The paper "Synchformer: Efficient Synchronization from Sparse Cues" addresses the challenge of audio-visual synchronization, particularly in 'in-the-wild' videos like those on YouTube, where synchronization cues are often sparse. The authors propose a novel model, Synchformer, which decouples feature extraction from synchronization modeling through multi-modal segment-level contrastive pre-training. This approach achieves state-of-the-art performance in both dense and sparse settings. Key contributions include: 1. **Model Architecture**: Synchformer splits the audio and visual streams into shorter temporal segments (0.64 seconds) and uses segment-level feature extractors to obtain frequency and spatio-temporal features. These features are then aggregated and fed into a lightweight synchronization module that predicts the temporal offset. 2. **Training Method**: The model is trained in two stages. First, segment-level contrastive pre-training is performed on feature extractors using InfoNCE loss to distinguish between positive and negative pairs. Second, the synchronization module is trained to predict the temporal offset using pre-trained and frozen feature extractors. 3. **Additional Capabilities**: The paper explores evidence attribution techniques to interpret the model's predictions and introduces a new capability for synchronizability prediction, which assesses whether it is possible to synchronize the provided audio and visual streams. 4. **Evaluation**: The model is evaluated on datasets such as LRS3, VGGSound, and AudioSet, achieving superior performance compared to state-of-the-art methods in both dense and sparse settings. 5. **Ablation Studies**: The paper includes ablation studies to validate the effectiveness of different components of the model, such as segment length, feature extractors, and training initialization. 6. **Conclusion**: Synchformer demonstrates significant improvements in audio-visual synchronization, making it adaptable to various downstream tasks and providing insights into the evidence used for synchronization predictions.The paper "Synchformer: Efficient Synchronization from Sparse Cues" addresses the challenge of audio-visual synchronization, particularly in 'in-the-wild' videos like those on YouTube, where synchronization cues are often sparse. The authors propose a novel model, Synchformer, which decouples feature extraction from synchronization modeling through multi-modal segment-level contrastive pre-training. This approach achieves state-of-the-art performance in both dense and sparse settings. Key contributions include: 1. **Model Architecture**: Synchformer splits the audio and visual streams into shorter temporal segments (0.64 seconds) and uses segment-level feature extractors to obtain frequency and spatio-temporal features. These features are then aggregated and fed into a lightweight synchronization module that predicts the temporal offset. 2. **Training Method**: The model is trained in two stages. First, segment-level contrastive pre-training is performed on feature extractors using InfoNCE loss to distinguish between positive and negative pairs. Second, the synchronization module is trained to predict the temporal offset using pre-trained and frozen feature extractors. 3. **Additional Capabilities**: The paper explores evidence attribution techniques to interpret the model's predictions and introduces a new capability for synchronizability prediction, which assesses whether it is possible to synchronize the provided audio and visual streams. 4. **Evaluation**: The model is evaluated on datasets such as LRS3, VGGSound, and AudioSet, achieving superior performance compared to state-of-the-art methods in both dense and sparse settings. 5. **Ablation Studies**: The paper includes ablation studies to validate the effectiveness of different components of the model, such as segment length, feature extractors, and training initialization. 6. **Conclusion**: Synchformer demonstrates significant improvements in audio-visual synchronization, making it adaptable to various downstream tasks and providing insights into the evidence used for synchronization predictions.