Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization

TL;DR

This paper introduces FSENet, a novel framework that uses facial features and contrastive learning to improve weakly-supervised sentiment localization in videos, achieving state-of-the-art results.

Contribution

The paper proposes a unified face-guided framework with modules for sentiment discovery, contrastive semantics, and pseudo-label generation, advancing weakly-supervised sentiment boundary detection.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively leverages facial cues for sentiment boundary detection.

Demonstrates strong generalization across supervision levels.

Abstract

Point-level weakly-supervised temporal sentiment localization (P-WTSL) aims to detect sentiment-relevant segments in untrimmed multimodal videos using timestamp sentiment annotations, which greatly reduces the costly frame-level labeling. To further tackle the challenges of imprecise sentiment boundaries in P-WTSL, we propose the Face-guided Sentiment Boundary Enhancement Network (\textbf{FSENet}), a unified framework that leverages fine-grained facial features to guide sentiment localization. Specifically, our approach \textit{first} introduces the Face-guided Sentiment Discovery (FSD) module, which integrates facial features into multimodal interaction via dual-branch modeling for effective sentiment stimuli clues; We \textit{then} propose the Point-aware Sentiment Semantics Contrast (PSSC) strategy to discriminate sentiment semantics of candidate points (frame-level) near annotation points via contrastive learning, thereby enhancing the model's ability to recognize sentiment boundaries. At \textit{last}, we design the Boundary-aware Sentiment Pseudo-label Generation (BSPG) approach to convert sparse point annotations into temporally smooth supervisory pseudo-labels. Extensive experiments and visualizations on the benchmark demonstrate the effectiveness of our framework, achieving state-of-the-art performance under full supervision, video-level, and point-level weak supervision, thereby showcasing the strong generalization ability of our FSENet across different annotation settings.