Paper in AAAI’s ICWSM (2017) “Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram”

May 18th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Face and Gesture, Julia Deeb-Swihart, Papers, Social Computing No Comments »

Paper

  • J. Deeb-Swihart, C. Polack, E. Gilbert, and I. Essa (2017), “Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram,” in In Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2017. [PDF] [BIBTEX]
    @InProceedings{    2017-Deeb-Swihart-SELLCSCI,
      author  = {Julia Deeb-Swihart and Christopher Polack and Eric
          Gilbert and Irfan Essa},
      booktitle  = {In Proceedings of The International AAAI Conference
          on Web and Social Media (ICWSM)},
      month    = {May},
      organization  = {AAAI},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Deeb-Swihart-SELLCSCI.pdf},
      title    = {Selfie-Presentation in Everyday Life: A Large-Scale
          Characterization of Selfie Contexts on Instagram},
      year    = {2017}
    }

Abstract

Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us full circle to Goffman—blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram—an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.

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Paper in IJCNN (2017) “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events”

May 18th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Machine Learning, Papers, Unaiza Ahsan No Comments »

Paper

  • U. Ahsan, M. D. Choudhury, and I. Essa (2017), “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events,” in Proceedings of The International Joint Conference on Neural Networks, Anchorage, Alaska, US, 2017. [PDF] [BIBTEX]
    @InProceedings{    2017-Ahsan-TUVAIISSE,
      address  = {Anchorage, Alaska, US},
      author  = {Unaiza Ahsan and Munmun De Choudhury and Irfan
          Essa},
      booktitle  = {Proceedings of The International Joint Conference
          on Neural Networks},
      month    = {May},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Ahsan-TUVAIISSE.pdf},
      publisher  = {International Neural Network Society},
      title    = {Towards Using Visual Attributes to Infer Image
          Sentiment Of Social Events},
      year    = {2017}
    }

Abstract

Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond
sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

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Paper in IEEE WACV (2017): “Complex Event Recognition from Images with Few Training Examples”

March 27th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, PAMI/ICCV/CVPR/ECCV, Papers, Unaiza Ahsan No Comments »

Paper

  • U. Ahsan, C. Sun, J. Hays, and I. Essa (2017), “Complex Event Recognition from Images with Few Training Examples,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2017. [PDF] [arXiv] [BIBTEX]
    @InProceedings{    2017-Ahsan-CERFIWTE,
      arxiv    = {https://arxiv.org/abs/1701.04769},
      author  = {Unaiza Ahsan and Chen Sun and James Hays and Irfan
          Essa},
      booktitle  = {IEEE Winter Conference on Applications of Computer
          Vision (WACV)},
      month    = {March},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Ahsan-CERFIWTE.pdf},
      title    = {Complex Event Recognition from Images with Few
          Training Examples},
      year    = {2017}
    }

Abstract

We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions and event subtypes, leading to a discriminative and compact representation for event images. Web images are obtained for each discovered event concept and we use (pre-trained) CNN features to train concept classifiers. Extensive experiments on challenging event datasets demonstrate that our proposed method outperforms several baselines using deep CNN features directly in classifying images into events with limited training examples. We also demonstrate that our method achieves the best overall accuracy on a data set with unseen event categories using a single training example.

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2015 C+J Symposium

October 2nd, 2015 Irfan Essa Posted in Computational Journalism, Nick Diakopoulos No Comments »

logoData and computation drive our world, often without sufficient critical assessment or accountability. Journalism is adapting responsibly—finding and creating new kinds of stories that respond directly to our new societal condition. Join us for a two-day conference exploring the interface between journalism and computing.October 2-3, New York, NY#CJ2015

Source: 2015 C+J Symposium

Participated the 4th Computation+Journalism Symposium, October 2-3, in New York, NY at The Brown Institute for Media Innovation Pulitzer Hall, Columbia University.  Keynotes were Lada Adamic (Facebook) and Chris Wiggins (Columbia, NYT), with 2 curated panels and 5 sessions of peer-reviewed papers.

Past Symposiums were held in

  • Atlanta, GA (CJ 2008, hosted by Georgia Tech),
  • Atlanta, GA (CJ 2013, hosted by Georgia Tech), and
  • NYC, NY (CJ 2014, hosted by Columbia U).
  • Next one is being hosted by Stanford and will be in Palo Alto, CA.
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Presentation at Max-Planck-Institut für Informatik in Saarbrücken (2015): “Video Analysis and Enhancement”

September 14th, 2015 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Presentations, Ubiquitous Computing No Comments »

Video Analysis and Enhancement: Spatio-Temporal Methods for Extracting Content from Videos and Enhancing Video OutputSaarbrücken_St_Johanner_Markt_Brunnen

Irfan Essa (prof.irfanessa.com)

Georgia Institute of Technology
School of Interactive Computing

Hosted by Max-Planck-Institut für Informatik in Saarbrucken (Bernt Schiele, Director of Computer Vision and Multimodal Computing)

Abstract 

In this talk, I will start with describing the pervasiveness of image and video content, and how such content is growing with the ubiquity of cameras.  I will use this to motivate the need for better tools for analysis and enhancement of video content. I will start with some of our earlier work on temporal modeling of video, then lead up to some of our current work and describe two main projects. (1) Our approach for a video stabilizer, currently implemented and running on YouTube, and its extensions. (2) A robust and scaleable method for video segmentation. 

I will describe, in some detail, our Video stabilization method, which generates stabilized videos and is in wide use running on YouTube, with Millions of users. Then I will  describe an efficient and scalable technique for spatiotemporal segmentation of long video sequences using a hierarchical graph-based algorithm. I will describe the videosegmentation.com site that we have developed for making this system available for wide use.

Finally, I will follow up with some recent work on image and video analysis in the mobile domains.  I will also make some observations about the ubiquity of imaging and video in general and need for better tools for video analysis. 

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Computation + Journalism Symposium 2014

October 25th, 2014 Irfan Essa Posted in Computational Journalism, Events, Nick Diakopoulos No Comments »

Hosted the 3rd Computation + Journalism Symposium 2014 at The Brown Institute for Media Innovation in the Pulitzer Hall, Columbia University, New York, NY, USA, on October 24-25. It was a huge success with about 250 attendees, and mixture of invited panels and contributed papers.  More details below:

Jon Klienberg kicked off the meeting with a very exciting keynote.  Videos of all sessions should be available from the above website.  Next C+J event will be in a year. Stay tuned for more details.  I was the co-organizer of this event with Nick Diakopoulos and Mark Hansen.

 

 

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Computational Journalist Nick Diakopoulos Appointed Assistant Professor at Philip Merrill College of Journalism, U of Maryland

April 2nd, 2014 Irfan Essa Posted in Computational Journalism, In The News, Nick Diakopoulos No Comments »

Congratulations to my Ph. D. Student Nicholas Diakopoulos and best wishes on his new position.

COLLEGE PARK, Md. – Computational journalist Nicholas A. Diakopoulos will be the newest assistant professor at the Philip Merrill College of Journalism. Dean Lucy Dalglish announced the appointment today.

….

With a background in computer science and human-computer interaction, Diakopoulos received his Ph.D. from the School of Interactive Computing at Georgia Tech.  He was also a computing innovation fellow at the School of Communication and Information at Rutgers University from 2009-2011.

via Computational Journalist Nick Diakopoulos Appointed Assistant Professor.

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Paper in AISTATS 2013 “Beyond Sentiment: The Manifold of Human Emotions”

April 29th, 2013 Irfan Essa Posted in AAAI/IJCAI/UAI, Behavioral Imaging, Computational Journalism, Machine Learning, Papers, WWW No Comments »

  • S. Kim, F. Li, G. Lebanon, and I. A. Essa (2013), “Beyond Sentiment: The Manifold of Human Emotions,” in Proceedings of AI STATS, 2013. [PDF] [BIBTEX]
    @InProceedings{    2012-Kim-BSMHE,
      author  = {Seungyeon Kim and Fuxin Li and Guy Lebanon and
          Irfan A. Essa},
      booktitle  = {Proceedings of AI STATS},
      pdf    = {http://arxiv.org/pdf/1202.1568v1},
      title    = {Beyond Sentiment: The Manifold of Human Emotions},
      year    = {2013}
    }

Abstract

Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.

via [arXiv.org 1202.1568] Beyond Sentiment: The Manifold of Human Emotions.

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Videos from the Computational Journalism Symposium (Jan 31 – Feb 1, 2013).

February 1st, 2013 Irfan Essa Posted in Computational Journalism, Events, Presentations No Comments »

The Computation + Journalism Symposium 2013, held Jan 31 – Feb 1, 2013, at Georgia Institute of Technology, Atlanta, GA, USA was a huge success. Please see the videos here of all the sessions. See me discuss computational journalism with Phil Meyer, and my slides and take-away points from the closing session.

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Computation + Journalism Symposium 2013 on Jan 31 – Feb 1, at GA Tech.

January 2nd, 2013 Irfan Essa Posted in Brad Stenger, CnJ, Computational Journalism, Events, Interesting, Nick Diakopoulos No Comments »

Join us for the 2nd Computation + Journalism Symposium 2013 in Atlanta, GA on Jan 31 – Feb 1, 2013

What role does computation have in the practice of journalism today and in the near future? As computer-driven forces like automation and aggregation increasingly alter the role of journalists and journalism in society, how can computation become a force of deliberate, positive social impact in journalism and civic life? Five years after the first Computation and Journalism symposium at Georgia Tech, this event brings together leaders in both journalism and computation to discuss and debate current trends and future opportunities.

Join us for the second Symposium on Computation + Journalism to be held at the Georgia Institute of Technology in Atlanta on Jan 31, – Feb 1, 2012. Visit this site for additional details.

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