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Found 5 papers, 3 papers with code
LA-Net: Landmark-Aware Learning for Reliable Facial Expression Recognition under Label Noise
Facial expression recognition (FER) remains a challenging task due to the ambiguity of expressions.
Facial Expression Recognition
Facial Expression Recognition (FER)
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
Accordingly, we propose a novel paradigm, i. e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy.
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling.
Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection
In this work, aiming to fully exploit features of annotated novel object and capture fine-grained features of query object, we propose Dense Relation Distillation with Context-aware Aggregation (DCNet) to tackle the few-shot detection problem.
Region-Aware Contrastive Learning for Semantic Segmentation
Inspired by recent progress in unsupervised contrastive learning, we propose the region-aware contrastive learning (RegionContrast) for semantic segmentation in the supervised manner.
