The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Fine-Grained Visual Comparisons with Local Learning
Abstract
Given two images, we want to predict which exhibits a particular visual attribute more than the other-even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets-including a large newly curated dataset for fine-grained comparisons-our method outperforms stateof-the-art methods for relative attribute prediction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,319
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Weinberger, K. Q., & Saul, L. K. (2009) · Journal of Machine Learning Research · doi:10.5555/1577069.1577078 · Cited by 4,044
Optimizing search engines using clickthrough data
Joachims, T. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775067 · Cited by 3,873
Information-theoretic metric learning
Davis, J. V., Kulis, B., Jain, P., Sra, S., & Dhillon, I. S. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273523 · Cited by 2,006
Atkeson, C. G., Moore, A. W., & Schaal, S. (1997) · Artificial Intelligence Review · doi:10.1023/a:1006559212014 · Cited by 1,671
Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459250 · Cited by 1,361
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
Zhang, H., Berg, A. C., Maire, M., & Malik, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.301 · Cited by 1,114
Parikh, D., & Grauman, K. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126281 · Cited by 851
Self-citationDiscriminant adaptive nearest neighbor classification
Hastie, T., & Tibshirani, R. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.506411 · Cited by 833
Bottou, L., & Vapnik, V. (1992) · Neural Computation · doi:10.1162/neco.1992.4.6.888 · Cited by 542
Cited by
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Kim, T., Cha, M., Kim, H., Lee, J. K., & Kim, J. (2017) · arXiv · doi:10.48550/arxiv.1703.05192 · Cited by 1,716
The iNaturalist Species Classification and Detection Dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., & Belongie, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00914 · Cited by 1,451
Toward Multimodal Image-to-Image Translation
Zhu, J.-Y., Zhang, R., Pathak, D., Darrell, T. J., Efros, A. A., Wang, O., & Shechtman, E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.11586 · Cited by 906
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Jeong, J., Zou, Y., Kim, T., Zhang, D., Ravichandran, A., & Dabeer, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01878 · Cited by 585
Metadata
- Authors
- Yu, A., & Grauman, K.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 192-199
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2093848332
- Cited by
- 454
- References
- 30
- FWCI
- 9.19
- Type
- conference paper
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fine-Grained Visual Comparisons with Local Learning. https://app.cochat.ai/papers/10.1109/cvpr.2014.32
- The paper
- Yu, A., & Grauman, K. (2014). Fine-Grained Visual Comparisons with Local Learning. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.32
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
