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Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers

The code is coming

<p align="center"> <img src="img/pipeline.png" alt="beyond masking" width="70%"> </p> <p align="center"> Figure 1: Pipeline of token-based pre-training. </p> <p align="center"> <img src="img/vis.png" alt="visualization" width="60%"> </p> <p align="center"> Figure 2: The visualization of the proposed 5 tasks. </p>

main results

All the results are pre-trained for 300 epochs using Vit-base as default.

<table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom"></th> <th valign="bottom">zoomed-in</th> <th valign="bottom">zoomed-out</th> <th valign="bottom">distorted</th> <th valign="bottom">blurred</th> <th valign="bottom">de-colorized</th> <!-- TABLE BODY --> <tr><td align="left">finetune</td> <td align="center"><tt>82.7</tt></td> <td align="center"><tt>82.5</tt></td> <td align="center"><tt>82.1</tt></td> <td align="center"><tt>81.8</tt></td> <td align="center"><tt>81.4</tt></td> </tr> </tbody></table> <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom"></th> <th valign="bottom">zoomed-in (a)</th> <th valign="bottom">mask (m)</th> <th valign="bottom">(a)+(m)</th> <!-- TABLE BODY --> <tr><td align="left">finetune</td> <td align="center"><tt>82.7</tt></td> <td align="center"><tt>82.9</tt></td> <td align="center"><tt>83.2</tt></td> </tr> </tbody></table> We note that the integrated version dose not require extra computational cost.

Effiencicy

<p align="center"> <img src="img/eff.png" alt="effiencicy" width="40%"> </p> <p align="center"> Figure 3: Efficiency of the integrated task. </p>