Posts Tagged ‘ 2012 ’

NIPS 2012: Multimodal Learning with Deep Boltzmann Machines

This is quite interesting paper from from the Ruslan (Toronto University) ( project page:,  video-lecture [they used Gaussian RBM while making DBM]

Interesting interms of application and how the DBM is used.

multi modal DBM

In this way they can use it given one set of features to find others. I will recommend watching the video lecture.

Co-Segmentation in CVPR 2012

There is the list of Co-Segmentation papers in the CVPR 2012 {if you find someother interesting papers regarding Co-Segmentation please send message or post comment thanks}

  • “Multi-Class Cosegmentation” Armand Joulin, Francis Bach, Jean Ponce
  • “On Multiple Foreground Cosegmentation” Gunhee KIM, Eric P. Xing
  • Higher Level Segmentation: Detecting and Grouping of Invariant Repetitive Patterns” Yunliang Cai, George Baciu: not directly co-segmentation paper but could be seen in that way. 
  • “Random Walks based Multi-Image Segmentation: Quasiconvexity Results and GPU-based Solutions” Maxwell D. Collins, Jia Xu, Leo Grady, Vikas Singh
  • A Hierarchical Image Clustering Cosegmentation FrameworkEdward Kim, Hongsheng Li, Xiaolei Huang
  • Unsupervised Co-segmentation Through Region MatchingJose C. Rubio, Joan Serrat, Antonio López, Nikos Paragios

Some interesting papers to look into

  • Learning Image-Specific Parameters for Interactive Segmentation”  Zhanghui Kuang, Dirk Schnieders, Hao Zhou, Kwan-Yee K. Wong, Yizhou Yu, Bo Peng
  • “Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps” Antonio Hernández-Vela, Nadezhda Zlateva, Alexander Marinov, Miguel Reyes, Petia Radeva, Dimo Dimov, Sergio Escalera
    • {JUST want to read it to See How the Depth Data is Being Used}
  • “Active Learning for Semantic Segmentation with Expected Change”  Alexander Vezhnevets, Joachim M. Buhmann, Vittorio Ferrari
    • Basic Objective is to Learn about the “Active Learning” and how it is used
  • “Semantic Segmentation using Regions and Parts”  Pablo Arbeláez, Bharath Hariharan, Chunhui Gu, Saurabh Gupta, Lubomir Bourdev, Jitendra Malik
  • “Affinity Learning via Self-diffusion for Image Segmentation and Clustering” Bo Wang, Zhuowen Tu
  • “Bag of Textons for Image Segmentation via Soft Clustering and Convex Shift”  Zhiding Yu, Ang Li, Oscar C. Au, Chunjing Xu
  • “Multiple Clustered Instance Learning for Histopathology Cancer Image Classification, Segmentation and Clustering” Yan Xu, Jun-Yan Zhu, Eric Chang, Zhuowen Tu
  • “Maximum Weight Cliques with Mutex Constraints for Video Object Segmentation”  Tianyang Ma, Longin Jan Latecki

CVPR 2012, attending Sebastian Thrun’s talk

{being updated as the talk goes on}

Sebastian is wearing a version of google glasses and showing the Google driver-less car video. The project is interesting but the story behind is much more interesting, they shows video of one of researcher driving it, who is legally blind.

It is showing how the Change Detection and segmentation will become an important problem.

If you guys can find the video link do share, talk is good enough to re listen

Talking about California the motorbikes can pass through very near to the car or inbetween the two cars to overtake them, so it becomes difficult to track them. Especially when they become so near to the car that they appear to be one. Mentions that same kind of problem could be seen in the Kinect and if someone working on the Tracking can solve more efficiently this problem it could be life saving. 

Saying the driverless car is lot safer than the human in case of collision but mentions where there are situations where computer cannot fully understand the situation and reacts improperly.

Showing one more application, used by the motor patrolling personals. So that they don’t have to give much attention to car and can do their job.

Excellent talk, although we were discussing a case of such cars in cities like Lahore, Delhi or even New York. In some crowded cities such situations arise quite commonly where the such “safe” cars might end up in deadlock.