Fetching the paper…
Reading the bibliography…
There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action.
Decoding advertisements
J. Williamson · 1978
Earlier work this paper cites.
Culture and the ad: Exploring otherness in the world of advertising
W. M. O’Barr · 1994
Earlier work this paper cites.
Practices of looking: An introduction to visual culture
M. Sturken, L. Cartwright, and M. Sturken · 2001
Earlier work this paper cites.
Media semiotics: An introduction
J. Bignell · 2002
Earlier work this paper cites.
The visual culture reader
N. Mirzoeff · 2002
Earlier work this paper cites.
Shot partitioning based recognition of tv commercials
J. M. Sánchez, X. Binefa, and J. Vitrià · 2002
Earlier work this paper cites.
Messages, signs, and meanings: A basic textbook in semiotics and communication
M. Danesi · 2004
Earlier work this paper cites.
The advertising research handbook
C. E. Young · 2005
Earlier work this paper cites.
Finding and identifying unknown commercials using repeated video sequence detection
J. M. Gauch and A. Shivadas · 2006
Earlier work this paper cites.
How to capture the heart? Reviewing 20 years of emotion measurement in advertising
K. Poels and S. Dewitte · 2006
Earlier work this paper cites.
Visual rhetoric: A reader in communication and American culture
L. C. Olson, C. A. Finnegan, and D. S. Hope · 2008
Earlier work this paper cites.
Describing objects by their attributes
A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth · 2009
Earlier work this paper cites.
Learning to detect unseen object classes by between-class attribute transfer
C. Lampert, H. Nickisch, and S. Harmeling · 2009
Earlier work this paper cites.
Learning to recognize objects from unseen modalities
C. M. Christoudias, R. Urtasun, M. Salzmann, and T. Darrell · 2010
Earlier work this paper cites.
Every picture tells a story: Generating sentences from images
A. Farhadi, M. Hejrati, M. A. Sadeghi, P. Young, C. Rashtchian, J. Hockenmaier, and D. Forsyth · 2010
Earlier work this paper cites.
Affective image classification using features inspired by psychology and art theory
J. Machajdik and A. Hanbury · 2010
Earlier work this paper cites.
Implementation and benchmarking of perceptual image hash functions
C. Zauner · 2010
Earlier work this paper cites.
Describable visual attributes for face verification and image search
N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar · 2011
Earlier work this paper cites.
Signs of life in the USA: Readings on popular culture for writers
S. Maasik and J. Solomon · 2011
Earlier work this paper cites.
Relative attributes
D. Parikh and K. Grauman · 2011
Earlier work this paper cites.
Image ranking and retrieval based on multi-attribute queries
B. Siddiquie, R. S. Feris, and L. S. Davis · 2011
Earlier work this paper cites.
Discovering the thematic object in commercial videos
G. Zhao, J. Yuan, J. Xu, and Y. Wu · 2011
Earlier work this paper cites.
Visual appearance of display ads and its effect on click through rate
J. Azimi, R. Zhang, Y. Zhou, V. Navalpakkam, J. Mao, and X. Fern · 2012
Earlier work this paper cites.
Multimedia features for click prediction of new ads in display advertising
H. Cheng, R. v. Zwol, J. Azimi, E. Manavoglu, R. Zhang, Y. Zhou, and V. Navalpakkam · 2012
Earlier work this paper cites.
What makes Paris look like Paris?
C. Doersch, S. Singh, A. Gupta, J. Sivic, and A. Efros · 2012
Earlier work this paper cites.
Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
Earlier work this paper cites.
Imagesense: towards contextual image advertising
T. Mei, L. Li, X.-S. Hua, and S. Li · 2012
Earlier work this paper cites.
Discriminative spatial saliency for image classification
G. Sharma, F. Jurie, and C. Schmid · 2012
Earlier work this paper cites.
Ucf101: A dataset of 101 human actions classes from videos in the wild
K. Soomro, A. R. Zamir, and M. Shah · 2012
Cited alongside, same era.
Label-embedding for attribute-based classification
Z. Akata, F. Perronnin, Z. Harchaoui, and C. Schmid · 2013
Cited alongside, same era.
Large-scale visual sentiment ontology and detectors using adjective noun pairs
D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang · 2013
Cited alongside, same era.
Recognizing image style
S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, and H. Winnemoeller · 2013
Cited alongside, same era.
Babytalk: Understanding and generating simple image descriptions
G. Kulkarni, V. Premraj, V. Ordonez, S. Dhar, S. Li, Y. Choi, A. C. Berg, and T. Berg · 2013
Cited alongside, same era.
Style-aware mid-level representation for discovering visual connections in space and time
Ask your neurons: A neural-based approach to answering questions about images
M. Malinowski, M. Rohrbach, and M. Fritz · 2015
Later among the works it cites.
A mixed bag of emotions: Model, predict, and transfer emotion distributions
K.-C. Peng, A. Sadovnik, A. Gallagher, and T. Chen · 2015
Later among the works it cites.
Exploring models and data for image question answering
M. Ren, R. Kiros, and R. Zemel · 2015
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Later among the works it cites.
Learning spatiotemporal features with 3d convolutional networks
D. Tran, L. Bourdev, R. Fergus, L. Torresani, and M. Paluri · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. J. Lee, A. Efros, M. Hebert, et al · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
Cited alongside, same era.
Linguistic regularities in continuous space word representations
T. Mikolov, W.-t. Yih, and G. Zweig · 2013
Cited alongside, same era.
Large-scale multimedia content analysis using scientific workflows
R. J. Sethi, Y. Gil, H. Jo, and A. Philpot · 2013
Cited alongside, same era.
Decorrelating semantic visual attributes by resisting the urge to share
D. Jayaraman, F. Sha, and K. Grauman · 2014
Cited alongside, same era.
Visual persuasion: Inferring communicative intents of images
J. Joo, W. Li, F. F. Steen, and S.-C. Zhu · 2014
Cited alongside, same era.
Predicting viewer perceived emotions in animated gifs
B. Jou, S. Bhattacharya, and S.-F. Chang · 2014
Cited alongside, same era.
Later among the works it cites.
Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
Later among the works it cites.
Show and tell: A neural image caption generator
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan · 2015
Later among the works it cites.
Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Later among the works it cites.
Learning aligned cross-modal representations from weakly aligned data
L. Castrejón, Y. Aytar, C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
Later among the works it cites.
We are humor beings: Understanding and predicting visual humor
A. Chandrasekaran, A. Kalyan, S. Antol, M. Bansal, D. Batra, C. L. Zitnick, and D. Parikh · 2016
Later among the works it cites.
3D shape attributes
D. F. Fouhey, A. Gupta, and A. Zisserman · 2016
Later among the works it cites.
Deep reconstruction-classification networks for unsupervised domain adaptation
M. Ghifary, W. B. Kleijn, M. Zhang, D. Balduzzi, and W. Li · 2016
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Later among the works it cites.
End-to-end saliency mapping via probability distribution prediction
S. Jetley, N. Murray, and E. Vig · 2016
Later among the works it cites.
Complura: Exploring and leveraging a large-scale multilingual visual sentiment ontology
H. Liu, B. Jou, T. Chen, M. Topkara, N. Pappas, M. Redi, and S.-F. Chang · 2016
Later among the works it cites.
Unsupervised domain adaptation with residual transfer networks
M. Long, H. Zhu, J. Wang, and M. I. Jordan · 2016
Later among the works it cites.
Newtonian image understanding: Unfolding the dynamics of objects in static images
R. Mottaghi, H. Bagherinezhad, M. Rastegari, and A. Farhadi · 2016
Later among the works it cites.
”What happens if…” Learning to predict the effect of forces in images
R. Mottaghi, M. Rastegari, A. Gupta, and A. Farhadi · 2016
Later among the works it cites.
Where do emotions come from? Predicting the Emotion Stimuli Map
K.-C. Peng, A. Sadovnik, A. Gallagher, and T. Chen · 2016
Later among the works it cites.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Later among the works it cites.
Toward a taxonomy and computational models of abnormalities in images
B. Saleh, A. Elgammal, J. Feldman, and A. Farhadi · 2016
Later among the works it cites.
Where to look: Focus regions for visual question answering
K. J. Shih, S. Singh, and D. Hoiem · 2016
Later among the works it cites.
Seeing behind the camera: Identifying the authorship of a photograph
C. Thomas and A. Kovashka · 2016
Later among the works it cites.
Walk and learn: Facial attribute representation learning from egocentric video and contextual data
J. Wang, Y. Cheng, and R. Schmidt Feris · 2016
Later among the works it cites.
What’s wrong with that object? Identifying images of unusual objects by modelling the detection score distribution
P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. Tao Shen · 2016
Later among the works it cites.
Ask me anything: Free-form visual question answering based on knowledge from external sources
Q. Wu, P. Wang, C. Shen, A. v. d. Hengel, and A. Dick · 2016
Later among the works it cites.
Ask, attend and answer: Exploring question-guided spatial attention for visual question answering
H. Xu and K. Saenko · 2016
Later among the works it cites.