---
license: mit
---
# TAC RGB encoder

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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

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- **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={2023},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TCSVT.2023.3326373}}
```