[Paper Review] On Attention Modules for Audio-Visual Synchronization

Naji Khosravan, Shervin Ardeshir+1 more|arXiv (Cornell University)|Dec 14, 2018

Music and Audio Processing15 references4 citations

TL;DR

This paper proposes spatio-temporal and temporal attention modules to improve audio-visual synchronization detection in videos by focusing on discriminative visual regions (e.g., lips, objects making sound) while ignoring non-informative content. The method achieves state-of-the-art accuracy, with a 9.8% improvement on the generic sound class and 8.7% over baseline on the speech class using spatio-temporal attention.

ABSTRACT

With the development of media and networking technologies, multimedia applications ranging from feature presentation in a cinema setting to video on demand to interactive video conferencing are in great demand. Good synchronization between audio and video modalities is a key factor towards defining the quality of a multimedia presentation. The audio and visual signals of a multimedia presentation are commonly managed by independent workflows - they are often separately authored, processed, stored and even delivered to the playback system. This opens up the possibility of temporal misalignment between the two modalities - such a tendency is often more pronounced in the case of produced content (such as movies). To judge whether audio and video signals of a multimedia presentation are synchronized, we as humans often pay close attention to discriminative spatio-temporal blocks of the video (e.g. synchronizing the lip movement with the utterance of words, or the sound of a bouncing ball at the moment it hits the ground). At the same time, we ignore large portions of the video in which no discriminative sounds exist (e.g. background music playing in a movie). Inspired by this observation, we study leveraging attention modules for automatically detecting audio-visual synchronization. We propose neural network based attention modules, capable of weighting different portions (spatio-temporal blocks) of the video based on their respective discriminative power. Our experiments indicate that incorporating attention modules yields state-of-the-art results for the audio-visual synchronization classification problem.

Motivation & Objective

• To address temporal misalignment between audio and video in multimedia content, especially in professionally produced videos like films.
• To develop neural network modules that automatically identify and weigh the most discriminative spatio-temporal video blocks for synchronization judgment.
• To improve binary classification of audio-visual synchronization by leveraging attention mechanisms that mimic human perception of relevant visual and auditory cues.
• To evaluate whether attention mechanisms can enhance model performance beyond standard convolutional networks in synchronization detection.

Proposed method

• A fully convolutional neural network is trained to classify audio-visual synchronization as positive or negative using synchronized and non-synchronized video-audio pairs.
• Temporal attention modules assign weights to different temporal segments of the video, emphasizing moments with high discriminative content such as speech or sound events.
• Spatio-temporal attention modules extend this by assigning higher weights to specific spatial regions (e.g., face, object) within each temporal block, enabling finer-grained focus.
• The attention weights are learned end-to-end during training, allowing the network to dynamically prioritize informative video blocks based on audio-visual alignment cues.
• Feature maps from the backbone network are processed through a learnable attention mechanism that computes soft attention weights using a multi-layer perceptron.
• The final prediction is made by aggregating the weighted features across time and space, improving decision boundary separation between sync and non-sync classes.

Experimental results

Research questions

• RQ1 Can attention mechanisms improve the accuracy of audio-visual synchronization classification in videos?
• RQ2 Does spatio-temporal attention outperform temporal-only attention in detecting audio-visual alignment?
• RQ3 Do attention modules help the model focus on discriminative visual cues such as lip movements or object interactions, as humans do?
• RQ4 How does attention-based feature weighting affect the separation of positive (sync) and negative (non-sync) prediction distributions?

Key findings

• The spatio-temporal attention module achieved a 9.8% absolute improvement in classification accuracy on the generic sound class compared to the baseline network.
• On the speech class, the spatio-temporal attention model achieved 80.3% accuracy, representing a 3.8% improvement over temporal attention and an 8.7% gain over the baseline.
• Visualizations show that the attention modules correctly localize high-weight regions to discriminative events such as a shoe tapping the ground or a speaker’s lips moving in sync with audio.
• The distribution of prediction scores from the attention-based model shows better separation between sync and non-sync examples than the baseline, indicating improved decision confidence.
• Temporal attention alone already improves performance over the baseline, but spatio-temporal attention provides a significant additional gain by incorporating spatial localization.
• The model’s attention behavior aligns with human perception, focusing on temporally and spatially localized audio-visual events while suppressing background or non-informative content.