Visual Prompt Tuning · PyTorchKR Papers

Visual Prompt Tuning(VPT) only introduces a small amount of task-specific learnable parameters into the input space while freezing the entire pre-trained Transformer backbone during downstream training. In practice, these additional parameters are simply prepended into the input sequence of each Transformer layer and learned together with a linear head during fine-tuning. VPT is especially effective in the low-data regime, and maintains its advantage across data scales. Finally, VPT is competitive for a range of Transformer scales and designs (ViTBase/Large/Huge, Swin). Put together, the results suggest that VPT is one of the most effective ways of adapting ever-growing vision backbones.

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VTAB

The Visual Task Adaptation Benchmark (VTAB) is a benchmark designed to evaluate general visual representations². It consists of a diverse and challenging suite of tasks². The benchmark defines a good general visual representation as one that yields good performance on unseen tasks, when trained on limited task-specific data². The VTAB benchmark contains the following 19 tasks that are derived from public datasets¹: - Caltech101 - CIFAR-100 - CLEVR distance prediction - CLEVR counting - Diabetic Rethinopathy - Dmlab Frames - dSprites orientation prediction - dSprites location prediction - Describable Textures Dataset (DTD) - EuroSAT - KITTI distance prediction - 102 Category Flower Dataset - Oxford IIIT Pet dataset - PatchCamelyon - Resisc45 - Small NORB azimuth prediction - Small NORB elevation prediction - SUN397 - SVHN The given model is independently fine-tuned for solving each of the above tasks¹. Average accuracy across all tasks is used to measure the model's performance¹. Detailed description of all tasks, evaluation protocol, and other details can be found in the VTAB paper¹. (1) Visual Task Adaptation Benchmark. https://google-research.github.io/task_adaptation/. (2) GitHub - google-research/task_adaptation. https://github.com/google-research/task_adaptation. (3) GitHub - KMnP/vpt: ️ Visual Prompt Tuning [ECCV 2022] https://arxiv .... https://github.com/KMnP/vpt.