Towards Solving Abstract Tasks Using Convolutional Neural Networks
Kuan–Chuan Peng · eCommons (Cornell University) · 2016
tasks are the tasks relating to or involving general ideas or qualities rather than specific people, objects, or actions.Recently, abstract tasks such as artistic style classification and memorability prediction have been receiving increasing attention in the computer vision community.Previous works related to abstract tasks mainly reply on standard handcrafted features without directly learning the features from the training data.In this thesis, we explore the efficacy of using convolutional neural networks (CNN) which learn the features tailored for each abstract task.Predicting emotion distributions and predicting emotion stimuli maps are the first two abstract tasks we work on.In both tasks, we build associated databases and show that CNN-based approaches can predict more accurate emotion distributions and emotion stimuli maps compared with the methods used in the previous works.Given the encouraging results in the emotion-related tasks, we apply CNN to eight different abstract tasks proposed recently in computer vision, showing that CNN-based approaches can outperform the state-of-the-art performance reported in the previous works.In addition to the traditional CNN framework, we propose using multi-task, multi-depth, and multi-scale CNN features to further improve the performance in abstract tasks.Multi-task features incorporate the features learned from the training data of other tasks.Multi-depth features consist of the features learned by different neural network architectures, but multi-scale features are formed by the features learned from the augmented training data in different scales.The experimental results show that all the three proposed CNN features outperform the traditional CNN framework.Furthermore, we train another fully connected networks to fuse our proposed CNN features.The fused features achieve better performance than using each of our proposed features.