Dual-Path Temporal Map Optimization for Make-up Temporal Video Grounding

TL;DR

This paper introduces DPTMO, a dual-path network that improves fine-grained make-up video grounding by capturing detailed semantic cues through query-agnostic and query-guided features, leading to more accurate localization.

Contribution

The paper proposes a novel dual-path proposal-based framework that effectively captures fine-grained semantic details in make-up videos, surpassing existing methods in accuracy.

Findings

DPTMO outperforms previous methods on the YouMakeup dataset.

Dual-path structure enhances semantic comprehension of make-up activities.

Joint optimization of two proposal sets improves timestamp prediction accuracy.

Abstract

Make-up temporal video grounding (MTVG) aims to localize the target video segment which is semantically related to a sentence describing a make-up activity, given a long video. Compared with the general video grounding task, MTVG focuses on meticulous actions and changes on the face. The make-up instruction step, usually involving detailed differences in products and facial areas, is more fine-grained than general activities (e.g, cooking activity and furniture assembly). Thus, existing general approaches cannot locate the target activity effectually. More specifically, existing proposal generation modules are not yet fully developed in providing semantic cues for the more fine-grained make-up semantic comprehension. To tackle this issue, we propose an effective proposal-based framework named Dual-Path Temporal Map Optimization Network (DPTMO) to capture fine-grained multimodal semantic details of make-up activities. DPTMO extracts both query-agnostic and query-guided features to construct two proposal sets and uses specific evaluation methods for the two sets. Different from the commonly used single structure in previous methods, our dual-path structure can mine more semantic information in make-up videos and distinguish fine-grained actions well. These two candidate sets represent the cross-modal makeup video-text similarity and multi-modal fusion relationship, complementing each other. Each set corresponds to its respective optimization perspective, and their joint prediction enhances the accuracy of video timestamp prediction. Comprehensive experiments on the YouMakeup dataset demonstrate our proposed dual structure excels in fine-grained semantic comprehension.