The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search
Abstract
Reranking has been a focal technique in multimedia retrieval due to its efficacy in improving initial retrieval results. Current reranking methods, however, mainly rely on the heuristic weighting. In this paper, we propose a novel reranking approach called Self-Paced Reranking (SPaR) for multimodal data. As its name suggests, SPaR utilizes samples from easy to more complex ones in a self-paced fashion. SPaR is special in that it has a concise mathematical objective to optimize and useful properties that can be theoretically verified. It on one hand offers a unified framework providing theoretical justifications for current reranking methods, and on the other hand generates a spectrum of new reranking schemes. This paper also advances the state-of-the-art self-paced learning research which potentially benefits applications in other fields. Experimental results validate the efficacy and the efficiency of the proposed method on both image and video search tasks. Notably, SPaR achieves by far the best result on the challenging TRECVID multimedia event search task.
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
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 10×
“f(v; k) is the self-paced function, and in [19], it is defined based on the l1 norm of v ∈ [0, 1] :”
BackgroundMethodInfluentialAlgorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
Zhu, C., Byrd, R. H., Lu, P., & Nocedal, J. (1997) · ACM Transactions on Mathematical Software · doi:10.1145/279232.279236 · Cited by 3,462
Action recognition by dense trajectories
Wang, H., Klaser, A., Schmid, C., & Liu, C.-L. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995407 · Cited by 2,154
Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization
Tseng, P. (2001) · Journal of Optimization Theory and Applications · doi:10.1023/a:1017501703105 · Cited by 1,989
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval
Zhai, C., & Lafferty, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130377 · Cited by 1,537
Relevance-Based Language Models
Lavrenko, V., & Croft, W. B. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130376 · Cited by 1,422
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
Joachims, T. (1997) · Cited by 1,262
Cited by
A Survey on Curriculum Learning
Wang, X., Chen, Y., & Zhu, W. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3069908 · Cited by 692
Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework
Zhang, D., Meng, D., & Han, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2567393 · Cited by 508
Self-citationMulti-Modal Curriculum Learning for Semi-Supervised Image Classification
Gong, C., Tao, D., Maybank, S. J., Liu, W., Kang, G., & Yang, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2563981 · Cited by 285
Zhang, D., Han, J., Zhao, L., & Meng, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1112-4 · Cited by 164
Self-citationTowards Human-Machine Cooperation: Self-Supervised Sample Mining for Object Detection
Wang, K., Yan, X., Zhang, D., Zhang, L., & Lin, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00173 · Cited by 127
Metadata
- Authors
- Jiang, L., Meng, D., Mitamura, T., & Hauptmann, A. G.
- Venue
- Proceedings of the 22nd ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2014-11-03
- Pages
- 547-556
- OpenAlex
- W1995137594
- Cited by
- 271
- References
- 41
- FWCI
- 13.88
- 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: Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search. https://app.cochat.ai/papers/10.1145/2647868.2654918
- The paper
- Jiang, L., Meng, D., Mitamura, T., & Hauptmann, A. G. (2014). Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search. Proceedings of the 22nd ACM international conference on Multimedia. https://doi.org/10.1145/2647868.2654918
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.
