Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification (2408.14441v1)

Abstract: Exploiting both audio and visual modalities for video classification is a challenging task, as the existing methods require large model architectures, leading to high computational complexity and resource requirements. Smaller architectures, on the other hand, struggle to achieve optimal performance. In this paper, we propose Attend-Fusion, an audio-visual (AV) fusion approach that introduces a compact model architecture specifically designed to capture intricate audio-visual relationships in video data. Through extensive experiments on the challenging YouTube-8M dataset, we demonstrate that Attend-Fusion achieves an F1 score of 75.64\% with only 72M parameters, which is comparable to the performance of larger baseline models such as Fully-Connected Late Fusion (75.96\% F1 score, 341M parameters). Attend-Fusion achieves similar performance to the larger baseline model while reducing the model size by nearly 80\%, highlighting its efficiency in terms of model complexity. Our work demonstrates that the Attend-Fusion model effectively combines audio and visual information for video classification, achieving competitive performance with significantly reduced model size. This approach opens new possibilities for deploying high-performance video understanding systems in resource-constrained environments across various applications.

• The paper introduces Attend-Fusion, a novel model that uses attention mechanisms to integrate audio and visual data efficiently.
• It employs a sophisticated fusion strategy that dynamically prioritizes salient features to balance accuracy and reduced model complexity.
• Experimental results on YouTube-8M show an F1 score of 75.64% using only 72 million parameters, highlighting its potential for resource-constrained environments.

Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification

The paper "Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification" addresses a critical challenge in multimedia analysis—efficiently integrating audio and visual data for video classification without the high computational costs often associated with current models. The paper proposes Attend-Fusion, a novel architecture designed to effectively capture intricate audio-visual interactions and relationships within video datasets, achieving competitive classification performance with significantly reduced model complexity.

Background and Motivation

With the rapid development of large-scale video datasets such as YouTube-8M, the need for models capable of processing and analyzing both visual and auditory information has become increasingly apparent. Traditional approaches to video classification often involve large, computationally expensive networks that integrate audio and visual modalities. These models can be challenging to deploy, particularly in resource-constrained environments. Thus, the researchers propose Attend-Fusion as a solution that maintains high classification accuracy while drastically reducing model size and complexity.

Methodology

Attend-Fusion employs a sophisticated fusion mechanism, integrating fully connected networks and attention mechanisms to leverage audio and visual cues effectively. This paper contrasts Attend-Fusion with various baseline models, including Fully Connected (FC) networks, FC Residual Networks (FCRN), and FC Residual Gated Networks (FCRGN), examining their performance across early and late fusion strategies. The attention mechanisms employed by Attend-Fusion enable the model to focus dynamically on relevant features within and across modalities, an approach that significantly contributes to its enhanced performance with reduced parameters.

Experimental Evaluation

The paper presents comprehensive experimentation on the YouTube-8M dataset, demonstrating Attend-Fusion’s efficacy. It highlights that Attend-Fusion achieves an F1 score of 75.64% using only 72 million parameters. This performance level is comparable to larger models such as the Fully-Connected Late Fusion model (75.96% F1 score) with a model size of 341 million parameters. Attend-Fusion's ability to achieve competitive results with an 80% reduction in the number of parameters underscores its potential for deployment in resource-constrained settings, reflecting an efficient balance between model size and computational efficacy.

Analysis and Implications

The results underscore the importance of attention mechanisms in facilitating more efficient and effective multimodal information processing. By focusing on the most salient features within audio and visual modalities, Attend-Fusion captures complex temporal and cross-modal dynamics, enabling robust classification performance despite reduced computational demands. This work contributes valuable insights regarding the trade-offs between model size, computational efficiency, and accuracy, aligning with sustainable development goals in AI to push towards more resource-efficient models.

Future Directions

The paper invites future research to explore the transferability of Attend-Fusion's architecture to other multimodal tasks beyond video classification, potentially improving real-time video analysis and interactive systems. Further investigation into adaptive attention mechanisms could also enhance model generalization across diverse and noisy data environments. Given the rapid evolution of video content and computational resource constraints, the techniques pioneered by Attend-Fusion present promising avenues for advancing real-world video understanding applications.

In conclusion, "Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification" presents a compelling case for the integration of compact models with advanced attention-based methods, addressing key challenges in the deployment of audio-visual analysis systems. The demonstrated balance between efficiency and performance lays a foundation for further exploration and application in the broader field of multimedia processing and AI-driven video analysis.