no code implementations • Findings (EMNLP) 2021 • Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
Such perspectives are called “frames” in communication research. We study, for the first time, the value of combining lead images and their contextual information with text to identify the frame of a given news article.
no code implementations • LREC 2022 • Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
As such, we introduce a U. S. gun violence news dataset that contains news headline and image pairings from 840 news articles with 15K high-quality, crowdsourced annotations on emotional responses to the news pairings.
1 code implementation • PoliticalNLP (LREC) 2022 • Sha Lai, Yanru Jiang, Lei Guo, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
We discuss the effectiveness of our approach by comparing the frames it generates in an unsupervised manner to the domain-expert-derived frames for the issue of gun violence, for which a supervised learning model for frame recognition exists.
1 code implementation • EMNLP (ACL) 2021 • Vibhu Bhatia, Vidya Prasad Akavoor, Sejin Paik, Lei Guo, Mona Jalal, Alyssa Smith, David Assefa Tofu, Edward Edberg Halim, Yimeng Sun, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
We propose and guide users through a five-step end-to-end computational framing analysis framework grounded in media framing theory in communication research.
no code implementations • 27 Nov 2022 • Kaihong Wang, Donghyun Kim, Rogerio Feris, Kate Saenko, Margrit Betke
We propose to perform adaptation on attention maps with cross-domain attention layers that share features between the source and the target domains.
1 code implementation • 19 May 2022 • Yi Zheng, Rushin H. Gindra, Emily J. Green, Eric J. Burks, Margrit Betke, Jennifer E. Beane, Vijaya B. Kolachalama
Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade.
1 code implementation • 1 Apr 2022 • Donghyun Kim, Kaihong Wang, Kate Saenko, Margrit Betke, Stan Sclaroff
In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision.
no code implementations • 18 Sep 2020 • Kaihong Wang, Chenhongyi Yang, Margrit Betke
Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data.
2 code implementations • 16 Aug 2020 • Alyssa Smith, David Assefa Tofu, Mona Jalal, Edward Edberg Halim, Yimeng Sun, Vidya Akavoor, Margrit Betke, Prakash Ishwar, Lei Guo, Derry Wijaya
The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents.
no code implementations • 12 Aug 2020 • Mona Jalal, Josef Spjut, Ben Boudaoud, Margrit Betke
We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications.
no code implementations • ACL 2020 • Afra Feyza Aky{\"u}rek, Lei Guo, R Elanwar, a, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
News framing refers to the practice in which aspects of specific issues are highlighted in the news to promote a particular interpretation.
no code implementations • 12 Feb 2020 • Nataniel Ruiz, Hao Yu, Danielle A. Allessio, Mona Jalal, Ajjen Joshi, Thomas Murray, John J. Magee, Jacob R. Whitehill, Vitaly Ablavsky, Ivon Arroyo, Beverly P. Woolf, Stan Sclaroff, Margrit Betke
In this work, we propose a video-based transfer learning approach for predicting problem outcomes of students working with an intelligent tutoring system (ITS).
no code implementations • 11 Feb 2020 • Mona Jalal, Kate K. Mays, Lei Guo, Margrit Betke
We report results of a comparison of the accuracy of crowdworkers and seven Natural Language Processing (NLP) toolkits in solving two important NLP tasks, named-entity recognition (NER) and entity-level sentiment (ELS) analysis.
1 code implementation • ECCV 2020 • Chenhongyi Yang, Vitaly Ablavsky, Kaihong Wang, Qi Feng, Margrit Betke
While visual object detection with deep learning has received much attention in the past decade, cases when heavy intra-class occlusions occur have not been studied thoroughly.
no code implementations • 16 Nov 2019 • Qitong Wang, Yi Zheng, Margrit Betke
Our text spotting framework, called UHTA, combines UHT with the state-of-the-art text recognition system ASTER.
Ranked #9 on
Scene Text Detection
on Total-Text
no code implementations • CONLL 2019 • Siyi Liu, Lei Guo, Kate Mays, Margrit Betke, Derry Tanti Wijaya
We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U. S. between 2016 and 2018.
1 code implementation • 31 Aug 2019 • Kaihong Wang, Mona Jalal, Sankara Jefferson, Yi Zheng, Elaine O. Nsoesie, Margrit Betke
We also propose a scrape-by-keywords methodology and used it to scrape ~30, 000 images and their captions of 38 Kenyan food types.
no code implementations • 4 Aug 2019 • Yi Zheng, Qitong Wang, Margrit Betke
A text-recognition network interprets isolated words.
no code implementations • 30 Apr 2019 • Danna Gurari, Yinan Zhao, Suyog Dutt Jain, Margrit Betke, Kristen Grauman
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
no code implementations • 11 Jan 2019 • Mehrnoosh Sameki, Sha Lai, Kate K. Mays, Lei Guo, Prakash Ishwar, Margrit Betke
We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling.
1 code implementation • 3 Oct 2018 • Elham Saraee, Mona Jalal, Margrit Betke
In this work, we introduce Savoias, a visual complexity dataset that compromises of more than 1, 400 images from seven image categories relevant to the above research areas, namely Scenes, Advertisements, Visualization and infographics, Objects, Interior design, Art, and Suprematism.
no code implementations • CVPR 2017 • Ajjen Joshi, Soumya Ghosh, Margrit Betke, Stan Sclaroff, Hanspeter Pfister
Leveraging recent work on learning Bayesian neural networks, we build fast, scalable algorithms for inferring the posterior distribution over all network weights in the hierarchy.
no code implementations • 30 Apr 2017 • Danna Gurari, Kun He, Bo Xiong, Jianming Zhang, Mehrnoosh Sameki, Suyog Dutt Jain, Stan Sclaroff, Margrit Betke, Kristen Grauman
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems.
no code implementations • 2 Feb 2017 • Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
Image and video analysis is often a crucial step in the study of animal behavior and kinematics.
no code implementations • 31 Aug 2016 • Mehrnoosh Sameki, Mattia Gentil, Kate K. Mays, Lei Guo, Margrit Betke
We explore two dynamic-allocation methods: (1) The number of workers queried to label a tweet is computed offline based on the predicted difficulty of discerning the sentiment of a particular tweet.
no code implementations • CVPR 2015 • Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech
We study the problem of Salient Object Subitizing, i. e. predicting the existence and the number of salient objects in an image using holistic cues.
no code implementations • CVPR 2016 • Danna Gurari, Suyog Jain, Margrit Betke, Kristen Grauman
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
no code implementations • 2 May 2016 • Mikhail Breslav, Tyson L. Hedrick, Stan Sclaroff, Margrit Betke
Our work introduces a novel way to increase pose estimation accuracy by discovering parts from unannotated regions of training images.