TODO:

  • Weight initializers

    • Logistic Regression init. for softmax layers (esp. for large layers – e.g., bilinear pooling output)
    • Try ConvolutionAware for CNNs?

  • Expand base CNN builders

  • Implement compact bilinear pooling

  • Figure out Buffer bugs when passing covariance_bound to cyvlfeat.gmm.gmm

  • Tests and benchmarks

keras-texture

Implementations of several keras layers, model classes, and other utilities that are useful in constructing models for texture recognition and fine-grained classification problems. It is a work in progress, and the tensorflow backend is required for most functionality.

Develop-mode installable with pip install -e . Root module of package is texture.

Benchmarks

Working on benchmarking models constructed with various texture recognition datasets:

Some fine-grained classification datasets are also of interest, but benchmarking those has a lower priority for me at the moment:

Further Improvements

Encoding

  • Smaller ResNet-based constructors for feature networks

Bilinear

  • Add support for fA and fB to have different input shapes (technically only output shapes need to correspond).
  • Add support for fA and fB to have different output shapes (crop/interpolate/pool to match them)

Would also like to add the matrix square root normalization layer as described in:

@inproceedings{lin2017impbcnn,
    Author = {Tsung-Yu Lin, and Subhransu Maji},
    Booktitle = {British Machine Vision Conference (BMVC)},
    Title = {Improved Bilinear Pooling with CNNs},
    Year = {2017}}

Authors claim this improves accuracy by several % on fine-grained recognition benchmarks.

DEP

  • Utilities for combining a base CNN with Encoding & BilinearModel to create a Deep Encoding Pooling Network.

Indices and tables