Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning (2003.05162v4)

Abstract: Captioning is a crucial and challenging task for video understanding. In videos that involve active agents such as humans, the agent's actions can bring about myriad changes in the scene. Observable changes such as movements, manipulations, and transformations of the objects in the scene, are reflected in conventional video captioning. Unlike images, actions in videos are also inherently linked to social aspects such as intentions (why the action is taking place), effects (what changes due to the action), and attributes that describe the agent. Thus for video understanding, such as when captioning videos or when answering questions about videos, one must have an understanding of these commonsense aspects. We present the first work on generating commonsense captions directly from videos, to describe latent aspects such as intentions, effects, and attributes. We present a new dataset "Video-to-Commonsense (V2C)" that contains $\sim9k$ videos of human agents performing various actions, annotated with 3 types of commonsense descriptions. Additionally we explore the use of open-ended video-based commonsense question answering (V2C-QA) as a way to enrich our captions. Both the generation task and the QA task can be used to enrich video captions.

Overview of Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning

The paper "Video2Commonsense: Generating Commonsense Descriptions to Enrich Video Captioning" introduces a novel framework aimed at enhancing conventional video captioning by generating commonsense descriptions directly from video content. Video captioning traditionally focuses on identifying and describing observable objects and actions within a scene. However, the paper argues for an enriched understanding that encompasses latent attributes such as intentions driving actions, as well as the effects and inherent attributes of the active agents in the video.

Key Contributions and Methodology

The authors present "Video-to-Commonsense (V2C)," a dataset comprising approximately 9,000 videos annotated with three types of commonsense descriptions: intentions, effects, and attributes. This forms the foundation for training and evaluating models designed to produce commonsense-enhanced captions. The paper introduces the V2C-Transformer architecture, which utilizes a video encoder and a transformer decoder with cross-modal self-attention, capable of generating both conventional captions and enriched commonsense descriptions.

Core Strategies:

1. Video Encoder & Transformer Decoder:
   • The video encoder leverages ResNet-152 features processed through an LSTM model to create global video representations. The transformer decoder operates in two stages: it first generates factual captions and then commonsense descriptions using these representations.
2. Dataset Annotation:
   • The V2C dataset construction involves automated retrieval from the ATOMIC commonsense knowledge base and subsequent refinement through human annotations. The annotation process not only ensures visual grounding but also enhances linguistic diversity and relevance.
3. V2C-QA Framework:
   • To further explore the capacity for commonsense understanding, the paper introduces a video question-answering (QA) task involving open-ended questions about latent aspects of the video, thus enriching caption generation.

Evaluation Metrics and Results

The authors assess their model using standard metrics such as BLEU, METEOR, ROUGE, and human evaluation involving Amazon Mechanical Turk (AMT) workers. The V2C-Transformer demonstrated significant improvements over baseline models, particularly in generating descriptions that incorporate inferential commonsense aspects, rather than mere observable details. The dataset's quality was validated through human oversight, highlighting its potential in supporting reasoning-based applications in AI.

Implications and Future Directions

The work presented in this paper is pivotal for advancing video understanding capabilities in AI. By incorporating commonsense reasoning, it opens avenues for developing systems that can better interpret human actions and motivations, enhancing interactive AI applications such as personal assistants and automated video content analysis. Future research could investigate expanding the commonsense reasoning across varied contexts and integrating real-time predictive models for dynamic scene analysis.

In summary, the Video2Commonsense framework establishes a strong baseline for incorporating commonsense knowledge into video captioning, indicating a promising direction for enriched AI comprehension and interaction.