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Found 5 papers, 3 papers with code
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones.
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
In the first stage, we devise a scoring network to evaluate the difficulty of training instructions, which is co-trained with the VLM.
GPT-4 Vision on Medical Image Classification -- A Case Study on COVID-19 Dataset
This technical report delves into the application of GPT-4 Vision (GPT-4V) in the nuanced realm of COVID-19 image classification, leveraging the transformative potential of in-context learning to enhance diagnostic processes.
Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction
Existing multimodal works that train models from scratch face the problem of lacking universal knowledge when modeling financial news.
Stock Trading Volume Prediction with Dual-Process Meta-Learning
Our method can model the common pattern behind different stocks with a meta-learner, while modeling the specific pattern for each stock across time spans with stock-dependent parameters.
