  This paper focuses on visual counting, which aims to predict the number of
occurrences given a natural image and a query (e.g. a question or a category).
Unlike most prior works that use explicit, symbolic models which can be
computationally expensive and limited in generalization, we propose a simple
and effective alternative by revisiting modulated convolutions that fuse the
query and the image locally. Following the design of residual bottleneck, we
call our method MoVie, short for Modulated conVolutional bottlenecks. Notably,
MoVie reasons implicitly and holistically and only needs a single forward-pass
during inference. Nevertheless, MoVie showcases strong performance for
counting: 1) advancing the state-of-the-art on counting-specific VQA tasks
while being more efficient; 2) outperforming prior-art on difficult benchmarks
like COCO for common object counting; 3) helped us secure the first place of
2020 VQA challenge when integrated as a module for 'number' related questions
in generic VQA models. Finally, we show evidence that modulated convolutions
such as MoVie can serve as a general mechanism for reasoning tasks beyond
counting.
