Deep Learning in Computer Vision Papers

In this post you will find some papers that are quite new but really worth reading. The application range of these papers is object detection, object segmentation, and visual tracking.

Object Detection

1. C. Szegedy, A. Toshev, D. Erhan (2013). “Deep Neural Networks for Object Detection”. Advances in Neural Information Processing Systems. pdf

2. R. Girshick (2015). “Fast R-CNN”. IEEE International Conference on Computer Vision. pdf

3. S. Ren et al. (2015). “Faster R-CNN: Towards real-time object detection with region proposal networks”. Advances in Neural Information Processing Systems. pdf

4. J. Redmon et al. (2016). “You onle look once: Unified, real-time object detection”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf

5. W. Liu et al. (2016). “SSD: Single Shot MultiBox Detector”. Lecture Notes in Computer Science. pdf

6. J. Dai et al. (2016). “R-FCN: Object Detection via Region-based Fully Convolutional Networks”. Advances in Neural Information Processing Systems. pdf

7. K. He et al. (2017). “Mask R-CNN”. Proceedings of the IEEE International Conference on Computer Vision. pdf

Object Segmentation

1. J. Long, E. Shelhamer, T. Darrell (2015). “Fully Convolutional Networks for Semantic Segmentation”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pdf

2. L.C. Chen et al. (2017). “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs”. IEEE Transactions on Pattern Analysis and Machine Intelligence. pdf

Object Tracking

1. L. Wang et al. (2015). “Visual Tracking with Fully Convolutional Networks”. Proceedings of the IEEE International Conference on Computer Vision. pdf

2. L. Bertinetto et al. (2016). “Fully-Convolutional Siamese Networks for Object Tracking”. European conference on computer vision. pdf