We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning.
Optical flow estimation has not been among the tasks where CNNs were successful.
Given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence.
We propose a novel semantic segmentation algorithm by learning a deconvolution network.