MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2005.05402v1)

Abstract: Generating multi-sentence descriptions for videos is one of the most challenging captioning tasks due to its high requirements for not only visual relevance but also discourse-based coherence across the sentences in the paragraph. Towards this goal, we propose a new approach called Memory-Augmented Recurrent Transformer (MART), which uses a memory module to augment the transformer architecture. The memory module generates a highly summarized memory state from the video segments and the sentence history so as to help better prediction of the next sentence (w.r.t. coreference and repetition aspects), thus encouraging coherent paragraph generation. Extensive experiments, human evaluations, and qualitative analyses on two popular datasets ActivityNet Captions and YouCookII show that MART generates more coherent and less repetitive paragraph captions than baseline methods, while maintaining relevance to the input video events. All code is available open-source at: https://github.com/jayleicn/recurrent-transformer

Analysis of Memory-Augmented Recurrent Transformer for Video Paragraph Captioning

The paper introduces the Memory-Augmented Recurrent Transformer (MART), a sophisticated model for video paragraph captioning tasks. Video paragraph captioning presents unique challenges due to the dual requirements of visual relevance and narrative coherence across multiple sentences. Unlike previous approaches, MART aims to enhance the transformers with an external memory module that augments its capability to utilize historical video and sentence data, thereby ensuring coherence and minimizing redundancy in the generated paragraphs.

The underlying architecture of MART is built upon the transformer model, which has widely surpassed the capabilities of RNN-based methods, such as LSTMs and GRUs, across myriad sequence-based tasks. The novelty in MART lies in its integration of a memory module that acts as a repository of contextually enriched content from previous segments. This module captures coreference and manages information flow across segments, thus enabling more coherent paragraph construction.

MART was rigorously evaluated on two prominent datasets, ActivityNet Captions and YouCookII, showing substantial improvements over existing baseline models, including the vanilla transformer and the Transformer-XL variations. Key metrics such as BLEU@4, METEOR, and CIDEr-D were utilized alongside a novel repetition metric, R@4, which provided insights into the repetitive nature of model-generated descriptions. MART displayed a significant reduction in repetitive sentence structures, highlighting its superior capability in maintaining narrative consistency.

A detailed comparison with Transformer-XL illuminated the efficacy of MART's memory module. While Transformer-XL attempts to create context repetition by directly incorporating past segment states, MART opts for a memory-efficient approach through highly summarized states that enhance semantic cohesion across sentences. This functional differentiation imbues MART with the ability to produce less redundant paragraphs without any compromise on visual accuracy.

The implications of this model are substantial both in practical applications and theoretical advancements. Practically, MART can enhance multiple domains where video content needs coherent and dynamically generated textual descriptions, such as media content management and automated reporting systems. Theoretically, it explores and demonstrates the potential of external memory in augmenting transformers, hinting at future directions for more sophisticated models that can leverage memory-like structures for various sequential tasks.

Looking forward, the possibility of embedding even more sophisticated memory components could be explored. This includes integrating differentiable memory architectures or even hybrid models that benefit from both transformer and memory insights. Moreover, addressing the inherent limitations observed, such as in fine-grained detail recognition, suggests a pathway towards multimodal models with deeper visual understanding capabilities.

In conclusion, the Memory-Augmented Recurrent Transformer model represents a significant step in advancing video paragraph captioning tasks, presenting robust methods to enhance coherence and visual narrative through the meaningful integration of memory modules within transformer architectures.