The DOI matches its Crossref record; 118 of the 123 references it cites were found in a registry or an index.
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Open accessAbstract
Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of $$35$$ objects, $$26$$ attributes, and $$21$$ pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For a few of these applications, some datasets were used like SQuAD, MS-MARCO and the like. Some works curated their own dataset owing to the distinctive application involved [122, 124].”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [122]
In the citing paper
“Most datasets that are currently available for training question generation systems are crowd-sourced [42, 47, 84, 92, 114, 115, 119, 120, 122, 123], which largely impacts the quality of generated questions.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [122]
In the citing paper
“Also, most works used factoid-based questions but Yes/No [133], MCQ type [124] and reasoning type questions [122] were also generated for some applications.”
From Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications (2023) · cites this paper as [122]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148 · cited 1×
“To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in Ranjay Krishna Stanford University, Stanford, CA, USA E-mail: ranjaykrishna@cs.stanford.edu Yuke Zhu Stanford University, Stanford, CA, USA Oliver Groth Dresden University of…”
MethodImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · cited 1×
“To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in Ranjay Krishna Stanford University, Stanford, CA, USA E-mail: ranjaykrishna@cs.stanford.edu Yuke Zhu Stanford University, Stanford, CA, USA Oliver Groth Dresden University of…”
MethodHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 1×
“We then use the NeuralTalk (Karpathy and Fei-Fei, 2014) model to train a long short-term memory (LSTM) network (Hochreiter and Schmidhuber, 1997) to generate descriptions of regions.”
MethodFaster 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 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Self-citationGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Self-citationRich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Research works printed but not linked: 1
- Hayes, P. J. (1985). The second naive physics manifesto. Theories of the commonsense world (pp. 1–36).
Cited by
Sorted by citations
Items 101–200 of 1,032
Graph-Structured Representations for Visual Question Answering
Teney, D., Liu, L., & van den Hengel, A. (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.2017.344 · Cited by 475
Long-Term Feature Banks for Detailed Video Understanding
Wu, C.-Y., Feichtenhofer, C., Fan, H., He, K., Krähenbühl, P., & Girshick, R. (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.2019.00037 · Cited by 465
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 469
Knowledge-Embedded Routing Network for Scene Graph Generation
Chen, T., Yu, W., Chen, R., & Lin, L. (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.2019.00632 · Cited by 450
Explainable AI Methods - A Brief Overview
Holzinger, A., Saranti, A., Molnar, C., Biecek, P., & Samek, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-04083-2_2 · Cited by 518
Visual question answering: A survey of methods and datasets
Wu, Q., Teney, D., Wang, P., Shen, C., Dick, A., & van den Hengel, A. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.05.001 · Cited by 457
Open-Vocabulary Object Detection Using Captions
Zareian, A., Rosa, K. D., Hu, D. H., & Chang, S. (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/cvpr46437.2021.01416 · Cited by 472
Synthetic Data for Deep Learning
Nikolenko, S. (2021) · Springer optimization and its applications · doi:10.1007/978-3-030-75178-4 · Cited by 474
Object Hallucination in Image Captioning
Rohrbach, A., Hendricks, L. A., Burns, K., Darrell, T., & Saenko, K. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1437 · Cited by 420
“his is perhaps because bounding box proposals come from the MSCOCO detection task and are thus “in-domain” as opposed to the TopDown-BB model which relies on proposals learned from the Visual Genome (Krishna et al., 2017) dataset. Second, frequently training models with the self-critical loss actually increases the am…”
MethodActBERT: Learning Global-Local Video-Text Representations
Zhu, L., & Yang, Y. (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/cvpr42600.2020.00877 · Cited by 425
SimVLM: Simple Visual Language Model Pretraining with Weak SupervisionPreprint
Wang, Z., Yu, J., Yu, A. W., Dai, Z., Tsvetkov, Y., & Cao, Y. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.10904 · Cited by 701
12-in-1: Multi-Task Vision and Language Representation Learning
Lu, J., Goswami, V., Rohrbach, M., Parikh, D., & Lee, 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/cvpr42600.2020.01045 · Cited by 420
Adversarial Attacks on Deep-learning Models in Natural Language Processing
Zhang, W. E., Sheng, Q. Z., Alhazmi, A., & Li, C. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3374217 · Cited by 650
Dynamic Fusion With Intra- and Inter-Modality Attention Flow for Visual Question Answering
Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S. C. H., Wang, X., & Li, 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.2019.00680 · Cited by 420
FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding
Rahnemoonfar, M., Chowdhury, T., Sarkar, A., Varshney, D., Yari, M., & Murphy, R. R. (2021) · IEEE Access · doi:10.1109/access.2021.3090981 · Cited by 480
Entangled Transformer for Image Captioning
Li, G., Zhu, L., Liu, P., & Yang, Y. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00902 · Cited by 411
Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge
Teney, D., Anderson, P., He, X., & van den Hengel, A. (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.2018.00444 · Cited by 416
Relation-Aware Graph Attention Network for Visual Question Answering
Li, L., Gan, Z., Cheng, Y., & Liu, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01041 · Cited by 414
Self-citationModeling Relationships in Referential Expressions with Compositional Modular Networks
Hu, R., Rohrbach, M., Andreas, J., Darrell, T., & Saenko, K. (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.2017.470 · Cited by 397
Data Programming: Creating Large Training Sets, Quickly
Ratner, A., De, C., Wu, S., Selsam, D., & Ré, C. (2016) · PubMed · doi:10.48550/arxiv.1605.07723 · Cited by 510
Similarity Reasoning and Filtration for Image-Text Matching
Diao, H., Zhang, Y., Ma, L., & Lu, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16209 · Cited by 393
Bansal, A., Sikka, K., Sharma, G., Chellappa, R., & Divakaran, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_24 · Cited by 389
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Krause, J., Johnson, J., Krishna, R., & Fei-Fei, L. (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.2017.356 · Cited by 377
Self-citationFrom BoW to CNN: Two Decades of Texture Representation for Texture Classification
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1125-z · Cited by 400
In Defense of Grid Features for Visual Question Answering
Jiang, H., Misra, I., Rohrbach, M., Miller, E. G., & Chen, X. (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/cvpr42600.2020.01028 · Cited by 384
General Multi-label Image Classification with Transformers
Lanchantin, J., Wang, T., Ordóñez, V., & Qi, Y. (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/cvpr46437.2021.01621 · Cited by 389
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Luccioni, S., Jernite, Y., & Strubell, E. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658542 · Cited by 392
“We describe how we chose the tasks, datasets and models in the sections below, and present the results of our analysis in Section 4.”
BackgroundMethodLanglotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368
Task-Adaptive Attention for Image Captioning
Yan, C. C., Hao, Y., Li, L., Yin, J., Liu, A.-A., Mao, Z., Chen, Z., & Gao, X. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3067449 · Cited by 369
Self-citationAn Empirical Study of Training End-to-End Vision-and-Language Transformers
Dou, Z.-Y., Xu, Y., Gan, Z., Wang, J., Wang, S., Wang, L., Zhu, C., Zhang, P., Yuan, L., Peng, N., et al. (2022) · 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/cvpr52688.2022.01763 · Cited by 401
Simple Open-Vocabulary Object Detection
Minderer, M., Gritsenko, A. A., Stone, A., Neumann, M., Weissenborn, D., Dosovitskiy, A., Mahendran, A., Arnab, A., Dehghani, M., Shen, Z., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_42 · Cited by 406
IQA: Visual Question Answering in Interactive Environments
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., & Farhadi, A. (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.2018.00430 · Cited by 377
Scaling Open-Vocabulary Image Segmentation with Image-Level Labels
Ghiasi, G., Gu, X., Cui, Y., & Lin, T.-Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20059-5_31 · Cited by 446
Hierarchical Question-Image Co-Attention for Visual Question Answering
Lu, J. (2024) · TIB Data Manager · doi:10.57702/uhdmfu1r · Cited by 357
Nguyen, D.-K., & Okatani, T. (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.2018.00637 · Cited by 352
VirTex: Learning Visual Representations from Textual Annotations
Desai, K., & Johnson, J. (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/cvpr46437.2021.01101 · Cited by 376
Self-citationThe hateful memes challenge: Detecting hate speech in multimodal memes
Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Ringshia, P., & Testuggine, D. (2020) · arXiv · doi:10.48550/arxiv.2005.04790 · Cited by 352
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
Armeni, I., He, Z.-Y., Zamir, A., Gwak, J., Malik, J., Fischer, M., & Savarese, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00576 · Cited by 350
Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition
Chen, T., Xu, M., Hui, X., Wu, H., & Lin, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00061 · Cited by 356
Deep Reinforcement Learning-Based Image Captioning with Embedding Reward
Ren, Z., Wang, X., Zhang, N., Lv, X., & 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.2017.128 · Cited by 345
Self-citationMUREL: Multimodal Relational Reasoning for Visual Question Answering
Cadene, R., Ben-younes, H., Cord, M., & Thome, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00209 · Cited by 330
The More You Know: Using Knowledge Graphs for Image Classification
Marino, K., Salakhutdinov, R., & Gupta, A. (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.2017.10 · Cited by 337
How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites
Chen, Z., Wang, W., Tian, H., Ye, S., Gao, Z., Cui, E., Tong, W., Hu, K., Luo, J., Ma, Z., et al. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4231-5 · Cited by 1,058
“…(en&zh) [41], ST-VQA (en) [11] Document DocVQA (en) [22], Common Crawl PDF (en&zh) Grounding RefCOCO/+/g (en) [75, 126], Visual Genome (en) [42] LLaVA-150K (en&zh) [59], LVIS-Instruct4V (en) [110], ALLaVA (en&zh) [16], Laion-GPT4V (en) [1], Conversation TextOCR-GPT4V (en) [2], SVIT (en&zh)…”
A Comprehensive Survey of Scene Graphs: Generation and Application
Chang, X., Ren, P., Xu, P., Li, Z., Chen, X., & Hauptmann, A. G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3137605 · Cited by 375
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation
Yu, R., Li, A., Morariu, V. I., & Davis, L. S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.121 · Cited by 329
Scene Graph Generation With External Knowledge and Image Reconstruction
Gu, J., Zhao, H., Lin, Z., Li, S., Cai, J., & Ling, M. (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.2019.00207 · Cited by 315
VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks
Sung, Y.-L., Cho, J., & Bansal, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00516 · Cited by 329
Learning the Best Pooling Strategy for Visual Semantic Embedding
Chen, J., Hu, H., Wu, H., Jiang, Y., & Wang, C. (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/cvpr46437.2021.01553 · Cited by 318
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373
Scene Text Visual Question Answering
Biten, A. F., Tito, R., Mafla, A., Gómez, L., Rusiñol, M., Jawahar, C. V., Valveny, E., & Karatzas, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00439 · Cited by 357
Florence-2: Advancing a Unified Representation for a Variety of Vision Tasks
Xiao, B., Wu, H., Xu, W., Dai, X., Hu, H., Lu, Y., Zeng, M., Liu, C., & Yuan, L. (2024) · 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/cvpr52733.2024.00461 · Cited by 459
“Notable datasets like MS-COCO [13, 48] and Visual Genome [36] integrate various types of annotations, enabling richer understanding in spatial and semantic gran-ularities and better model interactions across annotations.”
BackgroundAction Genome: Actions As Compositions of Spatio-Temporal Scene Graphs
Ji, J., Krishna, R., Fei-Fei, L., & Niebles, J. C. (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/cvpr42600.2020.01025 · Cited by 326
Self-citationPPDM: Parallel Point Detection and Matching for Real-Time Human-Object Interaction Detection
Liao, Y., Liu, S., Wang, F., Chen, Y., Qian, C., & Feng, J. (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/cvpr42600.2020.00056 · Cited by 304
Large-Scale Adversarial Training for Vision-and-Language Representation LearningPreprint
Gan, Z., Chen, Y.-C., Li, L., Zhu, C., Cheng, Y., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.06195 · Cited by 358
Self-citationFoundation Models Defining a New Era in Vision: A Survey and Outlook
Awais, M., Naseer, M., Khan, S., Anwer, R. M., Cholakkal, H., Shah, M., Yang, M.-H., & Khan, F. S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3506283 · Cited by 304
“…[86], Chen et al. [49] propose Universal Image-TExt Representation ( UNITER ), a method to leverage conventional image-text datasets (COCO [142], Visual Genome [143], Conceptual Captions [54], SBU Captions [144]) to train foundation models that can be used for heterogeneous vision-language tasks.”
Context-Aware Attention Network for Image-Text Retrieval
Zhang, Q., Lei, Z., Zhang, Z., & Li, S. Z. (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/cvpr42600.2020.00359 · Cited by 308
Graph Structured Network for Image-Text Matching
Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., & Zhang, Y. (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/cvpr42600.2020.01093 · Cited by 305
Recurrent Fusion Network for Image Captioning
Jiang, W., Ma, L., Jiang, Y., Liu, W., & Zhang, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01216-8_31 · Cited by 306
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. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0965-7 · Cited by 429
GPS-Net: Graph Property Sensing Network for Scene Graph Generation
Lin, X., Ding, C., Zeng, J., & Tao, D. (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/cvpr42600.2020.00380 · Cited by 304
Factorizable Net: An Efficient Subgraph-Based Framework for Scene Graph Generation
Li, Y., Ouyang, W., Zhou, B., Shi, J., Zhang, C., & Wang, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_21 · Cited by 290
Intelligent Computing: The Latest Advances, Challenges, and Future
Zhu, S., Yu, T., Xu, T., Chen, H., Dustdar, S., Gigan, S., Gündüz, D., Hossain, E., Jin, Y., Lin, F., et al. (2023) · Intelligent Computing · doi:10.34133/icomputing.0006 · Cited by 334
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Wang, Y.-X., & Hebert, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46466-4_37 · Cited by 296
A-OKVQA: A Benchmark for Visual Question Answering Using World Knowledge
Schwenk, D., Khandelwal, A., Clark, C., Marino, K., & Mottaghi, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_9 · Cited by 405
Multi-Modal Knowledge Graph Construction and Application: A Survey
Zhu, X., Li, Z., Wang, X., Jiang, X., Sun, P., Wang, X., Xiao, Y., & Yuan, N. J. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3224228 · Cited by 353
Vision-Language Pre-Training with Triple Contrastive Learning
Yang, J., Duan, J., Tran, S., Xu, Y., Chanda, S., Chen, L., Zeng, B., Chilimbi, T., & Huang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01522 · Cited by 309
Learning to Segment Every Thing
Hu, R., Dollár, P., He, K., Darrell, T., & Girshick, R. (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.2018.00445 · Cited by 292
StyleNet: Generating Attractive Visual Captions with Styles
Gan, C., Gan, Z., He, X., Gao, J., & Deng, L. (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.2017.108 · Cited by 291
Wang, T., Huang, J., Zhang, H., & Sun, Q. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01077 · Cited by 291
UniT: Multimodal Multitask Learning with a Unified Transformer
Hu, R., & Singh, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00147 · Cited by 303
Identity-Aware Textual-Visual Matching with Latent Co-attention
Li, S., Xiao, T., Li, H., Yang, W. P., & Wang, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.209 · Cited by 291
MMKG: Multi-modal Knowledge Graphs
Liu, Y., Li, H., García-Durán, A., Niepert, M., Oñoro-Rubio, D., & Rosenblum, D. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21348-0_30 · Cited by 303
Geva, M., Goldberg, Y., & Berant, J. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1107 · Cited by 284
VLP: A Survey on Vision-language Pre-training
Chen, F., Zhang, D., Han, M., Chen, X.-Y., Shi, J. C., Xu, S., & Xu, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1369-5 · Cited by 330
Cross-Modal Attention With Semantic Consistence for Image–Text Matching
Xu, X., Wang, T., Yang, Y., Zuo, L., Shen, F., & Shen, H. T. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2967597 · Cited by 282
Does learning require memorization? a short tale about a long tail
Feldman, V. (2020) · Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing · doi:10.1145/3357713.3384290 · Cited by 287
Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge
Hinami, R., Mei, T., & Satoh, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.391 · Cited by 277
Kimera: From SLAM to spatial perception with 3D dynamic scene graphs
Rosinol, A., Violette, A., Abate, M., Hughes, N., Chang, Y., Shi, J., Gupta, A., & Carlone, L. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211056674 · Cited by 306
Multimodal Large Language Models: A Survey
Wu, J., Gan, W., Chen, Z., Wan, S., & Yu, P. S. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386743 · Cited by 369
Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal TransformersPreprint
Huang, Z., Zeng, Z., Liu, B., Fu, D., & Fu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.00849 · Cited by 433
Illuminating the dark spaces of healthcare with ambient intelligence
Haque, A., Milstein, A., & Fei-Fei, L. (2020) · Nature · doi:10.1038/s41586-020-2669-y · Cited by 274
Self-citationSurvey on Multi-Output Learning
Xu, D., Shi, Y., Tsang, I. W.-H., Ong, Y.-S., Gong, C., & Shen, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2945133 · Cited by 293
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
Li, K., Wang, Y., He, Y., Li, Y., Wang, Y., Liu, Y., Wang, Z., Xu, J., Chen, G., Lou, P., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02095 · Cited by 300
“In addition to the aforementioned training data in Stage1, we further introduce 2M image captions (COCO [46], Visual Genome [34], and SBU [60]) and 10M video captions (InternVid [78]), to enrich the caption diversity.”
MethodSpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
Chen, B., Xu, Z., Kirmani, S., Ichter, B., Sadigh, D., Guibas, L., & Xia, F. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01370 · Cited by 281
“Many prior works have focused on benchmarking VLMs [58, 66], considering tasks like VQA (e.g. VQAv2 [28], OK-VQA [47], COCO [41], or Visual Genome [37]).”
SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities
Chen, B., Xu, Z., Kirmani, S., Ichter, B., Driess, D., Florence, P., Sadigh, D., Guibas, L., & Xia, F. (2024) · arXiv · doi:10.48550/arxiv.2401.12168 · Cited by 269
Say As You Wish: Fine-Grained Control of Image Caption Generation With Abstract Scene Graphs
Chen, S., Jin, Q., Wang, P., & Wu, Q. (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/cvpr42600.2020.00998 · Cited by 272
Self-citationBBDM: Image-to-Image Translation with Brownian Bridge Diffusion Models
Li, B., Xue, K., Liu, B., & Lai, Y. (2023) · 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/cvpr52729.2023.00194 · Cited by 324
ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering
Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., & Tao, D. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019127 · Cited by 281
“datasets, such as Visual Genome (Krishna et al. 2016) and VQA (Antol et al.”
BackgroundMethodSpatio-Temporal Graph for Video Captioning With Knowledge Distillation
Pan, B., Cai, H., Huang, D.-A., Lee, K.-H., Gaidon, A., Adeli, E., & Niebles, J. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01088 · Cited by 281
Cross-modal Scene Graph Matching for Relationship-aware Image-Text Retrieval
Wang, S., Wang, R., Yao, Z., Shan, S., & Chen, X. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093614 · Cited by 264
DVQA: Understanding Data Visualizations via Question Answering
Kafle, K., Price, B. L., Cohen, S. D., & Kanan, C. (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.2018.00592 · Cited by 305
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Li, R., Zhang, S., Wan, B., & He, X. (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/cvpr46437.2021.01096 · Cited by 265
OCNet: Object Context for Semantic Segmentation
Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., & Wang, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-021-01465-9 · Cited by 857
Unifying Vision-and-Language Tasks via Text GenerationPreprint
Cho, J., Lei, J., Tan, H. X., & Bansal, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02779 · Cited by 291
Graphical Contrastive Losses for Scene Graph Parsing
Zhang, J., Shih, K. J., Elgammal, A., Tao, A., & Catanzaro, B. (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.2019.01180 · Cited by 263
ReferIt3D: Neural Listeners for Fine-Grained 3D Object Identification in Real-World Scenes
Achlioptas, P., Abdelreheem, A., Xia, F., Elhoseiny, M., & Guibas, L. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_25 · Cited by 282
Huang, T.-H., Ferraro, F., Mostafazadeh, N., Misra, I., Agrawal, A., Devlin, J., Girshick, R., He, X., Kohli, P., Batra, D., et al. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1147 · Cited by 254
Yang, K., Qinami, K., Fei-Fei, L., Deng, J., & Russakovsky, O. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3375709 · Cited by 254
Self-citationScaling Language-Image Pre-Training via Masking
Li, Y., Fan, H., Hu, R., Feichtenhofer, C., & He, K. (2023) · 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/cvpr52729.2023.02240 · Cited by 292
Multimodal Sentiment Analysis With Image-Text Interaction Network
Zhu, T., Li, L., Yang, J., Zhao, S., Liu, H., & Qian, J. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3160060 · Cited by 275
Self-citation
Metadata
- Authors
- Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L.
- Affiliations
- Stanford University (US)
- Technische Universität Dresden (DE)
- Yahoo (United States) (US)
- Snap (United States) (US)
- Centrum Wiskunde & Informatica (NL)
- Corresponding author
- Krishna, Ranjay
- ORCID
- Venue
- International Journal of Computer Vision
- Published
- 2017-02-06
- Volume
- 123
- Issue
- 1
- Pages
- 32-73
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2277195237
- Cited by
- 5,107
- References
- 123
- Citations by year
- 2026: 313 · 2025: 508 · 2024: 690 · 2023: 761 · 2022: 671 · 2021: 738 · 2020: 586 · 2019: 411 · 2018: 230 · 2017: 149
- FWCI
- 156.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Computer Vision (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- arXiv (Cornell University)
- Funders
- Brown Institute for Media Innovation
- Yahoo Inc.
- Stanford University Computer Science Department
- Office of Naval Research
- Toyota USA
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object relations, picture description, question answering, object properties, WordNet synsets, visual cognition, crowdsourced annotation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
