Recurrent Neural Networks for object detection
Recurrent neural networks have been recently used to detect patterns inside an image as well as patterns in between successive images. This has a big importance, since objects in a scene construct a strong context in autonomous driving domain as well as containing strong links with the objects in the following scenes. In this topic, students are expected to work on recurrent neural network architectures used for object detection in autonomous driving domain.
Papers for guidance:
- Broad, Alexander, Michael Jones, and Teng-Yok Lee. "Recurrent Multi-frame Single Shot Detector for Video Object Detection."
- Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in neural information processing systems (pp. 802-810).
- Lu, Y., Lu, C., & Tang, C. K. (2017). Online video object detection using association LSTM. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2344-2352).
- Tripathi, S., Lipton, Z. C., Belongie, S., & Nguyen, T. (2016). Context matters: Refining object detection in video with recurrent neural networks. arXiv preprint arXiv:1607.04648.