5 papers
STrack: Self-supervised Tracking with Soft Assignment Flow
Fatemeh Azimi, Fahim Mannan, Felix Heide
In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as lear…
Spatial Transformer Networks for Curriculum Learning
Fatemeh Azimi, Jean-Francois Jacques Nicolas Nies, Sebastian Palacio +3
Curriculum learning is a bio-inspired training technique that is widely adopted to machine learning for improved optimization and better training of neural networks regarding the c…
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Fatemeh Azimi, Federico Raue, Joern Hees +1
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance. In this work, we combine the idea of ST…
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
Fatemeh Azimi, Stanislav Frolov, Federico Raue +2
One-shot Video Object Segmentation~(VOS) is the task of pixel-wise tracking an object of interest within a video sequence, where the segmentation mask of the first frame is given a…
Revisiting Sequence-to-Sequence Video Object Segmentation with Multi-Task Loss and Skip-Memory
Fatemeh Azimi, Benjamin Bischke, Sebastian Palacio +3
Video Object Segmentation (VOS) is an active research area of the visual domain. One of its fundamental sub-tasks is semi-supervised / one-shot learning: given only the segmentatio…