Understanding human behavior and environmental information in egocentric videos is very challenging due to the invisibility of some actions (e.g., laughing and sneezing) and the local nature of the first-person view. Leveraging the corresponding exocentric video to provide global context has shown promising results. However, existing visual-to-visual and visual-to-textual Ego-Exo video alignment methods struggle with the issue that some activities may have non-visual overlap. To address this, we propose using sound as a bridge, as audio is often consistent across Ego-Exo videos. However, direct audio-to-audio alignment lacks context. Thus, we introduce two context-aware sound modules: one aligns audio with vision via a visual-audio cross-attention module, and another aligns text with sound closed caption generated by LLM. Experimental results on two Ego-Exo video association benchmarks show that each of the proposed modules enhances the state-of-the-art methods. Moreover, the proposed sound-aware egocentric or exocentric representation boosts the performance of downstream tasks, such as action recognition of exocentric videos and scene recognition of egocentric videos. The code and models can be accessed at https://github.com/shhuangcoder/SoundBridge.

In this paper, a text-based characterization combined with numerical features for Social Media Headline Prediction (SMHP) is proposed. Description of images, users' emotions and opinions are all described in text, our text-based characterization learns these important features by training a Doc2vec model. Numerical features of social cues contain general characteristics of social media headline, we build an effective method to extract numerical features. Experiments conducted on real-world SMHP dataset manifest the effectiveness of the proposed approach, which achieves the following performance: Spearmanr's Rho: 0.4559, MAE:1.9797.