citing works
Rethinking Diversified and Discriminative Proposal Generation for Visual Grounding
Zhou, Y., Yu, J., Xiang, C., Zhao, Z., Tian, Q. C., & Tao, D. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/155 · Cited by 134 · see citing works
Query-Guided Regression Network with Context Policy for Phrase Grounding
Chen, K., Kovvuri, R., & Nevatia, R. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.95 · Cited by 123 · see citing works
Conditional Image-Text Embedding Networks
Plummer, B. A., Kordas, P., Kiapour, M. H., Zheng, S., Piramuthu, R., & Lazebnik, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_16 · Cited by 121 · see citing works
Cross-Modal Relationship Inference for Grounding Referring Expressions
Yang, S., Li, G., & Yu, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00427 · Cited by 116 · see citing works
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment
Datta, S., Sikka, K., Roy, A., Ahuja, K., Parikh, D., & Divakaran, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00269 · Cited by 90 · see citing works
Learning Cross-Modal Context Graph for Visual Grounding
Liu, Y., Wan, B., Zhu, X., & He, X. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6833 · Cited by 83 · see citing works
Visual Grounding with Transformers
Du, Y., Fu, Z., Liu, Q., & Wang, Y. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859880 · Cited by 83 · see citing works
Neural Sequential Phrase Grounding (SeqGROUND)
Doğan, P., Sigal, L., & Groß, M. H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00430 · Cited by 49 · see citing works
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement Learning
Sun, M., Xiao, J., & Lim, E. G. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01384 · Cited by 40 · see citing works
Visual-Semantic Graph Matching for Visual Grounding
Jing, C., Wu, Y., Pei, M., Hu, Y. G., Jia, Y., & Wu, Q. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413902 · Cited by 34 · see citing works
Parallel Attention Mechanisms in Neural Machine Translation
Medina, J. R., & Kalita, J. K. (2018) · IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2018.00088 · Cited by 19 · see citing works
Visual Query Answering by Entity-Attribute Graph Matching and Reasoning
Xiong, P., Zhan, H., Wang, X. Y., Sinha, B., & Wu, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00855 · Cited by 19 · see citing works
Phrase Grounding by Soft-Label Chain Conditional Random Field
Liu, J., & Hockenmaier, J. (2019) · doi:10.18653/v1/d19-1515 · Cited by 8 · see citing works
Visual grounding through iterative refinement
Fan, Z. (2020) · cIRcle (University of British Columbia) · doi:10.14288/1.0391964 · Cited by 1 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
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- Authors
- Chen, S., & Li, B.
- Affiliations
- University of Toronto (CA)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 15513-15522
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312523916
- Cited by
- 28
- References
- 49
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 10 · 2023: 7
- FWCI
- 2.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- visual grounding, multimodal fusion, graph optimization, spatial transformation, RefCOCO
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