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---
license: mit
---
# TAC RGB encoder
<!-- Provide a quick summary of what the model is/does. -->
This model is used for encoding RGB image into a dense feature.
**Caution,** the model does not contain the last FC layer.
So, the output features are not aligned with depth.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
The model is pre-trained with RGB-D contrastive objectives, named TAC.
Different from InfoNCE-based loss fuctions, TAC leverages the similarity between videos frames and estimate a similarity matrix as soft labels.
The backbone of this version is ViT-B/32.
The pre-training is conducted on a new unified RGB-D database, UniRGBD.
The main purpose of this work is depth representation.
So, the RGB encoder is just a side model.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** [TAC](https://github.com/RavenKiller/TAC)
- **Paper:** [Learning Depth Representation from RGB-D Videos by Time-Aware Contrastive Pre-training](https://ieeexplore.ieee.org/document/10288539)
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@ARTICLE{10288539,
author={He, Zongtao and Wang, Liuyi and Dang, Ronghao and Li, Shu and Yan, Qingqing and Liu, Chengju and Chen, Qijun},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={Learning Depth Representation From RGB-D Videos by Time-Aware Contrastive Pre-Training},
year={2024},
volume={34},
number={6},
pages={4143-4158},
doi={10.1109/TCSVT.2023.3326373}}
```
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