There is a similar post at Yaroslav Bulato's blog.
- Multiple Kernel Learning and the SMO Algorithm by Vishwanathan, Sun, Ampornput and Varma (pdf) . Code here.
Efficient training of p-norm MKL using Sequential Minimal Optimization.
- Kernel Descriptors for Visual Recognition by
Liefeng Bo, Xiaofeng Ren, Dieter Fox (pdf)
A general setting to design image patch descriptors using kernels. The proposed kernel is demonstrated to outperform SIFT.
- A Theory of Multiclass Boosting by Indraneel Mukherjee, Robert Schapire (pdf)
Title says it all.
- Deep Coding Network by Yuanqing Lin, Zhang Tong, Shenghuo Zhu, Kai Yu (pdf)
This is a continuation of the work on Linear Coordinate Coding (pdf) which won the image net callenge.
- Tree-Structured Stick Breaking for Hierarchical Data by Ryan Adams, Zoubin Ghahramani, Michael Jordan (pdf)
This is work on hierarchical topic models using trees. I must confess I have not ultimately understood this work but I find it highly interesting. In contrast to nested Chinese Restaurant Processes(pdf ), samples from the distribution can also be inner nodes.
- Segmentation as Maximum-Weight Independent Set by William Brendel, Sinisa Todorovic (pdf)
This work on image segmentation formulates consistent segmentations using graphs and introduces a new optimization algorithm to approximately find independent sets.
This work is quite similar to a tech report from Adrian Ion (pdf) but focuses more on optimization than on the potentials for the graph.
- Simultaneous Object Detection and Ranking with Weak Supervision by Matthew Blaschko, Andrea Vedaldi, Andrew Zisserman (pdf)
A multiple instance approach to finding objects in weakly labeled images using something like a "latent SVR".
- Convex Multiple-Instance Learning by Estimating Likelihood Ratio by Fuxin Li, Cristian Sminchisescu (pdf)
Multiple instance learning using a novel convex formulation. This is a promising direction and hopefully will be applied soon to object segmentation using "Object Recognition as Ranking Holistic Figure-Ground Hypotheses" (pdf) by the same group.