Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection

Abstract

Feature representation and object category classification are two key components of most object detection methods. While significant improvements have been achieved for deep feature representation learning, traditional SVM/softmax classifiers remain the dominant methods for the final object category classification. However, SVM/softmax classifiers lack the capacity of explicitly exploiting the complex structure of deep features, as they are purely discriminative methods. The recently proposed discriminative dictionary pair learning (DPL) model involves a fidelity term to minimize the reconstruction loss and a discrimination term to enhance the discriminative capability of the learned dictionary pair, and thus is appropriate for balancing the representation and discrimination to boost object detection performance. In this paper, we propose a novel object detection system by unifying DPL with the convolutional feature learning. Specifically, we incorporate DPL as a Dictionary Pair Classifier Layer (DPCL) into the deep architecture, and develop an end-to-end learning algorithm for optimizing the dictionary pairs and the neural networks simultaneously. Moreover, we design a multi-task loss for guiding our model to accomplish the three correlated tasks: objectness estimation, categoryness computation, and bounding box regression. From the extensive experiments on PASCAL VOC 2007/2012 benchmarks, our approach demonstrates the effectiveness to substantially improve the performances over the popular existing object detection frameworks (e.g., R-CNN [13] and FRCN [12]), and achieves new state-of-the-arts.

DOI: 10.1109/CVPR.2016.235

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Cite this paper

@article{Wang2016DictionaryPC, title={Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection}, author={Keze Wang and Liang Lin and Wangmeng Zuo and Shuhang Gu and Lei Zhang}, journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2016}, pages={2138-2146} }