About me

I finished my Ph.D. in computer vision at The University of Chicago under the supervision of Pedro Felzenszwalb in April 2012. Now, I'm a postdoctoral fellow working with Jitendra Malik at UC Berkeley.

My main research interests are in computer vision, AI, and machine learning. I'm particularly focused on building models for object detection and recognition. These models aim to incorporate the "right" biases so that machine learning algorithms can understand image content from moderate to large-scale datasets. I always have an eye towards fast systems that work well in practice.

During my Ph.D., I spent time as a research intern at Microsoft Research Cambridge, UK working on human pose estimation from depth images. I also participated in several first-place entries into the PASCAL VOC object detection challenge, and was awarded a "life-time achievement" prize for my work on deformable part models. I think this refers to the life-time of the PASCAL challenge—and not mine!

Project pages

• Deformable Part Models (DPM) (voc-release5 — dissertation code / includes cascade and NIPS grammar model)
• Cascade object detection with deformable part models (add-on code for voc-release4.01)

Journal papers

Efficient Human Pose Estimation from Single Depth Images
J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, A. Blake
IEEE Transactions on Pattern Analysis and Machine Intelligence, preprint, Dec. 2012
An integrated description of the original Kinect pose estimation algorithm and my ICCV 2011 algorithm.

Object Detection with Discriminatively Trained Part Based Models^†
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, Sep. 2010
code: PAMI code / latest (voc-release5) Deformable part models (DPM).

Conference papers

Discriminatively Activated Sparselets
R. Girshick, H. O. Song, T. Darrell
To Appear in International Conference on Machine Learning (ICML), 2013
oral presentation
supplement / Caltech-101 demo code
Speed up a wide array of structured predictors (including DPMs and multiclass SVMs) by discriminatively learning activations over a dictionary of model atoms. Same speedup as the ECCV 2012 sparselets paper, but with much higher accuracy.

Sparselet Models for Efficient Multiclass Object Detection
H.O. Song, S. Zickler, T. Althoff, R. Girshick, M. Fritz, C. Geyer, P. Felzenszwalb, T. Darrell
European Conference on Computer Vision (ECCV), 2012
Fast DPM detection by sparse coding model parameters.

Object Detection with Grammar Models
R. Girshick, P. Felzenszwalb, D. McAllester
Neural Information Processing Systems (NIPS), 2011
spotlight presentation: video
code: voc-release5
State-of-the-art person detection on PASCAL VOC using DPM-like models described in a grammar framework.

Efficient Regression of General-Activity Human Poses from Depth Images
R. Girshick, J. Shotton, P. Kohli, A. Criminisi, A. Fitzgibbon
IEEE International Conference on Computer Vision (ICCV), 2011
supplement / video
Pose estimation using the Kinect depth sensor. Faster (4x) and more accurate than the original Kinect pose estimation algorithm (CVPR 2011).

Cascade Object Detection with Deformable Part Models^†
P. Felzenszwalb, R. Girshick, D. McAllester
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010
oral presentation: pdf / keynote / video
code: voc-release4 / voc-release5 Fast cascade algorithm for DPM detection (about 14x faster than the baseline).

Visibility Constraints on Features of 3D Objects^†
R. Basri, P. Felzenszwalb, R. Girshick, D. Jacobs, C. Klivans
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009

Simulating Chinese Brush Painting: the Parametric Hairy Brush
R. Girshick
ACM SIGGRAPH Posters, 2004
Session: Nonphotorealistic Animation and Rendering
(Undergrad senior thesis, Brandeis University, May 2004)

Ph.D. dissertation

From Rigid Templates to Grammars: Object Detection with Structured Models
R. Girshick
Ph.D. dissertation, The University of Chicago, Apr. 2012
slides Models and algorithms that improve on the original DPM by more than 50% mAP.

Object Detection with Heuristic Coarse-to-Fine Search
R. Girshick
M.S. thesis, The University of Chicago, Dec. 2009
Precursor to the DPM cascade (CVPR 2010).

Random stuff

• Some photos
• Some better photos
• A dubious figure of questionable character
• An article on convex relaxation
• Like wikipedia?