Efficient Crowd Counting via Structured Knowledge Transfer
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Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications. However, most previous works relied on heavy backbone networks and required prohibitive runtimes, which would seriously restrict their deployment scopes and cause poor scalability. To liberate these crowd counting models, we propose a novel Structured Knowledge Transfer (SKT) framework integrating two complementary transfer modules, which can generate a lightweight but still highly effective student network by fully exploiting the structured knowledge of a well-trained teacher network. Specifically, an Intra-Layer Pattern Transfer sequentially distills the knowledge embedded in single-layer features of the teacher network to guide feature learning of the student network. Simultaneously, an Inter-Layer Relation Transfer densely distills the cross-layer correlation knowledge of the teacher to regularize the student's feature evolution. In this way, our student network can learn compact and knowledgeable features, yielding high efficiency and competitive performance. Extensive evaluations on three benchmarks well demonstrate the knowledge transfer effectiveness of our SKT for extensive crowd counting models. In particular, only having one-sixteenth of the parameters and computation cost of original models, our distilled VGG-based models obtain at least 6.5× speed-up on an Nvidia 1080 GPU and even achieve state-of-the-art performance.
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