In this paper, we introduce Attention Prompt Tuning (APT) - a computationally
efficient variant of prompt tuning for video-based applications such as action
recognition. Prompt tuning approaches involve injecting a set of learnable
prompts along with data tokens during fine-tuning while keeping the backbone
frozen. This approach greatly reduces the number of learnable parameters
compared to full tuning. For image-based downstream tasks, normally a couple of
learnable prompts achieve results close to those of full tuning. However,
videos, which contain more complex spatiotemporal information, require hundreds
of tunable prompts to achieve reasonably good results. This reduces the
parameter efficiency observed in images and significantly increases latency and
the number of floating-point operations (FLOPs) during inference. To tackle
these issues, we directly inject the prompts into the keys and values of the
non-local attention mechanism within the transformer block. Additionally, we
introduce a novel prompt reparameterization technique to make APT more robust
against hyperparameter selection. The proposed APT approach greatly reduces the
number of FLOPs and latency while achieving a significant performance boost
over the existing parameter-efficient tuning methods on UCF101, HMDB51, and
SSv2 datasets for action recognition. The code and pre-trained models are
available at this https URL

通过在 transformer 模块的键和值中直接插入提示信息，我们提出了注意力提示调整（Attention Prompt Tuning，APT）方法，从而在视频行为识别中大大减少了浮点运算和延迟，并在 UCF101、HMDB51 和 SSv2 数据集上实现了显著的性能提升。