Eventful Transformers：利用视觉Transformer中的时间冗余
Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
AI总结：
针对视觉Transformer用于视频识别时计算成本高的问题，提出Eventful Transformers，仅重处理随时间显著变化的token，可从现有模型转换且无需重训练，在两个基准数据集上实现2-4倍计算节省，准确率下降极小。
摘要：
视觉Transformer在一系列视觉识别任务中取得了令人瞩目的准确率。遗憾的是，其准确率往往伴随着高昂的计算成本，这在视频识别中尤为突出——模型通常需要在不同帧或时间块上重复运行。本研究利用连续输入之间的时间冗余，降低Transformer用于视频处理的成本。我们提出了一种方法，仅识别并重新处理那些随时间发生显著变化的token。我们提出的模型系列Eventful Transformers，可从现有Transformer转换而来（通常无需任何重新训练），且能在运行时自适应控制计算成本。我们在大规模视频目标检测数据集ImageNet VID和动作识别数据集EPIC-Kitchens 100上评估了该方法。结果表明，我们的方法可大幅节省计算量（约2-4倍），且准确率仅有小幅下降。
英文摘要：
Vision Transformers achieve impressive accuracy across a range of visual recognition tasks. Unfortunately, their accuracy frequently comes with high computational costs. This is a particular issue in video recognition, where models are often applied repeatedly across frames or temporal chunks. In this work, we exploit temporal redundancy between subsequent inputs to reduce the cost of Transformers for video processing. We describe a method for identifying and re-processing only those tokens that have changed significantly over time. Our proposed family of models, Eventful Transformers, can be converted from existing Transformers (often without any re-training) and give adaptive control over the compute cost at runtime. We evaluate our method on large-scale datasets for video object detection (ImageNet VID) and action recognition (EPIC-Kitchens 100). Our approach leads to significant computational savings (on the order of 2-4x) with only minor reductions in accuracy.
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