Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks (1510.07712v2)

Abstract: We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal- and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.

Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks

The paper "Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks" presents a novel approach for generating descriptive paragraphs for videos. The proposed methodology leverages hierarchical Recurrent Neural Networks (RNNs) to effectively capture the temporal and spatial dependencies required for coherent video captioning. This approach consists of two key components: a sentence generator and a paragraph generator, each serving distinct functional purposes within the hierarchical framework.

Methodology

The core innovation of this research is the hierarchical structure that integrates both a sentence generator and a paragraph generator.

• Sentence Generator: This component creates concise sentences for short video segments. It uses RNNs enhanced with temporal and spatial attention mechanisms, allowing the model to focus on relevant visual elements selectively during the sentence generation process.
• Paragraph Generator: This component captures inter-sentence dependencies by using the output from the sentence generator. It combines sentential embeddings with paragraph history to inform the initial state of the sentence generator for subsequent sentences, ensuring contextual coherence throughout the generated paragraph.

The authors implement this framework on two benchmark datasets, YouTubeClips and TACoS-MultiLevel, showcasing its general applicability to both open and closed-domain video captioning tasks.

Results

The experiments demonstrate that the hierarchical RNN framework achieves superior performance compared to state-of-the-art methods in video captioning. On YouTubeClips, the model attained a BLEU@4 score of 0.499, and on TACoS-MultiLevel, it reached a BLEU@4 score of 0.305. These results underscore the effectiveness of the hierarchical approach in capturing and utilizing temporal dependencies present in video data.

Implications and Future Directions

The hierarchical RNN framework presents significant implications for both practical applications and theoretical advancements in video captioning and related tasks. Practically, this method can be employed in automatic video subtitling, enhancing accessibility features such as blind navigation, and improving video retrieval systems. The results suggest that integrating hierarchical structures could be beneficial for more complex video understanding tasks that require multi-sentence descriptions.

Theoretically, this work opens avenues for exploring more complex hierarchical models in machine learning that can better capture dependencies across time and modality. Further research could focus on enhancing object detection in videos or refinement in attention mechanisms to improve the model's understanding of nuanced contextual information.

In conclusion, this paper contributes a sophisticated approach for paragraph-level video captioning, demonstrating marked improvements in performance metrics. This approach provides a stepping-stone for further innovations in the field of video analysis and machine intelligence.