Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers
This work addresses computational efficiency for video processing tasks, offering an incremental improvement by adapting existing Transformers with minimal re-training.
The paper tackled the high computational cost of Vision Transformers in video recognition by exploiting temporal redundancy between frames, achieving 2-4x computational savings with minor accuracy reductions on datasets like ImageNet VID and EPIC-Kitchens 100.
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.