42 citations · 248 across the 35 of their papers we have counts for
8 papers · 1 filter
Learning to Forecast Videos of Human Activity with Multi-granularity Models and Adaptive Rendering
Mengyao Zhai, Jiacheng Chen, Ruizhi Deng +3
We propose an approach for forecasting video of complex human activity involving multiple people. Direct pixel-level prediction is too simple to handle the appearance variability i…
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
Frederick Tung, Srikanth Muralidharan, Greg Mori
When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the spec…
Learning to Learn from Noisy Web Videos
Serena Yeung, Vignesh Ramanathan, Olga Russakovsky +3
Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training v…
Active Learning for Structured Prediction from Partially Labeled Data
Mehran Khodabandeh, Zhiwei Deng, Mostafa S. Ibrahim +2
We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or v…
Learning Person Trajectory Representations for Team Activity Analysis
Nazanin Mehrasa, Yatao Zhong, Frederick Tung +2
Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics. We propose an end-…
Hierarchical Label Inference for Video Classification
Nelson Nauata, Jonathan Smith, Greg Mori
Videos are a rich source of high-dimensional structured data, with a wide range of interacting components at varying levels of granularity. In order to improve understanding of unc…