Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
#12 best model for Real-Time Semantic Segmentation on Cityscapes test
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.
We present a robust and real-time monocular six degree of freedom relocalization system.
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.
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story.