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Found 3 papers, 0 papers with code
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding in Open World
In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness.
A Step Toward More Inclusive People Annotations for Fairness
In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images.
Large-Scale Visual Speech Recognition
To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).
Ranked #11 on
Lipreading
on LRS3-TED
(using extra training data)
