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Residual learning has recently surfaced as an effective means of constructingvery deep neural networks for object recognition. However, current incarnationsof residual networks do not allow for the modeling and integration of complexrelations between closely coupled recognition tasks or across domains. Suchproblems are often encountered in multimedia applications involving large-scalecontent recognition. We propose a novel extension of residual learning for deepnetworks that enables intuitive learning across multiple related tasks usingcross-connections called cross-residuals. These cross-residuals connections canbe viewed as a form of in-network regularization and enables greater networkgeneralization. We show how cross-residual learning (CRL) can be integrated inmultitask networks to jointly train and detect visual concepts across severaltasks. We present a single multitask cross-residual network with >40% lessparameters that is able to achieve competitive, or even better, detectionperformance on a visual sentiment concept detection problem normally requiringmultiple specialized single-task networks. The resulting multitaskcross-residual network also achieves better detection performance by about10.4% over a standard multitask residual network without cross-residuals witheven a small amount of cross-task weighting.

Deep Cross Residual Learning for Multitask Visual Recognition