The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Grounding of Textual Phrases in Images by Reconstruction
Open accessAbstract
Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text reference resolution. Few datasets provide the ground truth spatial localization of phrases, thus it is desirable to learn from data with no or little grounding supervision. We propose a novel approach which learns grounding by reconstructing a given phrase using an attention mechanism, which can be either latent or optimized directly. During training our approach encodes the phrase using a recurrent network language model and then learns to attend to the relevant image region in order to reconstruct the input phrase. At test time, the correct attention, i.e., the grounding, is evaluated. If grounding supervision is available it can be directly applied via a loss over the attention mechanism. We demonstrate the effectiveness of our approach on the Flickr 30k Entities and ReferItGame datasets with different levels of supervision, ranging from no supervision over partial supervision to full supervision. Our supervised variant improves by a large margin over the state-of-the-art on both datasets.
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Cites
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Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
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Wang, L., Li, Y., & Lazebnik, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.541 · Cited by 760
Segmentation from Natural Language Expressions
Hu, R., Rohrbach, M., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_7 · Cited by 438
Self-citationScanRefer: 3D Object Localization in RGB-D Scans Using Natural Language
Chen, D. Z., Chang, A. X., & Nießner, M. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58565-5_13 · Cited by 312
Attention, please! A survey of neural attention models in deep learning
de Santana Correia, A., & Colombini, E. L. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10148-x · Cited by 298
Speaker-Follower Models for Vision-and-Language Navigation
Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv · doi:10.48550/arxiv.1806.02724 · Cited by 291
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Metadata
- Authors
- Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., & Schiele, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 817-834
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2247513039
- Cited by
- 221
- References
- 10
- FWCI
- 5.52
- Type
- conference paper
- Language
- en
- Open access
- green
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