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Found 5 papers, 3 papers with code
VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization
This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning.
Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training
To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge.
What Makes Pre-trained Language Models Better Zero-shot Learners?
Current methods for prompt learning in zeroshot scenarios widely rely on a development set with sufficient human-annotated data to select the best-performing prompt template a posteriori.
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification
Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.
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Probabilistic Group Testing under Sum Observations: A Parallelizable 2-Approximation for Entropy Loss
We consider the problem of group testing with sum observations and noiseless answers, in which we aim to locate multiple objects by querying the number of objects in each of a sequence of chosen sets.
