Grounded Question-Answering in Long Egocentric Videos (2312.06505v4)

Abstract: Existing approaches to video understanding, mainly designed for short videos from a third-person perspective, are limited in their applicability in certain fields, such as robotics. In this paper, we delve into open-ended question-answering (QA) in long, egocentric videos, which allows individuals or robots to inquire about their own past visual experiences. This task presents unique challenges, including the complexity of temporally grounding queries within extensive video content, the high resource demands for precise data annotation, and the inherent difficulty of evaluating open-ended answers due to their ambiguous nature. Our proposed approach tackles these challenges by (i) integrating query grounding and answering within a unified model to reduce error propagation; (ii) employing LLMs for efficient and scalable data synthesis; and (iii) introducing a close-ended QA task for evaluation, to manage answer ambiguity. Extensive experiments demonstrate the effectiveness of our method, which also achieves state-of-the-art performance on the QaEgo4D and Ego4D-NLQ benchmarks. Code, data, and models are available at https://github.com/Becomebright/GroundVQA.

Insights into Grounded Question-Answering in Long Egocentric Videos

The paper "Grounded Question-Answering in Long Egocentric Videos" by Shangzhe Di and Weidi Xie addresses a challenging niche within the video understanding domain, specifically focusing on egocentric videos and presenting a grounded approach to question-answering. This field has predominantly dealt with short, third-person-view videos. However, with the emergence of datasets like Ego4D, which comprises long, first-person perspective videos, there is a pressing need to adapt and evolve video understanding methodologies to handle such data efficiently.

Core Contributions and Methodology

The paper introduces a model designed to tackle the dual task of temporal grounding and question answering within long egocentric video contexts, termed as GroundVQA. The authors identify several unique challenges inherent to this task category: the difficulty of anchoring queries temporally across extended video sequences, the intensive resources required for credible data annotation, and the general challenge of evaluating open-ended answers due to inherent ambiguities.

Key aspects of the approach include:

1. Unified Model Architecture: The integration of query grounding and answers generation within a single model framework reduces error propagation, a major concern when chaining multiple specialized models. Synchronicity between these processes is shown to ameliorate error accumulation, benefiting from the synergy present in multi-task learning frameworks.
2. Data Generation: The authors make extensive use of LLMs to generate training samples from copious narrations available in the Ego4D dataset. This creative use of LLMs alleviates the overfitting risk typically associated with limited training datasets by synthesizing over 303K training samples from narrations.
3. Evaluation with CloseQA Task: To tackle the ambiguity in open-ended answers evaluation, a close-ended QA task is developed. This involves the creation of multiple-choice questions, adding a new layer to the evaluation, which in turn assists in a more transparent assessment of the model's competence.

Results

The experimental outcomes demonstrate that the proposed GroundVQA model not only achieves state-of-the-art results on QaEgo4D and Ego4D-NLQ benchmarks but also elucidates the benefit of integrating temporal grounding into QA tasks. The model's competency extends to outperform existing methodologies, such as those relying merely on open-ended QA evaluation metrics like BLEU or METEOR by employing more pertinent tasks and subjective performance measures.

Implications

The implications of these findings are manifold:

1. Real-World Application: The proposed methods hold significant promise for applications in robotics and augmented reality, where understanding and querying past experiences can lead to more interactive and intelligent systems.
2. Data Efficiencies: By leveraging LLMs for data generation, the paper presents a cost-effective paradigm for training large-scale video understanding models, opening opportunities for systems trained on synthetically annotated datasets.
3. Model Development: Given the eye-opening results with unified modeling, broader applications can leverage similar multi-task learning architectural strategies to address diverse challenges within AI.

Future Work

The research opens multiple avenues for future exploration:

• Enhancing the granularity of grounded temporal segments to improve the accuracy of the QA tasks.
• Extending the use of LLMs beyond data generation to direct integration within the video analysis pipeline could further drive advancements.
• Exploration of advanced evaluation metrics catered to contextual understanding and multi-modal data interactions.

Overall, this paper not only addresses a significant gap in egocentric video understanding but also provides robust methodological insights and results that lay down groundwork for further enhancements in AI's capabilities to perceive, interpret, and reason with video data.