Visual sentiment analysis via deep multiple clustered instance learning

[1]  Feng Zheng,et al.  A Multi-Attentive Pyramidal Model for Visual Sentiment Analysis , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[2]  Zhi-Hua Zhou,et al.  Solving multi-instance problems with classifier ensemble based on constructive clustering , 2007, Knowledge and Information Systems.

[3]  Chong-Wah Ngo,et al.  Deep Multimodal Learning for Affective Analysis and Retrieval , 2015, IEEE Transactions on Multimedia.

[4]  Eun Yi Kim,et al.  Discovering visual features for recognizing user's sentiments in social images , 2016, 2016 International Conference on Big Data and Smart Computing (BigComp).

[5]  Feiran Huang,et al.  Image-text sentiment analysis via deep multimodal attentive fusion , 2019, Knowl. Based Syst..

[6]  Tsuhan Chen,et al.  Where do emotions come from? Predicting the Emotion Stimuli Map , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[7]  Min Xu,et al.  Multi-scale blocks based image emotion classification using multiple instance learning , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[8]  Yue Gao,et al.  Predicting Microblog Sentiments via Weakly Supervised Multimodal Deep Learning , 2018, IEEE Transactions on Multimedia.

[9]  Jiebo Luo,et al.  Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia , 2016, WSDM.

[10]  Quoc-Tuan Truong,et al.  Visual Sentiment Analysis for Review Images with Item-Oriented and User-Oriented CNN , 2017, ACM Multimedia.

[11]  Zhuanghui Wu,et al.  Visual Sentiment Prediction with Attribute Augmentation and Multi-attention Mechanism , 2020, Neural Processing Letters.

[12]  Shin'ichi Satoh,et al.  Image sentiment analysis using latent correlations among visual, textual, and sentiment views , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Min Xu,et al.  Multi-level region-based Convolutional Neural Network for image emotion classification , 2019, Neurocomputing.

[14]  Xavier Giró-i-Nieto,et al.  From pixels to sentiment: Fine-tuning CNNs for visual sentiment prediction , 2016, Image Vis. Comput..

[15]  Yue Gao,et al.  Exploring Principles-of-Art Features For Image Emotion Recognition , 2014, ACM Multimedia.

[16]  Wenyu Liu,et al.  Revisiting multiple instance neural networks , 2016, Pattern Recognit..

[17]  Tao Chen,et al.  Assistive Image Comment Robot—A Novel Mid-Level Concept-Based Representation , 2015, IEEE Transactions on Affective Computing.

[18]  Hao Yang,et al.  OutfitNet: Fashion Outfit Recommendation with Attention-Based Multiple Instance Learning , 2020, WWW.

[19]  Jiebo Luo,et al.  Sentribute: image sentiment analysis from a mid-level perspective , 2013, WISDOM '13.

[20]  Liang Wang,et al.  WSCNet: Weakly Supervised Coupled Networks for Visual Sentiment Classification and Detection , 2020, IEEE Transactions on Multimedia.

[21]  Mingxuan Sun,et al.  A Deep Multi-Modal CNN for Multi-Instance Multi-Label Image Classification , 2018, IEEE Transactions on Image Processing.

[22]  Rongrong Ji,et al.  Large-scale visual sentiment ontology and detectors using adjective noun pairs , 2013, ACM Multimedia.

[23]  Paul L. Rosin,et al.  Visual Sentiment Prediction Based on Automatic Discovery of Affective Regions , 2018, IEEE Transactions on Multimedia.

[24]  Zhuowen Tu,et al.  Weakly supervised histopathology cancer image segmentation and classification , 2014, Medical Image Anal..

[25]  Jufeng Yang,et al.  Discovering affective regions in deep convolutional neural networks for visual sentiment prediction , 2016, 2016 IEEE International Conference on Multimedia and Expo (ICME).