agneet42/robustness_depth_lang
[CVPR 2024] "On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation"
This project helps computer vision researchers understand how well language guidance works for tasks like monocular depth estimation. It takes an image and optional textual descriptions as input, then evaluates how robustly and accurately the system estimates the depth information for the scene. Computer vision scientists and engineers working on 3D scene understanding and perception systems would use this to evaluate the effectiveness of language-guided models.
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Use this if you are a computer vision researcher evaluating the reliability and generalization capabilities of language-guided depth estimation models under various conditions.
Not ideal if you are looking for a plug-and-play solution for improving depth estimation in real-world applications without delving into research methodology and robustness analysis.
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8
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2
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 05, 2024
Commits (30d)
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