CS 7670

Special Topics in Computer Vision

Fall 2014, Cornell University

Thursdays 11:30-12:30 in 310 Gates

Instructors: Noah Snavely (snavely@cs.cornell.edu), Kyle Wilson (wilsonkl@cs.cornell.edu), Song Cao (caosong@cs.cornell.edu)

In the past decade computer vision has made incredible progress across the board, in geometry, recognition, image processing, and other areas. In this graduate seminar in computer vision, we will survey and discuss state-of-the-art research papers in this quickly moving field, with a focus on 3D geometry estimation, image matching and retrieval, use of the Internet to gather and annotate data, and scene understanding. This will draw on papers from both computer vision and computer graphics venues.







25 Sept

Kyle Wilson

Interesting Papers from ECCV2014

J. Deng et al. Large-Scale Object Classification using Label Relation Graphs. ECCV 2014. (Best Paper award)[PDF]

P. Isola, D. Zoran, D. Krishnan, E. H. Adelson. Crisp Boundary Detection Using Pointwise Mutual Information. ECCV 2014. [WEB ][PDF]

2 Oct


9 Oct

Sean Bell

The ILSVRC2014 competition (workshop at ECCV2014)

The competition: "ImageNet Large Scale Visual Recognition Challenge" Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei. [PDF]

(1st place) GoogLeNet Team: "Going deeper with convolutions" Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. [PDF]

(2nd place) VGG Team: "Very Deep Convolutional Networks for Large-Scale Image Recognition" Karen Simonyan, Andrew Zisserman[PDF]

16 Oct


23 Oct

Scott Wehrwein

Simultaneous Mosaicing and Tracking with an Event Camera. BMVC 2014. [PDF]

Programmable Automotive Headlights. ECCV 2014. [PDF]

30 Oct

Song Cao

Visualizing and Understanding Convolutional Networks. Matthew Zeiler, Rob Fergus. ECCV 2014. [PDF]

Part-based R-CNNs for Fine-grained Category Detection. Ning Zhang, Jeff Donahue, Ross Girshick, Trevor Darrell. ECCV 2014. [PDF]

6 Nov

Kevin Matzen

Intriguing properties of neural networks. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, Rob Fergus. ICLR 2014. [PDF]

Generative Adversarial Networks. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. NIPS 2014. [PDF]

13 Nov


CVPR deadline: 14 Nov

20 Nov

Michael Wilber

Stochastic Triplet Embedding

Van Der Maaten, Laurens, and Kilian Weinberger. "Stochastic triplet embedding." Machine Learning for Signal Processing (MLSP), 2012. [PDF]

Tamuz, Omer, et al. "Adaptively learning the crowd kernel." ICML 2011. [PDF]

4 Dec

Paul Upchurch

Fine-grained categorization

Deng, Krause, Fei-Fei, "Fine-Grained Crowdsourcing for Fine-Grained Recognition." CVPR 2013. [PDF]

Krause, Gebru, Deng, Li, Fei-Fei. "Learning Features and Parts for Fine-Grained Recognition." ICPR 2014.[PDF]

11 Dec

Academic Integrity

This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit must be the student's own work. Violations of the rules will not be tolerated.