TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration

Abstract

Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations. However, modern methods require a high computational budget due to processing the same observations multiple times through the autoencoder transformers. As a remedy, we introduce a novel approach to AVE called TOken REcycling (TORE). It divides the encoder into extractor and aggregator components. The extractor processes each observation sepa-rately, enabling the reuse of tokens passed to the aggrega-tor. Moreover, to further reduce the computations, we de-crease the decoder to only one block. Through extensive experiments, we demonstrate that TORE outperforms state-of-the-art methods while reducing computational overhead by up to 90%.