29 citations · 108 across the 22 of their papers we have counts for
6 papers · 1 filter
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (…
Query-Conditioned Three-Player Adversarial Network for Video Summarization
Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang +2
Video summarization plays an important role in video understanding by selecting key frames/shots. Traditionally, it aims to find the most representative and diverse contents in a v…
Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful
Rajesh Chidambaram, Michael Kampffmeyer, Willie Neiswanger +3
Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization b…
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang +3
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be a…
Rethinking Knowledge Graph Propagation for Zero-Shot Learning
Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang +3
Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the grap…
Dilated Temporal Relational Adversarial Network for Generic Video Summarization
Yujia Zhang, Michael Kampffmeyer, Xiaodan Liang +3
The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summa…