Practical overview
When it fits
Use CoVT when investigating how continuous visual representations can support spatially grounded reasoning in vision-language models, or when exploring the project's supplied evaluation, demo and training resources.
Who it is for
- Researchers studying multimodal learning and visual reasoning.
- Developers exploring vision-language model evaluation, demonstrations or training.
Documented uses
- Explore an image-question interface powered by a CoVT vision-language model.
- Evaluate CoVT model variants using the repository's VLMEvalKit-based setup.
- Reproduce the method using the released training data and instructions.
- Compare model variants that use different combinations of visual signals.
Requirements & limits
The project is presented as a public GitHub repository under the Apache-2.0 license. The README links to model weights and training data on Hugging Face.
- The repository separates evaluation, Gradio demo and training into distinct instruction paths.
- Listed model variants differ in their baseline model and supported visual signals.
- The README identifies Python as the primary repository language; it does not establish a runtime platform or API.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Open the CoVT interactive demo instructions
Reach the documented instructions for CoVT's Gradio demo, which provides image uploads and conversational questions through a web UI.
Before you start
- Access to the CoVT repository README.
Steps
Open the CoVT repository README.
In the Quick Start section, select the instructions linked as docs/Demo.md.
Expected outcome
The linked guide provides the next documented stage for getting started with the interactive CoVT demo.
Guide limitations
- The README does not provide the demo setup or launch steps in the available documentation.
Open the CoVT evaluation instructions
Reach the documented evaluation guide, which uses VLMEvalKit for model evaluation.
Before you start
- Access to the CoVT repository README.
Steps
Open the CoVT repository README.
In the Quick Start section under Evaluation, follow the instruction linked as docs/Eval.md.
Expected outcome
The linked guide provides the next documented stage for evaluating models with the repository's VLMEvalKit-based framework.
Guide limitations
- The README does not provide the evaluation setup or execution steps in the available documentation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- Wakals/CoVT · Official homepage title · retained in its source language