Temporal knowledge distillation for active perception is provided. Despite significant performance improvements in object detection and classification using deep structures, they still require prohibitive runtime to process images and maintain the highest possible performance for real-time applications. Observing that a human visual system (HVS) relies heavily on temporal dependencies among frames from visual input to conduct recognition efficiently, embodiments described herein propose a novel framework dubbed as temporal knowledge distillation (TKD). The TKD framework distills temporal knowledge gained from a heavy neural network-based model over selected video frames (e.g., the perception of the moments) for a light-weight model. To enable the distillation, two novel procedures are described: 1) a long-short term memory (LSTM)-based key frame selection method; and 2) a novel teacher-bounded loss design.
TEMPORAL KNOWLEDGE DISTILLATION FOR ACTIVE PERCEPTION