Anchor-Free Action Proposal Network with Uncertainty Estimation

Selen Pehlivan, Jorma Laaksonen

Abstract: Proposal generation is a fundamental yet challenging task for two-stage temporal action detection pipelines. The task aims at predicting starting and ending boundaries of segments in realistic video sequences and action recognition methods cannot be directly applied to such videos due to their untrimmed nature. Most state-of-the-art models rely on temporal convolutional neural networks with pre-defined anchor segments. By eliminating anchors, we propose a lighter end-to-end trainable Anchor-Free Multiscale Transformer-based Generator (AMTG) model using local clues via video snippets. To improve effectiveness for temporal evaluation, we apply multiscale Transformer encoders to sequences with a bi-directional mask extension that simultaneously predicts boundary distances with uncertainties and various snippet-based local scores. Later, our model integrates local predictions to generate proposal candidates using the proposed scoring function. Experiments on the THUMOS14 and ActivityNet-1.3 benchmarks demonstrate the effectiveness of AMTG for the temporal proposal generation task.