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Found 7 papers, 6 papers with code
Exploring Expression-related Self-supervised Learning for Affective Behaviour Analysis
This paper explores an expression-related self-supervised learning (SSL) method (ContraWarping) to perform expression classification in the 5th Affective Behavior Analysis in-the-wild (ABAW) competition.
Unsupervised Facial Expression Representation Learning with Contrastive Local Warping
Therefore, given a facial image, ContraWarping employs some global transformations and local warping to generate its positive and negative samples and sets up a novel contrastive learning framework.
TransMatting: Tri-token Equipped Transformer Model for Image Matting
However, existing methods perform poorly when faced with highly transparent foreground objects due to the large area of uncertainty to predict and the small receptive field of convolutional networks.
Vision Transformer with Attentive Pooling for Robust Facial Expression Recognition
The proposed APP is employed to select the most informative patches on CNN features, and ATP discards unimportant tokens in ViT.
Ranked #5 on
Facial Expression Recognition (FER)
on RAF-DB
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
TransMatting: Enhancing Transparent Objects Matting with Transformers
Image matting refers to predicting the alpha values of unknown foreground areas from natural images.
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition
To be specific, the universal features denote the general characteristic of facial emotions within a period and the unique features denote the specific characteristic at this moment.
Facial Expression Recognition
Facial Expression Recognition (FER)
TransFER: Learning Relation-aware Facial Expression Representations with Transformers
Second, to build rich relations between different local patches, the Vision Transformers (ViT) are used in FER, called ViT-FER.
Facial Expression Recognition
Facial Expression Recognition (FER)
+2
