SCE-MAE: Selective Correspondence Enhancement with Masked Autoencoder for Self-Supervised Landmark Estimation

no code implementations • 28 May 2024 • Kejia Yin, Varshanth R. Rao, Ruowei Jiang, Xudong Liu, Parham Aarabi, David B. Lindell

Self-supervised landmark estimation is a challenging task that demands the formation of locally distinct feature representations to identify sparse facial landmarks in the absence of annotated data.

Self-Supervised Learning Task 2

Decompose the Sounds and Pixels, Recompose the Events

no code implementations • 21 Dec 2021 • Varshanth R. Rao, Md Ibrahim Khalil, Haoda Li, Peng Dai, Juwei Lu

In this paper, we propose a framework centering around a novel architecture called the Event Decomposition Recomposition Network (EDRNet) to tackle the Audio-Visual Event (AVE) localization problem in the supervised and weakly supervised settings.