  This technical report presents an overview of our solution used in the
submission to ActivityNet Challenge 2019 Task 1 (\textbf{temporal action
proposal generation}) and Task 2 (\textbf{temporal action
localization/detection}). Temporal action proposal indicates the temporal
intervals containing the actions and plays an important role in temporal action
localization. Top-down and bottom-up methods are the two main categories used
for proposal generation in the existing literature. In this paper, we devise a
novel Multi-Granularity Fusion Network (MGFN) to combine the proposals
generated from different frameworks for complementary filtering and confidence
re-ranking. Specifically, we consider the diversity comprehensively from
multiple perspectives, e.g. the characteristic aspect, the data aspect, the
model aspect and the result aspect. Our MGFN achieves the state-of-the-art
performance on the temporal action proposal task with 69.85 AUC score and the
temporal action localization task with 38.90 mAP on the challenge testing set.
