The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Recognition using visual phrases
Abstract
In this paper we introduce visual phrases, complex visual composites like “a person riding a horse”. Visual phrases often display significantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multi-class detection system must decode detector outputs to produce final results; this is usually done with non-maximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results.
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Cites
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
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A discriminatively trained, multiscale, deformable part model
Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587597 · Cited by 2,867
Object class recognition by unsupervised scale-invariant learning
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Statistical machine translation
(2010) · Choice Reviews Online · doi:10.5860/choice.47-6293 · Cited by 1,309
Every Picture Tells a Story: Generating Sentences from Images
Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15561-1_2 · Cited by 1,204
Self-citationModeling mutual context of object and human pose in human-object interaction activities
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Spatial Priors for Part-Based Recognition Using Statistical Models
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Discriminative models for multi-class object layout
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Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers
Gupta, A. K., & Davis, L. S. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_3 · Cited by 164
POP: Patchwork of Parts Models for Object Recognition
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Scene Discovery by Matrix Factorization
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Self-citation
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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
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845
Scene 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
Image retrieval using scene graphs
Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101
Learning Non-maximum Suppression
Hosang, J., Benenson, R., & Schiele, B. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.685 · Cited by 736
Metadata
- Authors
- Sadeghi, M. A., & Farhadi, A.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 1745-1752
- OpenAlex
- W2049705550
- Cited by
- 417
- References
- 15
- FWCI
- 23.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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