Movie Description (1605.03705v1)

Abstract: Audio Description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. In total the Large Scale Movie Description Challenge (LSMDC) contains a parallel corpus of 118,114 sentences and video clips from 202 movies. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are indeed more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in a challenge organized in the context of the workshop "Describing and Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at ICCV 2015.

• The paper introduces the LSMDC dataset, integrating 118,114 video clips from 202 movies to advance automated movie description.
• It employs dual methodologies, using semantic parsing with SMT and visual classifier-driven LSTM networks, to translate visual cues into text.
• The study validates its approaches with metrics like BLEU and METEOR, paving the way for future multimodal narrative generation research.

An Overview of the Large Scale Movie Description Challenge

The paper "Movie Description" presents an in-depth exploration into the field of automated movie description, focusing on the development and evaluation of a robust dataset: the Large Scale Movie Description Challenge (LSMDC). This dataset is an amalgamation of the MPII-MD and M-VAD datasets and is designed to facilitate advancements in video understanding through both computer vision and natural language processing.

Dataset Construction and Properties

LSMDC comprises 202 movies and is curated to include 118,114 video clips with temporal alignments to corresponding descriptive sentences, sourced primarily from audio descriptions (ADs) and scripts. ADs, being linguistic narratives integrated into movies primarily for visually impaired audiences, offer rich, visual-centric content, often more aligned with what is visually depicted than scripts produced during pre-production stages. The AD component, coupled with scripts where available, forms a comprehensive corpus ideal for training and benchmarking video description models.

Methodological Approaches

The authors adopt a dual-faceted methodological approach for the task of movie description. Initially, they employ a parsing mechanism to extract semantic representations (SRs) from the natural language descriptors. These SRs, embodying elements such as subjects, verbs, objects, and locations, serve as intermediary structures bridging the gaps between visual data and linguistic counterparts.

Two primary video description methodologies are elaborated upon in the paper:

1. Semantic Parsing and SMT: This approach leverages statistical machine translation (SMT) models to convert visual features into textual descriptions using parsed semantic labels. The SR extraction informs a conditional random field (CRF) to predict high-probability sentence representations which are then translated into coherent sentences.
2. Visual-Labels and LSTM: This method involves constructing classifiers from the visual labels identified in the dataset, grouped into semantic categories (verbs, objects, places) for focused training. These classifiers' output becomes input to a Long Short-Term Memory (LSTM) network tasked with the sentence generation process, standing out due to its strategic utilization of robust visual classification and advanced neural architectures.

Evaluation Metrics and Implications

Automatic metrics like BLEU, METEOR, ROUGE, and CIDEr facilitate objective assessment, while human evaluations provide nuanced insights into grammatical correctness, relevance, and usefulness. The findings underscore the complexity of visual semantics, especially considering the abstract or nuanced elements in movie scenes that require not only accurate object and action recognition but also contextual understanding over extended narrative arcs.

Insights and Future Directions

LSMDC's establishment paves the way for novel research directions, emphasizing the integration of multimodal data for richer narrative generation. Future prospects in automated video description may explore improved object detection linked with emotion recognition and scene semantics, potentially advancing interactive AI applications capable of real-time movie summarization and comprehension. The paper also hints at the ongoing challenge of grappling with diverse vocabulary and sentence structures present in open-domain video datasets, presenting opportunities for innovation in model architectures.

This comprehensive paper lays a significant foundation for subsequent scholarly exploration and development in the automated description of complex visual content, enhancing media accessibility and advancing Artificial Intelligence's understanding of human-like narrative generation.