ST-Adapter: Parameter-Efficient Image-to-Video

By default, we insert a ST-Adapter to every Transformer block in the backbone, but we also show the performance impact of using fewer ST-Adapters. As shown in

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ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

By default, we insert a ST-Adapter to every Transformer block in the backbone, but we also show the performance impact of using fewer ST-Adapters. As shown in Table 5b, while more ST-Adapters

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We''ve included an example configuration in config.py.example which contains all required fields with values left empty. Please copy config.py.example to config.py and fill in the values before running the

ST-Adapter: Parameter-Efficient Image-to-Video Transfer

Unless otherwise specified, we use ViT-B/16 backbone and 8 input frames in all ablation experiments, and we use one ST-Adapter with bottleneck width 384 before MHSA in each Transformer block.

GitHub

OverviewEnvironmentConfigurationDataset preparationBackbone preparationRun the modelsAcknowledgementsSome common configurations (e.g., dataset paths, pretrained backbone paths) are set in config.py. We''ve included an example configuration in config.py.example which contains all required fields with values left empty. Please copy config.py.example to config.py and fill in the values before running the models.See more on github NeurIPS Proceedings

ST-Adapter: Parameter-Efficient Image-to-Video Transfer

Unless otherwise specified, we use ViT-B/16 backbone and 8 input frames in all ablation experiments, and we use one ST-Adapter with bottleneck width 384 before MHSA in each Transformer block.

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