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Found 11 papers, 5 papers with code
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels
Large-scale multilingual pre-trained language models have achieved remarkable performance in zero-shot cross-lingual tasks.
Towards More Accurate Uncertainty Estimation In Text Classification
The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy.
Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges
Studies highlight the gap in the deceptive power of LLM-generated fake news with and without human assistance, yet the potential of prompting techniques has not been fully explored.
Can LLM find the green circle? Investigation and Human-guided tool manipulation for compositional generalization
The meaning of complex phrases in natural language is composed of their individual components.
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission
Sequential labeling is a task predicting labels for each token in a sequence, such as Named Entity Recognition (NER).
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation.
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance.
Cross-Domain Few-Shot Semantic Segmentation
In this paper, we extend few-shot semantic segmentation to a new task, called Cross-Domain Few-Shot Semantic Segmentation (CD-FSS), which aims to generalize the meta-knowledge from domains with sufficient training labels to low-resource domains.
Semantic Editing On Segmentation Map Via Multi-Expansion Loss
Each MEx area has the mask area of the generation as the majority and the boundary of original context as the minority.
Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations
Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-networkbased methods typically suffer from a shortage of large amounts of pixel-level annotations.
Robust Regression via Online Feature Selection under Adversarial Data Corruption
The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when features are not accessible entirely at one time.
