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Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection

18 Apr 2019   10:00-11:45

Speaker: Matthieu Cord
Title: Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary
Block for Visual Detection (NIPS 2018)

Abstract: Multi-Task Learning (MTL) is appealing for deep learning regularization. In this paper, we tackle a specific MTL context denoted as
primary MTL, where the ultimate goal is to improve the performance of a given
primary task by leveraging several other auxiliary tasks. Our main methodological contribution is to introduce ROCK, a new generic multi-modal fusion block for deep learning tailored to the primary MTL context. ROCK architecture is based on a residual connection, which makes forward prediction explicitly impacted by the intermediate auxiliary representations. The auxiliary predictor's architecture is also specifically designed to our primary MTL
context, by incorporating intensive pooling operators for maximizing complementarity of intermediate representations. Extensive experiments on NYUv2 dataset (object detection with scene classification, depth prediction, and surface normal estimation as auxiliary tasks) validate the relevance of the approach and its superiority to flat MTL approaches. Our method outperforms state-of-the-art object detection models on NYUv2 dataset by a large margin, and is also able to handle large-scale heterogeneous inputs (real and synthetic images) with missing annotation modalities.

Matthieu Cord, is a professor at Sorbonne University,
http://webia.lip6.fr/~cord/

Place
E-112 (Vyčichlova knihovna), FEL ČVUT, Karlovo náměstí 13, Praha 2
Organizer
Katedra kybernetiky FEL ČVUT
Contact person
doc. Ing. Tomáš Svoboda, Ph.D., svobodat@fel.cvut.cz, 224 357 448
More information
https://cyber.felk.cvut.cz/seminars/?event=1301