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Late Fusion via Subspace Search with Consistency Preservation

We propose an late fusion algorithm, i.e., Hard-rank Constraint Matrix Factorization-based method (HCMF). We experiment on classification tasks, e.g., UCF-101, CIFAR-10, CIFAR-100, OXFORD-IIIT-Pet, PASCAL VOC 2007.

Table 1. The Mean Accuracy on 5 Real-World Datasets.


If you find this project help your research, please cite:

  title  = {Late Fusion via Subspace Search with Consistency Preservation},
  author = {Dong, Xuanyi and Yan, Yan and Tan, Mingkui and Yang, Yi and Tsang, Ivor W},
  journal= {IEEE Transactions on Image Processing (TIP)},
  year   = {2019}, 
  volume = {28}, 
  number = {1}, 
  pages  = {518-528},
  doi    = {10.1109/TIP.2018.2867747}, 
  ISSN   = {1057-7149},
  month  = {Jan}


To generate the figures used in this paper (synthetic experiments), please use the following code:

cd codes/visualization/
warp_vis(250, 15, 20, [0.3], 'results/visual/')


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