ImageNet classification using binary convolutional neural networks.
We present YOLO, a new approach to object detection.
We introduce YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories.
Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research.
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging.
Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.
Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.
Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time.