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Found 12 papers, 5 papers with code
A Bag of Tricks for Few-Shot Class-Incremental Learning
FSCIL requires both stability and adaptability, i. e., preserving proficiency in previously learned tasks while learning new ones.
Ranked #2 on
Few-Shot Class-Incremental Learning
on CUB-200-2011
Contrastive Learning of View-Invariant Representations for Facial Expressions Recognition
ViewFX learns view-invariant features of expression using a proposed self-supervised contrastive loss which brings together different views of the same subject with a particular expression in the embedding space.
Active Learning with Contrastive Pre-training for Facial Expression Recognition
Even though some prior works have focused on reducing the need for large amounts of labelled data using different unsupervised methods, another promising approach called active learning is barely explored in the context of FER.
Exploring the Boundaries of Semi-Supervised Facial Expression Recognition: Learning from In-Distribution, Out-of-Distribution, and Unconstrained Data
While semi-supervised learning has shown promise in FER, most current methods from general computer vision literature have not been explored in the context of FER.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data
We propose UnMixMatch, a semi-supervised learning framework which can learn effective representations from unconstrained unlabelled data in order to scale up performance.
Consistency-guided Prompt Learning for Vision-Language Models
Our approach improves the generalization of large foundation models when fine-tuned on downstream tasks in a few-shot setting.
Ranked #2 on
Prompt Engineering
on Oxford-IIIT Pet Dataset
Impact of Labelled Set Selection and Supervision Policies on Semi-supervised Learning
All these labelled samples are then used along with the unlabelled data throughout the training process.
Temporal Contrastive Learning with Curriculum
We present ConCur, a contrastive video representation learning method that uses curriculum learning to impose a dynamic sampling strategy in contrastive training.
Analysis of Semi-Supervised Methods for Facial Expression Recognition
To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Self-supervised Contrastive Learning of Multi-view Facial Expressions
The model is then fine-tuned with labeled data in a supervised setting.
Spatiotemporal Contrastive Learning of Facial Expressions in Videos
Experiments are performed on the Oulu-CASIA dataset and the performance is compared to other works in FER.
Facial Emotion Recognition Using Transfer Learning in the Deep CNN
Human facial emotion recognition (FER) has attracted the attention of the research community for its promising applications.
Ranked #1 on
Facial Expression Recognition (FER)
on JAFFE
Facial Emotion Recognition
Facial Expression Recognition (FER)
+1
