The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval
Open accessAbstract
Semantically complex queries which include attributes of objects and relations between objects still pose a major challenge to image retrieval systems.Recent work in computer vision has shown that a graph-based semantic representation called a scene graph is an effective representation for very detailed image descriptions and for complex queries for retrieval.In this paper, we show that scene graphs can be effectively created automatically from a natural language scene description.We present a rule-based and a classifierbased scene graph parser whose output can be used for image retrieval.We show that including relations and attributes in the query graph outperforms a model that only considers objects and that using the output of our parsers is almost as effective as using human-constructed scene graphs (Recall@10 of 27.1% vs. 33.4%).Additionally, we demonstrate the general usefulness of parsing to scene graphs by showing that the output can also be used to generate 3D scenes.
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Glove: Global Vectors for Word Representation
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Accurate unlexicalized parsing
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Self-citationA survey of content-based image retrieval with high-level semantics
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Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
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(2013) · Cited by 1,117
Cited by
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Self-citationSPICE: 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
Visual Relationship Detection with Language Priors
Lu, C., Krishna, R., Bernstein, M., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_51 · Cited by 1,069
Self-citationA 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
“PICE. SPICE (Semantic Propositional Image Caption Evaluation) [3] is a new caption evaluation metric based on semantic concept. It is based on a graph-based semantic representation called scene-graph [63, 123]. This graph can extract the information of different objects, attributes and their relationships from the ima…”
MethodImage 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
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Metadata
- Authors
- Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., & Manning, C. D.
- Venue
- Proceedings of the Fourth Workshop on Vision and Language
- Published
- 2015
- Pages
- 70-80
- OpenAlex
- W2250378130
- Cited by
- 332
- References
- 28
- FWCI
- 6.37
- Type
- conference paper
- Language
- en
- Open access
- gold
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- Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., & Manning, C. D. (2015). Generating Semantically Precise Scene Graphs from Textual Descriptions for Improved Image Retrieval. Proceedings of the Fourth Workshop on Vision and Language. https://doi.org/10.18653/v1/w15-2812
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