Attention-Based Multimodal Fusion for Video Description (1701.03126v2)

Abstract: Currently successful methods for video description are based on encoder-decoder sentence generation using recur-rent neural networks (RNNs). Recent work has shown the advantage of integrating temporal and/or spatial attention mechanisms into these models, in which the decoder net-work predicts each word in the description by selectively giving more weight to encoded features from specific time frames (temporal attention) or to features from specific spatial regions (spatial attention). In this paper, we propose to expand the attention model to selectively attend not just to specific times or spatial regions, but to specific modalities of input such as image features, motion features, and audio features. Our new modality-dependent attention mechanism, which we call multimodal attention, provides a natural way to fuse multimodal information for video description. We evaluate our method on the Youtube2Text dataset, achieving results that are competitive with current state of the art. More importantly, we demonstrate that our model incorporating multimodal attention as well as temporal attention significantly outperforms the model that uses temporal attention alone.

Attention-Based Multimodal Fusion for Video Description

The paper "Attention-Based Multimodal Fusion for Video Description" presents a significant contribution to the domain of automatic video description, leveraging advancements in attention mechanisms within encoder-decoder architectures. The authors explore the integration of modality-dependent attention mechanisms to improve the efficacy of video description tasks.

Overview and Methodology

The research extends upon existing video description models by incorporating multimodal attention, allowing the network to selectively focus on different modalities such as image, motion, and audio features. Traditional methods in this field typically rely on encoder-decoder models utilizing Recurrent Neural Networks (RNNs) with temporal or spatial attention. However, this paper innovates by introducing a modality-dependent fusion that accentuates not just specific temporal or spatial aspects but integrates across various data modalities.

The structural framework involves using a Long Short-Term Memory (LSTM) network, both as an encoder and decoder, to process input features extracted from pre-trained convolutional neural networks (CNNs) like GoogLeNet, VGGNet, and C3D, alongside audio features. The integration of these modalities is managed through an attention mechanism that adaptively weighs the contribution of each modality based on the input context and decoder state. This is realized with a novel multimodal attention strategy which dynamically assigns attention weights to different feature types, thus providing context-sensitive fusion of multimodal inputs during sentence generation.

Experimental Evaluation

The authors conducted experiments using the Youtube2Text dataset, a challenging dataset consisting of diverse video clips with multiple associated textual descriptions. The evaluation metrics included BLEU, METEOR, and CIDEr scores, which are standard in assessing the quality of generated content against human descriptors.

Results demonstrated that the proposed multimodal attention model achieved results competitive with or better than some state-of-the-art models relying solely on temporal attention. Specifically, the integration of multimodal attention especially enhanced performance on CIDEr, a metric valued for its robustness against discrepancies in ground-truth annotations.

Implications and Future Developments

The implications of this work are noteworthy in both practical and theoretical realms. Practically, deploying this model can enhance systems requiring synthesized natural language summaries from video content, potentially transforming accessibility tools and content search engines. Theoretically, this work pushes forward the understanding of multimodal information processing, providing a framework that can be adapted or expanded upon in different contexts like cross-modal retrieval or complex scene understanding.

Future research directions could involve expanding on this foundation by exploring deeper integration of additional modalities or employing more sophisticated attention mechanisms informed by recent developments in transformer architectures. Further experimentation with more varied and noisy datasets could also provide insights into the robustness and adaptability of the proposed model in real-world applications.

Overall, the paper presents a methodologically sound and practically impactful model that marks a meaningful advancement in the automatic video captioning domain through its novel use of multimodal attention.