Paper Info
Title
Deep Multi-task Learning with Label Correlation Constraint for Video Concept Detection.
Abstract
In this work we propose a method that integrates multi-task learning (MTL) and deep learning. Our method appends a MTL-like loss to a deep convolutional neural network, in order to learn the relations between tasks together at the same time, and also incorporates the label correlations between pairs of tasks. We apply the proposed method on a transfer learning scenario, where our objective is to fine-tune the parameters of a network that has been originally trained on a large-scale image dataset for concept detection, so that it be applied on a target video dataset and a corresponding new set of target concepts. We evaluate the proposed method for the video concept detection problem on the TRECVID 2013 Semantic Indexing dataset. Our results show that the proposed algorithm leads to better concept-based video annotation than existing state-of-the-art methods.
Year
DOI
Venue
2016
10.1145/2964284.2967271
ACM Multimedia
Field
DocType
Citations 
Computer science,Convolutional neural network,Transfer of learning,Video annotation,Search engine indexing,Artificial intelligence,Deep learning,Computer vision,Multi-task learning,Pattern recognition,TRECVID,Correlation,Machine learning
Conference
6
PageRank 
References 
Authors
0.42
17
3
Name
Order
Citations
PageRank
Fotini Markatopoulou1355.93
Vasileios Mezaris280381.40
Ioannis Patras31960123.15