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license: ecl-2.0 |
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**VD-IT model** |
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The is our pre-trained checkpoint for our paper [**Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation**](https://arxiv.org/abs/2403.12042). |
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We use a video diffusion model ([ModelScopeT2V](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)) as our base model, applying prompt tuning to adapt it as a visual backbone for downstream video understanding tasks. |
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### Model traning |
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We first pre-train our model on Ref-COCO and then fine-tune it on Ref-YouTube-VOS. The training of the models utilizes |
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two NVIDIA A100 GPUs, processing 5 frames per clip over the course of 9 epochs. The initial learning rate is set to 5e-5 and reduced by a factor of 10 at the 6th and 8th epochs. |
