NExT-QA:Next Phase of Question-Answering to Explaining Temporal Actions (2105.08276v2)

Abstract: We introduce NExT-QA, a rigorously designed video question answering (VideoQA) benchmark to advance video understanding from describing to explaining the temporal actions. Based on the dataset, we set up multi-choice and open-ended QA tasks targeting causal action reasoning, temporal action reasoning, and common scene comprehension. Through extensive analysis of baselines and established VideoQA techniques, we find that top-performing methods excel at shallow scene descriptions but are weak in causal and temporal action reasoning. Furthermore, the models that are effective on multi-choice QA, when adapted to open-ended QA, still struggle in generalizing the answers. This raises doubt on the ability of these models to reason and highlights possibilities for improvement. With detailed results for different question types and heuristic observations for future works, we hope NExT-QA will guide the next generation of VQA research to go beyond superficial scene description towards a deeper understanding of videos. (The dataset and related resources are available at https://github.com/doc-doc/NExT-QA.git)

An Overview of the NExT-QA Benchmark for Video Question Answering

The paper "NExT-QA: Next Phase of Question-Answering to Explaining Temporal Actions" introduces a novel video question answering (VideoQA) benchmark designed to advance the understanding of videos beyond mere description toward explaining temporal actions. The NExT-QA dataset focuses on enabling models to reason about causal and temporal relationships within video content, which remain underexplored challenges in video comprehension and question answering tasks. This benchmark fosters the development of models capable of deeper video content understanding, specifically targeting causal and temporal reasoning alongside scene recognition tasks.

Dataset Composition and Objectives

NExT-QA comprises a manually annotated dataset including approximately 5,440 videos and around 52,000 question-answer pairs. The dataset is organized into three main types of questions: causal, temporal, and descriptive. Each category poses unique challenges and fosters a comprehensive evaluation of VideoQA models:

• Causal Questions require models to infer reasons for observed actions or predict intentions behind previous actions within the video.
• Temporal Questions focus on understanding actions in time, asking for relational order such as “what occurs before” or “what follows after” certain events.
• Descriptive Questions emphasize scene description and recognition, which includes identifying objects, locations, and actions.

Two tasks, multi-choice and open-ended QA, are proposed based on these questions. The former involves selecting the correct answer from a set of candidates, while the latter requires generating answers directly from video content and the posed question.

Analysis of Current Techniques

Through rigorous experimentation, the paper evaluates several state-of-the-art VideoQA models on the NExT-QA dataset. Adaptive evaluation settings reveal critical insights:

• Baseline Comparisons: Traditional methods like selecting the longest answer or employing semantic similarity measures achieve subpar results, underscoring the challenge of reasoning-based questions in NExT-QA.
• Performance of SOTA Models: The analysis finds models excel in answering basic descriptive questions but falter significantly in causal and temporal reasoning tasks. This discrepancy indicates a gap in current models' ability to capture deeper video semantics beyond surface-level visual recognition.

The investigation also highlights the importance of effective integration of visual features and text embeddings. Models leveraging fine-tuned BERT representations for text exhibited improved results over traditional embeddings like GloVe, especially in reasoning-intensive tasks.

Implications and Future Directions

The research paper offers valuable insights into the current state and challenges of VideoQA, emphasizing the potential for advancements in video understanding methodologies. It encourages further exploration in several key areas:

• Extended Use of Pre-trained Models: The use of robust pre-trained models like BERT shows promise and warrants deeper investigation into their applications and adaptations specific to video contexts, including temporal and causal reasoning.
• Graph-based Reasoning: Given the successes of heterogeneous graph reasoning models on NExT-QA, the paper suggests that graph-based approaches might be particularly effective for capturing complex dependencies within video data.
• Performance on Open-ended QA: The metrics on open-ended QA tasks reveal deficiencies in current models' ability to generate coherent and contextually appropriate answers, indicating a need for future research into more effective answer generation techniques.

In summary, the NExT-QA benchmark serves as a pivotal step towards more sophisticated video comprehension models by introducing a carefully designed dataset that challenges conventional capabilities in VideoQA with its focus on causal and temporal action reasoning. Subsequent research can leverage the benchmark's insights to improve reasoning capabilities, bridging existing model shortcomings and ultimately advancing AI's ability to process and understand dynamic video content with human-like proficiency.