Instance LSeg - exploring instance level information from visual language model
This final year project explores the potential of using large-scale pretrained visual language models in instance-level zero-shot computer vision tasks. Specifically, we propose Instance LSeg - a novel approach to extend the zero-shot semantic segmentation method LSeg to perform language guided...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/171917 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | This final year project explores the potential of using large-scale pretrained visual
language models in instance-level zero-shot computer vision tasks. Specifically, we
propose Instance LSeg - a novel approach to extend the zero-shot semantic
segmentation method LSeg to perform language guided instance segmentation and
grounding of natural language expressions in images. To evaluate our method, we
used three popular referring datasets, and we observe that our method achieves
highly competitive results against published generalized visual grounding baselines |
---|