This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022. MTVG aims at localizing the temporal boundary of the step in an untrimmed video based on a textual description. The biggest challenge of this task is the fi ne-grained video-text semantics of make-up steps. However, current methods mainly extract video features using action-based pre-trained models. As actions are more coarse-grained than make-up steps, action-based features are not sufficient to provide fi ne-grained cues. To address this issue,we propose to achieve fi ne-grained representation via exploiting feature diversities. Specifically, we proposed a series of methods from feature extraction, network optimization, to model ensemble. As a result, we achieved 3rd place in the MTVG competition.
@article{arxiv.2208.06179,
title = {Exploiting Feature Diversity for Make-up Temporal Video Grounding},
author = {Xiujun Shu and Wei Wen and Taian Guo and Sunan He and Chen Wu and Ruizhi Qiao},
journal= {arXiv preprint arXiv:2208.06179},
year = {2022}
}
Comments
3st Place in PIC Makeup Temporal Video Grounding (MTVG) Challenge in ACM-MM 2022