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.
Ranked #3 on Edge Detection on BIPED
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
Ranked #18 on 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.
Ranked #8 on Image Retrieval on Flickr30K 1K test
We propose a novel semantic segmentation algorithm by learning a deconvolution network.
Ranked #3 on Curved Text Detection on SCUT-CTW1500
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.
In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions.