Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CVPR.2016.235 | 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) |
Field | DocType | Volume |
Computer science,Convolutional neural network,Artificial intelligence,Artificial neural network,Classifier (linguistics),Discriminative model,Computer vision,Object detection,Softmax function,Pattern recognition,Support vector machine,Machine learning,Feature learning | Conference | 2016 |
Issue | ISSN | Citations |
1 | 1063-6919 | 8 |
PageRank | References | Authors |
0.50 | 16 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keze Wang | 1 | 281 | 14.64 |
| Liang Lin | 2 | 3007 | 151.07 |
| Wangmeng Zuo | 3 | 3833 | 173.11 |
| Shuhang Gu | 4 | 701 | 28.25 |
| Lei Zhang | 5 | 16326 | 543.99 |