Boundary-Denoising for Video Activity Localization

TL;DR

This paper introduces DenoiseLoc, a novel encoder-decoder model that improves video activity localization by treating boundary detection as a denoising task, leading to more precise boundaries and faster training convergence.

Contribution

It proposes a boundary denoising approach with a new model, DenoiseLoc, that enhances localization accuracy and efficiency in video activity detection tasks.

Findings

+12.36% average mAP on QV-Highlights dataset

+1.64% mAP@0.5 on THUMOS'14 dataset

State-of-the-art performance on TACoS and MAD datasets

Abstract

Video activity localization aims at understanding the semantic content in long untrimmed videos and retrieving actions of interest. The retrieved action with its start and end locations can be used for highlight generation, temporal action detection, etc. Unfortunately, learning the exact boundary location of activities is highly challenging because temporal activities are continuous in time, and there are often no clear-cut transitions between actions. Moreover, the definition of the start and end of events is subjective, which may confuse the model. To alleviate the boundary ambiguity, we propose to study the video activity localization problem from a denoising perspective. Specifically, we propose an encoder-decoder model named DenoiseLoc. During training, a set of action spans is randomly generated from the ground truth with a controlled noise scale. Then we attempt to reverse this process by boundary denoising, allowing the localizer to predict activities with precise boundaries and resulting in faster convergence speed. Experiments show that DenoiseLoc advances %in several video activity understanding tasks. For example, we observe a gain of +12.36% average mAP on QV-Highlights dataset and +1.64% mAP@0.5 on THUMOS'14 dataset over the baseline. Moreover, DenoiseLoc achieves state-of-the-art performance on TACoS and MAD datasets, but with much fewer predictions compared to other current methods.