Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting

What is it about?

Crowd counting is a popular topic with widespread applications. Currently, the biggest challenge to crowd counting is large-scale variation in objects. In this paper, we focus on overcoming this challenge by proposing a novel Attentive Encoder-Decoder Network (AEDN), which is supervised on multiple feature scales to conduct crowd counting via density estimation.

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Why is it important?

This work has three main contributions. First, we augment the traditional encoder-decoder architecture with our proposed residual attention blocks, which, beyond skip-connected encoded features, further extend the decoded features with attentive features. AEDN is better at establishing long-range dependencies between the encoder and decoder, therefore promoting more effective fusion of multi-scale features for handling scale-variations. Second, we design a new KL-divergence based distribution loss to supervise the scale-aware structural differences between two density maps, which complements the pixel-isolated MSE loss and better optimizes AEDN to generate high-quality density maps. Third, we adopt a multi-scale supervision scheme, such that multiple KL divergences and MSE losses are deployed at all decoding stages, providing more thorough supervisions for different feature scales.

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