The presentations are 20 min long, including questions. So plan for 15 minutes to present and 5 min for questions. You should convey the major idea in the paper, you do not (and should not) explain every single little detail.
Some useful points:
- Give the paper title and the authors on the first slide
- Clearly state the problem the paper tackles
- Explain the prior work on this problem and its limitation (from the prior work section in the paper
- Briefly state which limitations of the prior work the paper proposes to solve and which computational techniques will be used
- Explain the main technical details (the "meat" of the paper)
- Experimental results
- Conclusions (which issues are left to solve, future work, etc...)
- Try to use as many pictures as possible instead of text/formulas
Presentations on December 3
-
- Miriam: "Face Recognition with Support Vector Machinesand 3D Head Models" by
J. Huang, V. Blanz, B. Heisele
- Jun: "Co-Tracking Using Semi-Supervised Support Vector Machines" by
F. Tang, S. Brennan, Q. Zhao, H. Tao
- Upeka:
"On the Design of Cascades of Boosted Ensembles for Face Detection"
by Charles Brubaker, J. Wu, J. Sun, M. D. Mullin and J. M. Rehg.
- Katarina
"Automatic Design of Cascaded classifiers" by E. Grossmann
- Rui:
"Object Recognition as Maching Translation: Learning a Lexicon for a Fixed Image Vocabulary"
by P. Duygulu, K. Barnard, J. Freitas, D. Forsyth
- Paulina: "FaceTracer: A Search Engine for Large Collections
of Images with Faces" by N. Kumar, P. Belhumeur, S. Nayar
Presentations on December 10
-
- Maria: "Automatic Image Orientation Detection" by
A. Vailaya, H. Zhang, C. Yang, F. Liu, A. Jain
- Rachita: "Training Support Vector Machines: an Application to Face Detection"
by E. Osuna, R. Freund, F. Girosi.
- Sameh:
"Geometric Context from a Single Image" by D. Hoiem, A. Efros, M. Hebert
- Paria: "Photo and Video Quality Evaluation:
Focusing on the Subject" by Y. Luo and X. Tang
- Vida: "Learning Object Detection from a Small Number of Examples: the Importance
of Good Features" by K. Levi and Y. Weiss.
- Hossam: "Distance Metric Learning for Large Margin Nearest Neighbor
Classification" by K. Weinberger, J. Blitzer, L. Saul.
Presentations at a later date: