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Found 10 papers, 5 papers with code
Visual Prompt Tuning for Few-Shot Text Classification
In few-shot text classification, a feasible paradigm for deploying VL-PTMs is to align the input samples and their category names via the text encoders.
Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation
When evaluated on the real-world benchmark involving three representative modalities, 2D images (chest X-rays), multi-view images (mammograms), and 3D images (thyroid CT scans), RadFound significantly outperforms other VL foundation models on both quantitative metrics and human evaluation.
MoVL:Exploring Fusion Strategies for the Domain-Adaptive Application of Pretrained Models in Medical Imaging Tasks
Yet, there exists a gap between input medical images and natural pretrained vision model.
TCM-GPT: Efficient Pre-training of Large Language Models for Domain Adaptation in Traditional Chinese Medicine
To the best of our knowledge, our study represents the pioneering validation of domain adaptation of a large language model with 7 billion parameters in TCM domain.
ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation
Large language models have exhibited exceptional performance on various Natural Language Processing (NLP) tasks, leveraging techniques such as the pre-training, and instruction fine-tuning.
VDT: General-purpose Video Diffusion Transformers via Mask Modeling
We also propose a unified spatial-temporal mask modeling mechanism, seamlessly integrated with the model, to cater to diverse video generation scenarios.
UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling
Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49. 7% recall@1 with 2. 2% model parameters, outperforming the latest competitors by 2. 0%.
Towards artificial general intelligence via a multimodal foundation model
To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.
L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing
To overcome these limitations, we propose a novel latent space factorization model, called L2M-GAN, which is learned end-to-end and effective for editing both local and global attributes.
WenLan: Bridging Vision and Language by Large-Scale Multi-Modal Pre-Training
We further construct a large Chinese multi-source image-text dataset called RUC-CAS-WenLan for pre-training our BriVL model.
Ranked #1 on
Image Retrieval
on RUC-CAS-WenLan
