Search Results for author: Margrit Betke

Found 30 papers, 10 papers with code

An Unsupervised Approach to Discover Media Frames

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.

Community Detection

Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage

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.

Multimodal Text and Image Classification News Annotation +1

BU-NEmo: an Affective Dataset of Gun Violence News

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.

BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis

no code implementations7 Jun 2023 Mahir Patel, Lucas Carstensen, Yiwen Gu, Michael E. Hasselmo, Margrit Betke

A major bottleneck of interdisciplinary computer vision (CV) research is the lack of a framework that eases the reuse and abstraction of state-of-the-art CV models by CV and non-CV researchers alike.

3D Reconstruction Camera Calibration +1

Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation

1 code implementation27 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.

Semantic Segmentation Unsupervised Domain Adaptation

A graph-transformer for whole slide image classification

1 code implementation19 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.

Classification Contrastive Learning +1

A Unified Framework for Domain Adaptive Pose Estimation

1 code implementation1 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.

2D Pose Estimation Animal Pose Estimation +2

Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation

no code implementations18 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.

Semantic Segmentation Style Transfer +1

OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames

2 code implementations16 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.

General Classification

SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors

no code implementations12 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.

Object object-detection +2

Multi-Label and Multilingual News Framing Analysis

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.

Transfer Learning Translation

Performance Comparison of Crowdworkers and NLP Tools on Named-Entity Recognition and Sentiment Analysis of Political Tweets

no code implementations11 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.

named-entity-recognition Named Entity Recognition +2

Learning to Separate: Detecting Heavily-Occluded Objects in Urban Scenes

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.

object-detection Object Detection +1

Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding U.S. Gun Violence

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.

Scraping Social Media Photos Posted in Kenya and Elsewhere to Detect and Analyze Food Types

1 code implementation31 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.

Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch

no code implementations30 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.

Semantic Segmentation

BUOCA: Budget-Optimized Crowd Worker Allocation

no code implementations11 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.

SAVOIAS: A Diverse, Multi-Category Visual Complexity Dataset

1 code implementation3 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.

Matrix Completion

Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks

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.

Active Learning Gesture Recognition +1

Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)

no code implementations30 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.

Object Semantic Segmentation +1

Automating Image Analysis by Annotating Landmarks with Deep Neural Networks

no code implementations2 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.

Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election

no code implementations31 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.

Sentiment Analysis

Salient Object Subitizing

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.

Image Retrieval Object +4

Pull the Plug? Predicting If Computers or Humans Should Segment Images

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.

Semantic Segmentation

Discovering Useful Parts for Pose Estimation in Sparsely Annotated Datasets

no code implementations2 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.

Pose Estimation

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