Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning (1511.03476v1)

Abstract: Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non-linearity. Third, HRNE is able to uncover temporal transitions between frame chunks with different granularities, i.e., it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks. Notably, even using a single network with only RGB stream as input, HRNE beats all the recent systems which combine multiple inputs, such as RGB ConvNet plus 3D ConvNet.

Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning

The research paper presents the Hierarchical Recurrent Neural Encoder (HRNE), a novel approach to video representation with an application to video captioning. HRNE leverages temporal information in videos, thus addressing a fundamental challenge in video content analysis where temporal structure plays a critical role. The paper proposes a multi-layered, hierarchical approach that aims to enhance the capture of long-range temporal dependencies, increase model efficiency, and improve video representation granularity.

Contributions and Methodology

The paper highlights three key contributions of the HRNE model:

1. Extended Temporal Structure Modeling: The encoder reduces the input information flow length and composes multiple consecutive inputs at higher levels, enabling more efficient and longer-range temporal structure modeling.
2. Reduced Computational Complexity: The approach reduces computational costs while introducing additional non-linearity, leading to better performance without the burden of increased computation typically associated with deep layers.
3. Multigranular Temporal Transitions: The model efficiently captures temporal transitions at multiple granularities, representing both the transitions between individual frames and segment-level transitions within videos.

HRNE integrates a two-layer hierarchical recurrent structure with the capability to process sequences over extended time frames. It leverages Long Short-Term Memory (LSTM) networks for recurrent processing, making it more adept at handling long-term dependencies compared to traditional methods. Moreover, the attention mechanism is incorporated to focus on critical temporal locations within the video, dynamically adjusting the importance of different frames during the representation process.

Experimental Results

The HRNE model demonstrates superior performance in video captioning tasks over several benchmarks, mainly using the Microsoft Video Description Corpus (MSVD) and the Montreal Video Annotation Dataset (M-VAD). The results indicate HRNE's effectiveness in achieving higher METEOR scores—33.1% on MSVD with the HRNE plus attention model surpassing existing methods, even outperforming systems that use multiple combined features. Furthermore, the model shows improved results on the more challenging M-VAD dataset, achieving a significant 6.8% in METEOR compared to other methods.

Implications and Future Work

Practically, this advancement in video representation has substantial implications for improving various video analysis applications, including video classification, retrieval, and event detection. Theoretical implications are profound, suggesting that hierarchical architectures with recurrent networks can model complex, layered data efficiently. The hierarchical approach presents a promising avenue for further exploration in temporal data representation.

Looking ahead, further work could explore expanding HRNE's applicability to other video analytics domains. Future research could also delve into optimizing the model for longer videos, exploring additional hierarchical levels, or integrating other forms of input data to enhance video understanding in a broader context.

In summary, the HRNE represents a significant step forward in video representation by effectively capturing and leveraging temporal dependencies, offering robust insights and opportunities for developing advanced video content analysis systems.