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The news media shape public opinion, and often, the visual bias they contain is evident for human observers.
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Software Framework for Topic Modelling with Large Corpora
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Unsupervised discovery of facial events
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Predicting the political alignment of twitter users
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A machine learning approach to twitter user classification
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What makes paris look like paris?
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Studies: Conservatives are from mars, liberals are from venus, February 2012
T. B. Edsall · 2012
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The visual image and the political image: A review of visual communication research in the field of political communication
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Classifying political orientation on twitter: It’s not easy!
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The role of the media in the construction of public belief and social change
C. Happer and G. Philo · 2013
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Style-aware mid-level representation for discovering visual connections in space and time
Y. Jae Lee, A. A. Efros, and M. Hebert · 2013
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Content extraction using diverse feature sets
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Linguistic models for analyzing and detecting biased language
M. Recasens, C. Danescu-Niculescu-Mizil, and D. Jurafsky · 2013
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Red brain, blue brain: Evaluative processes differ in democrats and republicans
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Learning to rank using privileged information
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Echo chamber or public sphere? predicting political orientation and measuring political homophily in twitter using big data
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Visual persuasion: Inferring communicative intents of images
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Distributed representations of sentences and documents
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Inferring user political preferences from streaming communications
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Distributed representations of sentences and documents
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Testing and comparing computational approaches for identifying the language of framing in political news
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Webly supervised learning of convolutional networks
X. Chen and A. Gupta · 2015
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Long-term recurrent convolutional networks for visual recognition and description
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell · 2015
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Self-paced curriculum learning
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Automated facial trait judgment and election outcome prediction: Social dimensions of face
J. Joo, F. F. Steen, and S.-C. Zhu · 2015
Self-supervised learning of visual features through embedding images into text topic spaces
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Automatic understanding of image and video advertisements
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Mining mid-level visual patterns with deep cnn activations
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The image is the message: Instagram marketing and the 2016 presidential primary season
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Automatic differentiation in pytorch
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Areas of attention for image captioning
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Is object localization for free?-weakly-supervised learning with convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2015
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Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, and C. H. Lampert · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Learning using privileged information: similarity control and knowledge transfer
V. Vapnik and R. Izmailov · 2015
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Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
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Weakly supervised action learning with rnn based fine-to-coarse modeling
A. Richard, H. Kuehne, and J. Gall · 2017
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Unsupervised part learning for visual recognition
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Captioning images with diverse objects
S. Venugopalan, L. Anne Hendricks, M. Rohrbach, R. Mooney, T. Darrell, and K. Saenko · 2017
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Untrimmednets for weakly supervised action recognition and detection
L. Wang, Y. Xiong, D. Lin, and L. Van Gool · 2017
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How polarized have we become? a multimodal classification of trump followers and clinton followers
Y. Wang, Y. Feng, Z. Hong, R. Berger, and J. Luo · 2017
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Feedback networks
A. R. Zamir, T.-L. Wu, L. Sun, W. B. Shen, B. E. Shi, J. Malik, and S. Savarese · 2017
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Curriculum domain adaptation for semantic segmentation of urban scenes
Y. Zhang, P. David, and B. Gong · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang · 2018
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Face verification from depth using privileged information
G. Borghi, S. Pini, F. Grazioli, R. Vezzani, and R. Cucchiara · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
L. Jiang, Z. Zhou, T. Leung, L.-J. Li, and L. Fei-Fei · 2018
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Deep learning under privileged information using heteroscedastic dropout
J. Lambert, O. Sener, and S. Savarese · 2018
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Patternnet: Visual pattern mining with deep neural network
H. Li, J. G. Ellis, L. Zhang, and S.-F. Chang · 2018
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TextBoxes++: A single-shot oriented scene text detector
B. S. Minghui Liao and X. Bai · 2018
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Big political data
T. Peck and N. Boutelier · 2018
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Same candidates, different faces: Uncovering media bias in visual portrayals of presidential candidates with computer vision
Y. Peng · 2018
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Persuasive faces: Generating faces in advertisements
C. Thomas and A. Kovashka · 2018
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Ts2c: Tight box mining with surrounding segmentation context for weakly supervised object detection
Y. Wei, Z. Shen, B. Cheng, H. Shi, J. Xiong, J. Feng, and T. Huang · 2018
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Interpreting the rhetoric of visual advertisements
K. Ye, N. Honarvar Nazari, J. Hahn, Z. Hussain, M. Zhang, and A. Kovashka · 2019
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Cap2det: Learning to amplify weak caption supervision for object detection
K. Ye, M. Zhang, A. Kovashka, W. Li, D. Qin, and J. Berent · 2019
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