Exploring Visual Engagement Signals for Representation Learning

By Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge J. Belongie, Ser-Nam Lim. 2021

15 citations recorded by the source corpus.

Visual engagement in social media platforms comprises interactions with photo posts including comments, shares, and likes. In this paper, we leverage such Visual Engagement clues as supervisory signals for representation learning. However, learning from engagement signals is non-trivial as it is not clear how to bridge the gap between low-level visual information and high-level social interactions. We present VisE, a weakly supervised learning approach, which maps social images to pseudo labels derived by clustered engagement signals. We then study how models trained in this way benefit subjective downstream computer vision tasks such as emotion recognition or political bias detection. Through extensive studies, we empirically demonstrate the effectiveness of VisE across a diverse set of classification tasks beyond the scope of conventional recognition 1.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Visual Prompt Tuning (2022, background): “A popular approach is to fine-tune only a subset of the parameters, such as the classifier head [52,32,9] or the bias terms [6].”