@conference {cMontes,
	title = {Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks},
	booktitle = {1st NIPS Workshop on Large Scale Computer Vision Systems 2016},
	year = {2016},
	month = {12/2016},
	abstract = {<p>This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at:\&nbsp;<a href="https://imatge-upc.github.io/activitynet-2016-cvprw/">https://imatge-upc.github.io/activitynet-2016-cvprw/</a></p>},
	author = {Montes, Alberto and Amaia Salvador and Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto}
}
