DeepStory: Video Story QA by Deep Embedded Memory Networks (1707.00836v1)

Abstract: Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children's cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.

DeepStory: Video Story QA by Deep Embedded Memory Networks

The paper "DeepStory: Video Story QA by Deep Embedded Memory Networks" introduces a novel approach to video story question-answering (QA) tasks by leveraging Deep Embedded Memory Networks (DEMN). This research addresses the inadequacies faced in video QA models compared to their text and image counterparts, meticulously crafting a coherent learning framework that integrates scene and dialogue information from videos. This paper demonstrates the efficacy of DEMN, a model that reconstructs video stories from continuous video streams, accumulating both visual sequences and linguistic dialogues into latent embedding spaces. As a key feature, the model utilizes a long-term memory system to retain pertinent video content and employs a Long Short-Term Memory (LSTM)-based attention mechanism to accurately respond to questions by pinpointing salient keywords within the narratives.

The researchers have built upon foundational deep learning concepts, eschewing the traditional hand-crafted features that characterized earlier QA models. The DEMN framework thus capitalizes on the synergy between neural memory networks and attention models, which bolsters its performance across video domains. The model was subject to rigorous evaluation using the PororoQA dataset—a robust collection tailored from the children's cartoon "Pororo"—and the public MovieQA benchmark. PororoQA features over 16,000 scene-dialogue pairs and nearly 9,000 QA pairs, making it an exceptional resource for testing video QA models due to its coherent narrative structures and high-quality scene descriptions.

Key Findings and Numerical Results

One of the significant results documented in this paper is that DEMN outstripped the performance of various rival QA models, including standard visual question-answering architectures, on both the PororoQA and MovieQA datasets. On PororoQA, DEMN achieved a QA accuracy of up to 68% with MRR scores as high as 0.26, underscoring the benefits of combining visual and linguistic information processing. Furthermore, the model attained state-of-the-art results on MovieQA, scoring 44.7% accuracy on the validation set and 30% on the test set in video QA mode.

The DEMN framework's ability to integrate disparate sources of information from videos into coherent storylines also indicates its potential disruptive impact on understanding complex multimodal data. This innovative approach potentially simplifies the optimization of story structures in video story QA tasks and shows promising future applications in more extensive and intricate video datasets.

Theoretical and Practical Implications

Theoretical implications of this work involve an improved comprehension of narrative structures within video data, paving the path for further exploration in multimodal data representation. The paper posits that the combination of visual and linguistic analysis through DEMN can significantly optimize video-centered AI models beyond those used in conventional domains like text and image processing.

Practically, the advancement of DEMN in video QA presents a reliable model for applications in video indexing, real-time content analysis, education, and even entertainment. The capacity to predict or construct narrative understanding from video data can enhance the technology used in multimedia analytics, providing enhanced user experiences in interactive media and chatbots.

Future Directions

Looking ahead, deeper exploration into methods like curriculum learning may refine strategies to deal with complex video structures, as suggested by the authors. The integration of DEMN models with more elaborate datasets could lead to the development of AI agents with even better comprehension abilities across various domains, thus catalyzing progress towards human-level intelligence in AI systems.

In summary, the "DeepStory" paper offers an impactful contribution toward the field of video story QA, providing a robust framework to handle intricacies of multimodal data and setting a benchmark for future research in automated video analysis and storytelling.