Search Results for author:
Found 6 papers, 3 papers with code
Less is More: Data Value Estimation for Visual Instruction Tuning
To investigate this issue, we conduct a series of empirical studies, which reveal a significant redundancy within the visual instruction datasets, and show that greatly reducing the amount of several instruction dataset even do not affect the performance.
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.
VLAB: Enhancing Video Language Pre-training by Feature Adapting and Blending
Towards this goal, we propose a novel video-text pre-training method dubbed VLAB: Video Language pre-training by feature Adapting and Blending, which transfers CLIP representations to video pre-training tasks and develops unified video multimodal models for a wide range of video-text tasks.
Ranked #1 on
Visual Question Answering (VQA)
on MSVD-QA
(using extra training data)
Enhancing Vision-Language Pre-Training with Jointly Learned Questioner and Dense Captioner
In this paper, we propose a novel method called Joint QA and DC GEneration (JADE), which utilizes a pre-trained multimodal model and easily-crawled image-text pairs to automatically generate and filter large-scale VQA and dense captioning datasets.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
A Survey of Vision-Language Pre-Trained Models
In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs).
