• Publications
  • Influence
You Only Look Once: Unified, Real-Time Object Detection
Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork. Expand
YOLOv3: An Incremental Improvement
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but moreExpand
YOLO9000: Better, Faster, Stronger
YOLO9000, a state-of-the-art, real-time object detection system that can detect over 9000 object categories, is introduced and a method to jointly train on object detection and classification is proposed, both novel and drawn from prior work. Expand
Bidirectional Attention Flow for Machine Comprehension
The BIDAF network is introduced, a multi-stage hierarchical process that represents the context at different levels of granularity and uses bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. Expand
Unsupervised Deep Embedding for Clustering Analysis
Deep Embedded Clustering is proposed, a method that simultaneously learns feature representations and cluster assignments using deep neural networks and learns a mapping from the data space to a lower-dimensional feature space in which it iteratively optimizes a clustering objective. Expand
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
The Binary-Weight-Network version of AlexNet is compared with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than \(16\,\%\) in top-1 accuracy. Expand
Describing objects by their attributes
We propose to shift the goal of recognition from naming to describing. Doing so allows us not only to name familiar objects, but also: to report unusual aspects of a familiar object (“spotty dog”,Expand
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
This work proposes a novel Hollywood in Homes approach to collect data, collecting a new dataset, Charades, with hundreds of people recording videos in their own homes, acting out casual everyday activities, and evaluates and provides baseline results for several tasks including action recognition and automatic description generation. Expand
AI2-THOR: An Interactive 3D Environment for Visual AI
AI2-THOR consists of near photo-realistic 3D indoor scenes, where AI agents can navigate in the scenes and interact with objects to perform tasks and facilitate building visually intelligent models. Expand
Target-driven visual navigation in indoor scenes using deep reinforcement learning
This paper proposes an actor-critic model whose policy is a function of the goal as well as the current state, which allows better generalization and proposes the AI2-THOR framework, which provides an environment with high-quality 3D scenes and a physics engine. Expand