Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning (1706.01231v1)

Abstract: Recent progress has been made in using attention based encoder-decoder framework for video captioning. However, most existing decoders apply the attention mechanism to every generated word including both visual words (e.g., "gun" and "shooting") and non-visual words (e.g. "the", "a"). However, these non-visual words can be easily predicted using natural LLM without considering visual signals or attention. Imposing attention mechanism on non-visual words could mislead and decrease the overall performance of video captioning. To address this issue, we propose a hierarchical LSTM with adjusted temporal attention (hLSTMat) approach for video captioning. Specifically, the proposed framework utilizes the temporal attention for selecting specific frames to predict the related words, while the adjusted temporal attention is for deciding whether to depend on the visual information or the language context information. Also, a hierarchical LSTMs is designed to simultaneously consider both low-level visual information and high-level language context information to support the video caption generation. To demonstrate the effectiveness of our proposed framework, we test our method on two prevalent datasets: MSVD and MSR-VTT, and experimental results show that our approach outperforms the state-of-the-art methods on both two datasets.

Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning: A Technical Overview

The paper "Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning" presents an innovative approach to video captioning, focusing on the integration of temporal attention mechanisms with hierarchical Long Short-Term Memory (LSTM) networks. This framework addresses the challenges inherent in video captioning, such as diverse scenes, actions, and objects, while aiming to improve the generation of natural language descriptions from video content.

Methodology and Approach

The authors introduce the hierarchical LSTM architecture, termed hLSTMat, which employs two layers of LSTMs to capture different levels of information: low-level visual features and high-level language context. This hierarchical structure is pivotal in managing the complexity of video data by leveraging temporal attention mechanisms. The temporal attention layer selects specific frames in the video based on the relevance to the context, while the adjusted temporal attention enables the model to decide whether visual information or language context should dominate the prediction for the subsequent word.

The adjusted temporal attention mechanism is crucial as it avoids misdirecting attention on non-visual words, potentially improving the accuracy and coherence of the caption generation. By adjusting the level of attention on visual versus contextual information dynamically, the model enhances the robustness of the captioning output.

Experimental Results

The proposed method was benchmarked against state-of-the-art video captioning frameworks using two datasets: MSVD and MSR-VTT. On both datasets, hLSTMat demonstrated superior performance, achieving notable improvements in BLEU and METEOR scores compared to existing methods. For instance, hLSTMat attained a BLEU-4 score of 53.0 and a METEOR score of 33.6 on the MSVD dataset, establishing itself as a leading approach in video captioning.

Implications

The hierarchical framework with adjusted temporal attention proposed in this paper has significant implications for the field of machine intelligence and video processing. By effectively managing the balance between visual data and language context, this approach could revolutionize automatic video subtitle generation, enhance video retrieval systems, and improve navigation aids for visually impaired users.

Future Developments

Future work may focus on integrating both spatial and temporal video features to further augment the performance of video captioning models. Additionally, exploring alternative neural network architectures and attention mechanisms could provide insights into better handling the rich semantic content present in videos.

In conclusion, the hLSTMat approach represents a promising advance in video captioning technology, highlighting the importance of context-sensitive attention mechanisms in natural language processing tasks. This paper provides a comprehensive view of how leveraging hierarchical structures and adaptive attention can transform video-to-text translation in various applications.