<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yemin Shi</style></author><author><style face="normal" font="default" size="100%">YongHong Tian</style></author><author><style face="normal" font="default" size="100%">Yaowei Wang</style></author><author><style face="normal" font="default" size="100%">Wei Zeng</style></author><author><style face="normal" font="default" size="100%">Tiejun Huang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning long-term dependencies for action recognition with abiologically-inspired deep network</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://openaccess.thecvf.com/content_ICCV_2017/papers/Shi_Learning_Long-Term_Dependencies_ICCV_2017_paper.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Venice, Italy</style></pub-location><pages><style face="normal" font="default" size="100%">716-725</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is typically composed of both feedforward and feedback connections. To address this problem, this paper proposes a biologically-inspired deep network, called shuttleNet. Technologically, the shuttleNet consists of several processors, each of which is a GRU while associated with multiple groups of hidden states. Unlike traditional RNNs, all processors inside shuttleNet are loop connected to mimic the brain's feedforward and feedback connections, in which they are shared across multiple pathways in the loop connection. Attention mechanism is then employed to select the best information flow pathway. Extensive experiments conducted on two benchmark datasets (i.e UCF101 and HMDB51) show that we can beat state-of-the-art methods by simply embedding shuttleNet into a CNN-RNN framework.&lt;/p&gt;</style></abstract></record></records></xml>