Misc,
CoCoNet: A Collaborative Convolutional Network.
(2019)cite arxiv:1901.09886Comment: submitted to: Pattern Recognition Letters.
Abstract
We present an end-to-end CNN architecture for fine-grained visual recognition
called Collaborative Convolutional Network (CoCoNet). The network uses a
collaborative filter after the convolutional layers to represent an image as an
optimal weighted collaboration of features learned from training samples as a
whole rather than one at a time. This gives CoCoNet more power to encode the
fine-grained nature of the data with limited samples in an end-to-end fashion.
We perform a detailed study of the performance with 1-stage and 2-stage
transfer learning and different configurations with benchmark architectures
like AlexNet and VggNet. The ablation study shows that the proposed method
outperforms its constituent parts considerably and consistently. CoCoNet also
outperforms the baseline popular deep learning based fine-grained recognition
method, namely Bilinear-CNN (BCNN) with statistical significance. Experiments
have been performed on the fine-grained species recognition problem, but the
method is general enough to be applied to other similar tasks. Lastly, we also
introduce a new public dataset for fine-grained species recognition, that of
Indian endemic birds and have reported initial results on it. The training
metadata and new dataset are available through the corresponding author.
