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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The pascal visual object classes (VOC) challenge
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Object Detection with Discriminatively Trained Part-Based Models
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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
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Statistical machine translation
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Every Picture Tells a Story: Generating Sentences from Images
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Self-citationModeling mutual context of object and human pose in human-object interaction activities
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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
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Learning Non-maximum Suppression
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End-to-End People Detection in Crowded Scenes
Stewart, R., Andriluka, M., & Ng, A. Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.255 · Cited by 539
Multimodal Convolutional Neural Networks for Matching Image and Sentence
Ma, L., Lu, Z., Shang, L., & Li, H. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.301 · Cited by 334
Factors of Transferability for a Generic ConvNet Representation
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., & Carlsson, S. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2500224 · Cited by 301
Learning Everything about Anything: Webly-Supervised Visual Concept Learning
Divvala, S. K., Farhadi, A., & Guestrin, C. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.412 · Cited by 267
Self-citationSingle-Pedestrian Detection Aided by Multi-pedestrian Detection
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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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