Search Results for author:
Found 7 papers, 1 papers with code
Learning Variations in Human Motion via Mix-and-Match Perturbation
In this paper, we introduce an approach to stochastically combine the root of variations with previous pose information, which forces the model to take the noise into account.
VIENA2: A Driving Anticipation Dataset
Action anticipation is critical in scenarios where one needs to react before the action is finalized.
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation
Our approach builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently.
Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation
Our experiments demonstrate the benefits of our classifier heatmaps and of our two-stream architecture on challenging urban scene datasets and on the YouTube-Objects benchmark, where we obtain state-of-the-art results.
Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation
We then show how to obtain multi-class masks by the fusion of foreground/background ones with information extracted from a weakly-supervised localization network.
Encouraging LSTMs to Anticipate Actions Very Early
In contrast to the widely studied problem of recognizing an action given a complete sequence, action anticipation aims to identify the action from only partially available videos.
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation
We outperform the state-of-the-art methods that, as us, rely only on RGB frames as input for both action recognition and anticipation.
