%0 Generic %1 cannici2020matrixlstm %A Cannici, Marco %A Ciccone, Marco %A Romanoni, Andrea %A Matteucci, Matteo %D 2020 %K 2020 deep-learning lstm %T Matrix-LSTM: a Differentiable Recurrent Surface for Asynchronous Event-Based Data %U http://arxiv.org/abs/2001.03455 %X Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse representation of the scene. Therefore, to apply standard computer vision algorithms, events need to be integrated into a frame or event-surface. This is usually attained through hand-crafted grids that reconstruct the frame using ad-hoc heuristics. In this paper, we propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells to learn end-to-end a task-dependent event-surfaces. Compared to existing reconstruction approaches, our learned event-surface shows good flexibility and expressiveness improving the baselines on optical flow estimation on the MVSEC benchmark and the state-of-the-art of event-based object classification on the N-Cars dataset.

@misc{cannici2020matrixlstm, abstract = {Dynamic Vision Sensors (DVSs) asynchronously stream events in correspondence of pixels subject to brightness changes. Differently from classic vision devices, they produce a sparse representation of the scene. Therefore, to apply standard computer vision algorithms, events need to be integrated into a frame or event-surface. This is usually attained through hand-crafted grids that reconstruct the frame using ad-hoc heuristics. In this paper, we propose Matrix-LSTM, a grid of Long Short-Term Memory (LSTM) cells to learn end-to-end a task-dependent event-surfaces. Compared to existing reconstruction approaches, our learned event-surface shows good flexibility and expressiveness improving the baselines on optical flow estimation on the MVSEC benchmark and the state-of-the-art of event-based object classification on the N-Cars dataset.}, added-at = {2020-01-14T23:33:54.000+0100}, author = {Cannici, Marco and Ciccone, Marco and Romanoni, Andrea and Matteucci, Matteo}, biburl = {https://www.bibsonomy.org/bibtex/2f34b31ded1c9760586387d5a0e652f7b/analyst}, description = {[2001.03455] Matrix-LSTM: a Differentiable Recurrent Surface for Asynchronous Event-Based Data}, interhash = {5e754a784f1edfc7a5e4feac085083ca}, intrahash = {f34b31ded1c9760586387d5a0e652f7b}, keywords = {2020 deep-learning lstm}, note = {cite arxiv:2001.03455Comment: 13 pages, 6 figures}, timestamp = {2020-01-14T23:33:54.000+0100}, title = {Matrix-LSTM: a Differentiable Recurrent Surface for Asynchronous Event-Based Data}, url = {http://arxiv.org/abs/2001.03455}, year = 2020 }