Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …

ChatGPT is attracting a cross-field interest as it provides a language interface with
remarkable conversational competency and reasoning capabilities across many domains …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

Abstract The Visual Question Answering (VQA) system is the process of finding useful
information from images related to the question to answer the question correctly. It can be …

Exploring large-scale pretrained foundation models is of significant interest in computer
vision because these models can be quickly transferred to many downstream tasks. This …

Building models that can be rapidly adapted to novel tasks using only a handful of annotated
examples is an open challenge for multimodal machine learning research. We introduce …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

We introduce Cap3D, an automatic approach for generating descriptive text for 3D objects.
This approach utilizes pretrained models from image captioning, image-text alignment, and …