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Found 5 papers, 2 papers with code
S$^3$Track: Self-supervised Tracking with Soft Assignment Flow
With this training approach in hand, we develop an appearance-based model for learning instance-aware object features used to construct a cost matrix based on the pairwise distances between the object features.
Spatial Transformer Networks for Curriculum Learning
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 convergence rate or obtained accuracy.
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance.
Hybrid-S2S: Video Object Segmentation with Recurrent Networks and Correspondence Matching
In this work, we study an RNN-based architecture and address some of these issues by proposing a hybrid sequence-to-sequence architecture named HS2S, utilizing a dual mask propagation strategy that allows incorporating the information obtained from correspondence matching.
