23 citations · 61 across the 8 of their papers we have counts for
10 papers
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention
Yu Yang, Seungbae Kim, Jungseock Joo
Interpretability is an important property for visual models as it helps researchers and users understand the internal mechanism of a complex model. However, generating semantic exp…
Understanding and Mitigating Annotation Bias in Facial Expression Recognition
Yunliang Chen, Jungseock Joo
The performance of a computer vision model depends on the size and quality of its training data. Recent studies have unveiled previously-unknown composition biases in common image…
Communicative Learning with Natural Gestures for Embodied Navigation Agents with Human-in-the-Scene
Qi Wu, Cheng-Ju Wu, Yixin Zhu +1
Human-robot collaboration is an essential research topic in artificial intelligence (AI), enabling researchers to devise cognitive AI systems and affords an intuitive means for use…
Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text
Kunwoo Park, Zhufeng Pan, Jungseock Joo
Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are,…
Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation
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 pro…
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
Kimmo Kärkkäinen, Jungseock Joo
Existing public face datasets are strongly biased toward Caucasian faces, and other races (e.g., Latino) are significantly underrepresented. This can lead to inconsistent model acc…