Ensemble of Exemplar-SVMs for Object Detection and Beyond



This paper proposes a conceptually simple but surprisingly powerful method which combines the effectiveness of a discriminative object detector with the explicit correspondence offered by a nearest-neighbor approach. The method is based on training a separate linear SVM classifier for every exemplar in the training set. Each of these Exemplar-SVMs is thus defined by a single positive instance and millions of negatives. While each detector is quite specific to its exemplar, we empirically observe that an ensemble of such Exemplar-SVMs offers surprisingly good generalization. Our performance on the PASCAL VOC detection task is on par with the much more complex latent part-based model of Felzenszwalb et al., at only a modest computational cost increase. But the central benefit of our approach is that it creates an explicit association between each detection and a single training exemplar. Because most detections show good alignment to their associated exemplar, it is possible to transfer any available exemplar meta-data (segmentation, geometric structure, 3D model, etc.) directly onto the detections, which can then be used as part of overall scene understanding.

ICCV 2011 Paper download and Citation

Tomasz Malisiewicz, Abhinav Gupta, Alexei A. Efros. Ensemble of Exemplar-SVMs for Object Detection and Beyond . In ICCV, 2011. PDF [BibTeX]

ICML 2012 Invited Talk Extended Abstract

Tomasz Malisiewicz, Abhinav Shrivastava, Abhinav Gupta, Alexei A. Efros. Exemplar-SVMs for Visual Object Detection, Label Transfer and Image Retrieval. To be presented as an invited applications talk at ICML, 2012. PDF | Talk Slides

Results: 3D Transfer


Results: Person Priming


Results: Segmentation Transfer


Matlab Code (Beta Version)

Source code for the entire Exemplar-SVM infrastructure (large-scale training using a cluster, fast detection, etc.) is available for download below. To download the source code, visit:

Exemplar-SVM code page on GitHub
Fork me on GitHub


Slides to a talk about Exemplar-SVMs which I gave at MIT (in PDF format). You can also take a look at the presentation I gave at ICML2012.

Related Papers from CMU

Abhinav Shrivastava, Tomasz Malisiewicz, Abhinav Gupta, Alexei A. Efros. Data-driven Visual Similarity for Cross-domain Image Matching. In SIGGRAPH Asia 2011. PDF Project Page

Tomasz Malisiewicz, Alexei A. Efros. Recognition by Association via Learning Per-exemplar Distances. In CVPR, June 2008. PDF Project Page


This research is supported by: