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Found 5 papers, 0 papers with code
Active Learning Based Fine-Tuning Framework for Speech Emotion Recognition
However, existing SER methods ignore the information gap between the pre-training speech recognition task and the downstream SER task, leading to sub-optimal performance.
Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognition
In this paper, we propose a method for joint modality fusion and graph contrastive learning for multimodal emotion recognition (Joyful), where multimodality fusion, contrastive learning, and emotion recognition are jointly optimized.
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization
Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them.
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model
Text infilling aims to restore incomplete texts by filling in blanks, which has attracted more attention recently because of its wide application in ancient text restoration and text rewriting.
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation
Automated radiology report generation aims to generate paragraphs that describe fine-grained visual differences among cases, especially those between the normal and the diseased.
