  Existing state-of-the-art crowd counting algorithms rely excessively on
location-level annotations, which are burdensome to acquire. When only
count-level (weak) supervisory signals are available, it is arduous and
error-prone to regress total counts due to the lack of explicit spatial
constraints. To address this issue, a novel and efficient counter (referred to
as CrowdMLP) is presented, which probes into modelling global dependencies of
embeddings and regressing total counts by devising a multi-granularity MLP
regressor. In specific, a locally-focused pre-trained frontend is cascaded to
extract crude feature maps with intrinsic spatial cues, which prevent the model
from collapsing into trivial outcomes. The crude embeddings, along with raw
crowd scenes, are tokenized at different granularity levels. The
multi-granularity MLP then proceeds to mix tokens at the dimensions of
cardinality, channel, and spatial for mining global information. An effective
proxy task, namely Split-Counting, is also proposed to evade the barrier of
limited samples and the shortage of spatial hints in a self-supervised manner.
Extensive experiments demonstrate that CrowdMLP significantly outperforms
existing weakly-supervised counting algorithms and performs on par with
state-of-the-art location-level supervised approaches.
