Emotion recognition by assisted learning with convolutional neural networks

Lang, 1998, Emotion, motivation, and anxiety: brain mechanisms and psychophysiology, Biol. Psychiatry, 44, 1248, 10.1016/S0006-3223(98)00275-3 Joshi, 2011, Aesthetics and emotions in images, Signal Process. Mag. IEEE, 28, 94, 10.1109/MSP.2011.941851 Dellandrea, 2010, Classification of affective semantics in images based on discrete and dimensional models of emotions, 1 Machajdik, 2010, Affective image classification using features inspired by psychology and art theory, 83 Zhao, 2014, Exploring principles-of-art features for image emotion recognition, 47 You, 2015, Robust image sentiment analysis using progressively trained and domain transferred deep networks Mikels, 2005, Emotional category data on images from the international affective picture system, Behav. Res. Methods, 37, 626, 10.3758/BF03192732 You, 2016, Building a large scale dataset for image emotion recognition: the fine print and the benchmark, 308 Guo, 2016, Deep learning for visual understanding: a review, Neurocomputing, 187, 27, 10.1016/j.neucom.2015.09.116 Liu, 2016, A survey of deep neural network architectures and their applications, Neurocomputing Ciresan, 2011, Flexible, high performance convolutional neural networks for image classification, 22, 1237 K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014). Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Zhou, 2013, Active deep learning method for semi-supervised sentiment classification, Neurocomputing, 120, 536, 10.1016/j.neucom.2013.04.017 A. Kendall, V. Badrinarayanan, R. Cipolla, Bayesian segnet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding, arXiv preprint arXiv:1511.02680 (2015). Long, 2015, Fully convolutional networks for semantic segmentation, 3431 Szegedy, 2013, Deep neural networks for object detection, 2553 Ren, 2015, Faster R-CNN: towards real-time object detection with region proposal networks, 91 Wang, 2015, Visual tracking with fully convolutional networks, 3119 Xue, 2016, Tracking people in rgbd videos using deep learning and motion clues, Neurocomputing, 204, 70, 10.1016/j.neucom.2015.06.112 Lu, 2012, On shape and the computability of emotions, 229 Nicolaou, 2011, A multi-layer hybrid framework for dimensional emotion classification, 933 Chan, 2005, Affect-based indexing and retrieval of films, 427 Wang, 2006, Image retrieval by emotional semantics: a study of emotional space and feature extraction, 4, 3534 Borth, 2013, Large-scale visual sentiment ontology and detectors using adjective noun pairs, 223 Arnheim, 1954 Colombo, 1999, Semantics in visual information retrieval, IEEE MultiMed., 38, 10.1109/93.790610 Valdez, 1994, Effects of color on emotions., J. Exp. Psychol. Gen., 123, 394, 10.1037/0096-3445.123.4.394 T. Chen, D. Borth, T. Darrell, S.-F. Chang, Deepsentibank: visual sentiment concept classification with deep convolutional neural networks, arXiv preprint arXiv:1410.8586 (2014). C. Xu, S. Cetintas, K.-C. Lee, L.-J. Li, Visual sentiment prediction with deep convolutional neural networks, arXiv preprint arXiv:1411.5731 (2014). Campos, 2017, From pixels to sentiment: fine-tuning cnns for visual sentiment prediction, Image Vis. Comput., 10.1016/j.imavis.2017.01.011 LeCun, 2010, Convolutional networks and applications in vision., 253 Kavukcuoglu, 2010, Learning convolutional feature hierarchies for visual recognition, 1090 Lang, 1999 Jia, 2014, Caffe: convolutional architecture for fast feature embedding, 675 Yanulevskaya, 2008, Emotional valence categorization using holistic image features, 101