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.
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Some applications used datasets such as SQuAD and MS-MARCO, while some works curated their own datasets for distinctive applications.
“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]
Most currently available datasets for training question generation systems are crowd-sourced, which largely affects the quality of generated questions.
“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]
Most works used factoid-based questions, but Yes/No, MCQ, and reasoning-type questions were also generated for some applications.
“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]
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“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…”
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“Keywords Computer Vision · Dataset · Image · Scene Graph · Question Answering · Objects · Attributes · Relationships · Knowledge · Language · Crowdsourcing”
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“objects (Isola et al. 2015) and understand their interactions within a scene (Sadeghi and Farhadi 2011).”
BackgroundWeakly Supervised Learning of Interactions between Humans and Objects
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The SENSEVAL-3 English Lexical Sample Task
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Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
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“The scene graph representation has been shown to improve semantic image retrieval (Johnson et al., 2015, Schuster et al., 2015) and image captioning (Farhadi et al., 2009, Chang et al., 2014, Gupta and Davis, 2008).”
MethodUnderstanding Indoor Scenes Using 3D Geometric Phrases
Choi, W., Chao, Y.-W., Pantofaru, C., & Savarese, S. (2013) · 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.2013.12 · Cited by 176 · cited 2× · see citing works
“, 2014), and 3D spatial geometry between objects has helped object detection (Choi et al., 2013).”
BackgroundEquilibrated adaptive learning rates for non-convex optimizationPreprint
Dauphin, Y., de Vries, H., & Bengio, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.04390 · Cited by 172 · cited 1× · see citing works
“We use a learning rate of 0.001 trained with rmsprop (Dauphin et al., 2015).”
MethodInfluentialImage Question Answering: A Visual Semantic Embedding Model and a New Dataset.Preprint
Ren, M., Kiros, R., & Zemel, R. S. (2015) · arXiv (Cornell University) · Cited by 144 · see citing works
Recognition by association via learning per-exemplar distances
Malisiewicz, T., & Efros, A. A. (2008) · 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.2008.4587462 · Cited by 135 · see citing works
VisKE: Visual knowledge extraction and question answering by visual verification of relation phrases
Sadeghi, F., Divvala, S., & Farhadi, A. (2015) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2015.7298752 · Cited by 124 · see citing works
Learning semantic relationships for better action retrieval in images
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Self-citationNonparametric Part Transfer for Fine-Grained Recognition
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Zero-Shot Learning via Visual Abstraction
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“Relationships have already shown their utility in improving visual cognitive tasks (Antol et al. 2014; Yang et al. 2012).”
BackgroundPhrase-based Image CaptioningPreprint
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Yang, Y., Baker, S., Kannan, A., & Ramanan, D. (2012) · 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.2012.6248095 · Cited by 87 · cited 1× · see citing works
“Relationships have already shown their utility in improving visual cognitive tasks (Antol et al. 2014; Yang et al. 2012).”
BackgroundVisual Madlibs: Fill in the blank Image Generation and Question AnsweringPreprint
Yu, L., Park, E., Berg, A. C., & Berg, T. L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00278 · Cited by 75 · see citing works
Learning Common Sense through Visual Abstraction
Vedantam, R., Lin, X., Batra, T., Zitnick, C. L., & Parikh, D. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.292 · Cited by 70 · see citing works
Embracing Error to Enable Rapid Crowdsourcing
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Self-citationIncorporating Scene Context and Object Layout into Appearance Modeling
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BackgroundToward Never Ending Language Learning.
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Building a Large-scale Multimodal Knowledge Base System for Answering Visual QueriesPreprint
Zhu, Y., Zhang, C., Ré, C., & Fei-Fei, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.05670 · Cited by 36 · see citing works
Self-citationDescribing Common Human Visual Actions in Images
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Learning to Answer Questions From Image Using Convolutional Neural NetworkPreprint
Ma, L., Lu, Z., & Li, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00333 · Cited by 29 · see citing works
A Template-Based Approach Toward Acquisition of Logical Sentences
Hou, C.-S. J., Noy, N. F., & Musen, M. A. (2002) · IFIP advances in information and communication technology · doi:10.1007/978-0-387-35602-0_8 · Cited by 13 · cited 2× · see citing works
“As a model trained on other datasets would generate complete sentences and would not be comparable (Chen et al., 2015) to our region descriptions, we convert all region descriptions generated by our model into complete sentences using predefined templates (Hou et al., 2002).”
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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
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DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
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Transformers in Vision: A Survey
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Places: A 10 Million Image Database for Scene Recognition
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On the Opportunities and Risks of Foundation Models
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Self-citationLXMERT: Learning Cross-Modality Encoder Representations from Transformers
Tan, H., & Bansal, M. (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/d19-1514 · Cited by 2,319 · see citing works
Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection
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SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (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.2017.667 · Cited by 2,073 · see citing works
UNITER: UNiversal Image-TExt Representation Learning
Chen, Y.-C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., & Liu, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58577-8_7 · Cited by 2,055 · see citing works
Self-citationScaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Jia, C., Yang, Y., Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y., Li, Z., & Duerig, T. (2021) · arXiv · doi:10.48550/arxiv.2102.05918 · Cited by 1,977 · see citing works
“However, vision-language pre-training datasets such as Conceptual Captions (Sharma et al., 2018), Visual Genome Dense Captions (Krishna et al., 2016), and ImageBERT (Qi et al., 2020) require even heavier work on human annotation, semantic parsing, cleaning and balancing.”
BackgroundKuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A., et al. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01316-z · Cited by 2,460 · see citing works
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781 · see citing works
Self-citationMaking the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.670 · Cited by 2,287 · see citing works
Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X. P., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Li, D., Wei, F., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,736 · see citing works
Relation Networks for Object Detection
Hu, H., Gu, J., Zhang, Z., Dai, J., & Wei, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00378 · Cited by 1,524 · see citing works
Improved Baselines with Visual Instruction Tuning
Liu, H., Li, C., Li, Y., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02484 · Cited by 1,748 · see citing works
“Furthermore, we find further adding region-level VQA datasets (Visual Genome [24], RefCOCO [23, 39]) improves the model’s capability of localizing fine-grained visual details.”
BackgroundGQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Hudson, D. A., & Manning, C. D. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2019.00686 · Cited by 3,703 · see citing works
Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding
Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582 · 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
Dense-Captioning Events in Videos
Krishna, R., Hata, K., Ren, F., Fei-Fei, L., & Niebles, J. C. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.83 · Cited by 1,279 · see citing works
Self-citationScene Graph Generation by Iterative Message Passing
Xu, D., Zhu, Y., Choy, C. B., & Fei-Fei, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.330 · Cited by 1,271 · see citing works
Self-citationGeneration 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
Meshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259 · see citing works
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986 · see citing works
Grounded Language-Image Pre-training
Li, L. H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J., 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/cvpr52688.2022.01069 · Cited by 1,197 · see citing works
Self-citationA Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169 · see citing works
DenseCap: Fully Convolutional Localization Networks for Dense Captioning
Johnson, J., Karpathy, A., & Fei-Fei, L. (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.2016.494 · Cited by 1,161 · see citing works
Self-citationVisual Relationship Detection with Language Priors
Lu, C., Krishna, R., Bernstein, M. S., & Fei-Fei, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_51 · Cited by 1,118 · see citing works
Self-citationNeural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (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.2018.00611 · Cited by 1,095 · see citing works
Learning Imbalanced Datasets with Label-Distribution-Aware Margin LossPreprint
Cao, K., Wei, C., Gaidon, A., Aréchiga, N., & Ma, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07413 · Cited by 1,080 · see citing works
Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
Li, J., Selvaraju, R. R., Gotmare, A. D., Joty, S., Xiong, C., & Hoi, S. (2021) · arXiv · doi:10.48550/arxiv.2107.07651 · Cited by 1,078 · see citing works
Vision-Language Models for Vision Tasks: A Survey
Zhang, J., Huang, J., Jin, S., & Lu, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3369699 · Cited by 1,051 · see citing works
“• VG [76] provides a multi-perspective understanding of images, e.g. , object-level information, scene graphs and visual question answer pairs.”
BackgroundAttention on Attention for Image Captioning
Huang, L., Wang, W., Chen, J., & Wei, X.-Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00473 · Cited by 1,092 · see citing works
Deep Visual-Semantic Alignments for Generating Image Descriptions
Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048 · see citing works
Self-citationDeep Modular Co-Attention Networks for Visual Question Answering
Zhou, Y., Yu, J., Cui, Y., Tao, D., & Tian, Q. C. (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.2019.00644 · Cited by 1,013 · see citing works
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works
Exploring Visual Relationship for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_42 · Cited by 962 · see citing works
VL-BERT: Pre-training of Generic Visual-Linguistic Representations
Su, W., Zhu, X., Cao, Y., Li, B., Lu, L., Wei, F., & Dai, J. (2019) · arXiv · doi:10.48550/arxiv.1908.08530 · Cited by 950 · see citing works
VinVL: Revisiting Visual Representations in Vision-Language Models
Zhang, P., Li, X., Hu, X., Yang, J., Zhang, L., Wang, L., Choi, Y., & Gao, 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/cvpr46437.2021.00553 · Cited by 965 · see citing works
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879 · see citing works
“Visual Genome dataset [72] is another dataset for image captioning.”
MethodGraph R-CNN for Scene Graph Generation
Yang, J., Lu, J., Lee, S., Batra, D., & Parikh, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_41 · Cited by 889 · see citing works
Auto-Encoding Scene Graphs for Image Captioning
Yang, X., Tang, K., Zhang, H., & Cai, J. (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.2019.01094 · Cited by 852 · see citing works
Unified Vision-Language Pre-Training for Image Captioning and VQA
Zhou, L., Palangi, H., Zhang, L., Hu, H., Corso, J., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.7005 · Cited by 864 · see citing works
Image Generation from Scene Graphs
Johnson, J., Gupta, A., & Fei-Fei, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00133 · Cited by 836 · see citing works
Self-citationVisual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · see citing works
“We elaborate on the details of the data collection we conducted upon 47,300 images from COCO [25] (a subset of images from Visual Genome [20]).”
MethodInfluentialSelf-citationMulti-modal Factorized Bilinear Pooling with Co-attention Learning for Visual Question Answering
Zhou, Y., Yu, J., Jianping Fan, B., & Tao, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.202 · Cited by 796 · see citing works
Unbiased Scene Graph Generation From Biased Training
Tang, K., Niu, Y., Huang, J., Shi, J., & Zhang, H. (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/cvpr42600.2020.00377 · Cited by 784 · see citing works
CLIP4Clip: An empirical study of CLIP for end to end video clip retrieval and captioning
Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., & Li, T. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.07.028 · Cited by 947 · see citing works
From Recognition to Cognition: Visual Commonsense Reasoning
Zellers, R., Bisk, Y., Farhadi, A., & Choi, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00688 · Cited by 769 · see citing works
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training
Li, G., Duan, N., Fang, Y., Gong, M., & Jiang, D. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6795 · Cited by 795 · see citing works
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., & Carion, N. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00180 · Cited by 761 · see citing works
Towards VQA Models That Can Read
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MUTAN: Multimodal Tucker Fusion for Visual Question Answering
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- 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 · 2016: 47 · 2015: 2 · 2012: 1
- 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 ›
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