Search Results for author: Jungseock Joo

Found 14 papers, 4 papers with code

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

no code implementations19 Aug 2021 Yunliang Chen, Jungseock Joo

We demonstrate that many expression datasets contain significant annotation biases between genders, especially when it comes to the happy and angry expressions, and that traditional methods cannot fully mitigate such biases in trained models.

Facial Expression Recognition

Communicative Learning with Natural Gestures for Embodied Navigation Agents with Human-in-the-Scene

1 code implementation5 Aug 2021 Qi Wu, Cheng-Ju Wu, Yixin Zhu, Jungseock Joo

In a series of experiments, we demonstrate that human gesture cues, even without predefined semantics, improve the object-goal navigation for an embodied agent, outperforming various state-of-the-art methods.

Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text

1 code implementation2 Jun 2021 Kunwoo Park, Zhufeng Pan, Jungseock Joo

Understanding who blames or supports whom in news text is a critical research question in computational social science.

Question Answering Sentiment Analysis

Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation

no code implementations21 May 2020 Jungseock Joo, Kimmo Kärkkäinen

Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups.


FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age

4 code implementations14 Aug 2019 Kimmo Kärkkäinen, Jungseock Joo

Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.

Facial Attribute Classification

Understanding the Political Ideology of Legislators from Social Media Images

no code implementations22 Jul 2019 Nan Xi, Di Ma, Marcus Liou, Zachary C. Steinert-Threlkeld, Jason Anastasopoulos, Jungseock Joo

In this paper, we seek to understand how politicians use images to express ideological rhetoric through Facebook images posted by members of the U. S. House and Senate.

Image as Data: Automated Visual Content Analysis for Political Science

no code implementations3 Oct 2018 Jungseock Joo, Zachary C. Steinert-Threlkeld

Image data provide unique information about political events, actors, and their interactions which are difficult to measure from or not available in text data.

Protest Activity Detection and Perceived Violence Estimation from Social Media Images

1 code implementation18 Sep 2017 Donghyeon Won, Zachary C. Steinert-Threlkeld, Jungseock Joo

We also release the UCLA Protest Image Dataset, our novel dataset of 40, 764 images (11, 659 protest images and hard negatives) with various annotations of visual attributes and sentiments.

Action Detection Activity Detection

Cultural Diffusion and Trends in Facebook Photographs

no code implementations24 May 2017 Quanzeng You, Darío García-García, Mahohar Paluri, Jiebo Luo, Jungseock Joo

Online social media is a social vehicle in which people share various moments of their lives with their friends, such as playing sports, cooking dinner or just taking a selfie for fun, via visual means, that is, photographs.

Fashion Conversation Data on Instagram

no code implementations13 Apr 2017 Yu-I Ha, Sejeong Kwon, Meeyoung Cha, Jungseock Joo

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram.

Joint Image-Text News Topic Detection and Tracking with And-Or Graph Representation

no code implementations15 Dec 2015 Weixin Li, Jungseock Joo, Hang Qi, Song-Chun Zhu

The AOG embeds a context sensitive grammar that can describe the hierarchical composition of news topics by semantic elements about people involved, related places and what happened, and model contextual relationships between elements in the hierarchy.

Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

no code implementations ICCV 2015 Jungseock Joo, Francis F. Steen, Song-Chun Zhu

Secondly, our model can categorize the political party affiliations of politicians, i. e., Democrats vs. Republicans, with the accuracy of 62. 6% (male) and 60. 1% (female).

Weakly Supervised Learning for Attribute Localization in Outdoor Scenes

no code implementations CVPR 2013 Shuo Wang, Jungseock Joo, Yizhou Wang, Song-Chun Zhu

We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.

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