An empirical study on adaptation methods for large-scale vision-language models
Since the rise of powerful large-scale pre-trained Vision-Language (VL) models, such as CLIP and ALIGN, pre-training and fine-tuning have become promising paradigms to build transferable models for different downstream tasks. However, it is often prohibitive to fine-tune the whole pre-trained VL mod...
Saved in:
Main Author: | Wang, Annan |
---|---|
Other Authors: | Chen Change Loy |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/165970 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Towards unbiased visual language reasoning and consistent segmentation
by: Huang, Jianqiang
Published: (2023) -
Methods for large-scale image-based localization using structure-from-motion point clouds
by: Cheng, Wentao
Published: (2020) -
Learning to recognize objects by adaptive knowledge transfer
by: Tao, Qingyi
Published: (2021) -
Instance LSeg - exploring instance level information from visual language model
by: Lin, Zixing
Published: (2023) -
Vergence control for a biologically inspired binocular active vision system
by: Zhang, Xuejie
Published: (2012)