TF-ATM: Training-Free Adaptive Token Merging

Vision Transformers (ViTs) show promising potential in various multimedia application scenarios. To facilitate their deployment on resource-constrained devices, token pruning and merging have been introduced. However, existing token compression methods focus solely on the abstract features exhibited by high-dimensional tokens after patch embedding, resulting in information loss when evaluating token importance. In this paper, we propose a novel Training-Free Adaptive Token Merging (TF-ATM) method by exploring the intrinsic properties of images themselves. Our TF-ATM is inspired by the observation that the characteristics of patches can intuitively reflect the redundancy level of images. Based on the observation, we develop a method that is mathematically formulated to merge tokens corresponding to patches that are close to the Median presentation in the Frequency domain (MF). The principle behind our merging is that patches close to MF can be replaced by their surrounding ones, and thus removing them does not impair performance. Besides, we experimentally show that patches farther from MF contain more important information, which can be leveraged to capture the object of interest accurately. Without any retraining, TF-ATM leads to significant improvements over the state-of-the-arts (SOTAs), with similar FLOPs (floating point operations). For example, we achieve a 44.5%-FLOPs reduction with only a small loss of 0.39% in top-1 accuracy for the MAE-H model on ImageNet dataset, superior to comparison approaches that require meticulous fine-tuning.