CausalVQA: A New Benchmark Dataset for Video Question Answering

In the ever-evolving landscape of artificial intelligence, Video Question Answering (VQA) stands as a critical research direction, garnering significant attention. However, existing VQA benchmark datasets suffer from notable limitations, either focusing on superficial perceptual understanding of real-world videos or being confined to narrow physical reasoning questions created within simulated environments. To bridge this gap, the CausalVQA benchmark dataset emerges, aiming to revolutionize how we evaluate AI models’ ability to reason about causal relationships in the physical world.

Introduction to CausalVQA

CausalVQA is a groundbreaking benchmark dataset for video question answering, composed of carefully crafted question-answer pairs designed to test models’ comprehension of causality in real-world scenarios. Unlike its predecessors, this dataset presents challenging questions rooted in authentic situations, focusing on a model’s capacity to predict outcomes of various actions and events through five distinct question types: counterfactual, hypothetical, anticipatory, planning, and descriptive.

The Motivation Behind CausalVQA

The limitations of existing VQA benchmarks are evident. They often fail to adequately assess a model’s causal reasoning abilities in real-world contexts. CausalVQA was designed to address this deficiency, incorporating robust quality control mechanisms that force models to base their answers on deep visual understanding rather than relying on linguistic cues. This approach ensures a more accurate evaluation of a model’s true capabilities.

Significance and Value

The introduction of CausalVQA marks a pivotal moment in VQA research, providing a more challenging and realistic benchmark. By using this dataset, researchers can comprehensively evaluate how well models perform in causal reasoning, identify their shortcomings, and drive innovations to improve them. Moreover, CausalVQA serves as an essential tool for evaluating physical world models, contributing to advancements in related fields.

Key Features of the CausalVQA Dataset

Diverse Question Types

CausalVQA comprises five distinct question types, each designed to probe different aspects of a model’s reasoning abilities:

  1. Counterfactual Questions
    These questions ask models to consider what would happen if a particular event had not occurred. For example, “What if the ball had not been pushed in the video? How would the subsequent events differ?” Counterfactual questions test a model’s ability to reason backward from effects to causes and predict alternative outcomes.

  2. Hypothetical Questions
    Hypothetical questions present imaginary scenarios and ask models to predict the results. For instance, “Suppose a new obstacle suddenly appears in the scene. How would the objects in the video react?” These questions challenge a model’s imagination and its capacity to forecast outcomes under novel conditions.

  3. Anticipatory Questions
    Anticipatory questions require models to predict future events based on the current video scene. For example, “In a video showing a ball rolling down a slope, where will the ball be in the next frame?” Answering such questions demands a model to understand spatial-temporal dynamics and apply physical principles.

  4. Planning Questions
    Planning questions task models with devising a feasible action plan to achieve a goal within a given video scenario. For instance, “How can an object reach a specific target location in a scene with obstacles?” These questions assess a model’s logical reasoning and decision-making abilities.

  5. Descriptive Questions
    Descriptive questions ask models to describe elements within a video, such as an object’s appearance, color, or motion. For example, “Describe the movement of the car in the video.” These questions evaluate a model’s visual perception and language expression capabilities.

Robust Quality Control Mechanisms

CausalVQA incorporates sophisticated quality control measures to ensure models cannot rely on trivial shortcuts. These mechanisms prevent models from depending solely on linguistic cues, forcing them to engage in deep visual analysis. This approach enhances the reliability and accuracy of evaluations, providing a truer measure of a model’s capabilities.

Performance Gap Between Models and Humans

Tests on current cutting-edge multimodal models using the CausalVQA benchmark reveal a significant performance gap compared to human reasoning. Models particularly struggle with anticipatory and hypothetical questions, highlighting challenges in leveraging spatio-temporal reasoning, understanding physical principles, and comprehending alternative possibilities. This gap underscores the need for advancements in developing models that can make accurate predictions in real-world scenarios.

Resources and Usage Guide for CausalVQA

Related Links

CausalVQA provides several essential resources for researchers:

  1. Paper Link: https://ai.meta.com/research/publications/causalvqa-a-physically-grounded-causal-reasoning-benchmark-for-video-models
    Access the detailed research paper to understand the dataset’s design principles, construction methods, and experimental results.

  2. Blog Link: https://ai.meta.com/blog/v-jepa-2-world-model-benchmarks
    Stay updated with the latest developments and insights about CausalVQA through this blog.

  3. Leaderboard Link: https://huggingface.co/spaces/facebook/pwm_leaderboard
    Check out the leaderboard to see how different models perform on the CausalVQA benchmark and compare their capabilities.

Step-by-Step Guide to Download and Use the Dataset

1. Obtain an Ego4D License

First, visit https://ego4ddataset.com/egoexo-license/ to sign up for an Ego4D license. Accept the terms of the license agreement and allow up to 48 hours for approval. Upon approval, you will receive an ID and secret via email, which are necessary for using the AWS S3 CLI tool.

2. Install and Configure the AWS CLI Tool

Download the AWS CLI tool from https://github.com/aws/aws-cli/tree/v2. After installation, run aws configure in the command line and follow the prompts to enter the ID and secret received via email. There is no need to specify a region or output format.

3. Download the Dataset

Use the AWS S3 CLI tool to download the dataset with the following command:

aws s3 cp s3://ego4d-consortium-sharing/egoexo-public/v2/causal_vqa/CausalVQA.zip <your location>\CausalVQA.zip

Replace <your location> with your desired local directory.

4. Clone the CausalVQA Repository

Clone the CausalVQA repository to your local machine and copy the dataset contents to the repository directory:

# Clone the repository
git clone <CausalVQA repo url>
cd CausalVQA
mkdir data
cd ..
unzip CausalVQA.zip -d CausalVQA_data
mv CausalVQA_data/CausalVQA/test CausalVQA/data
mv CausalVQA_data/CausalVQA/debug CausalVQA/data

After these steps, the directory structure should be:

CausalVQA/
├── lmms-eval/
├── models/
├── scripts/
├── tasks/
├── data/
    └── debug/
    └── test/

5. Set Up the Environment and Dependencies

CausalVQA provides a Makefile to facilitate environment setup:

make setup_env
conda activate causalvqa_eval
make setup_vllm
make setup_lmms_eval
make setup_plm
make setup_cleanup
make prep_debug_data

Each command may take time to execute. Follow the prompts carefully. Note that metrics will only be generated for the debug set; the test set provides video segments, questions, and answer options, but correct answers are withheld.

6. Prepare for Evaluation

Before evaluation, copy the tasks to lmms_eval and update models. Replace <add absolute ref> in the dataset path with the correct absolute path; otherwise, the dataset will not load. Then run:

make prep_evals

7. Run Evaluations

CausalVQA provides evaluation parameters in the Makefile. Different models require specific configurations (e.g., gemini_oai and gpt4o need API keys and host locations). Run evaluations with the following commands:

make run_internvl2_5
make run_llava_onevision
make run_qwen2_5vl_vllm
make run_plm
make run_gemini_oai
make run_gpt4o

Contents of Annotation Files

Each annotation file includes the following information:

  • qid: A unique question identifier for pairing.
  • type: The question type (anticipatory, counterfactual, descriptive, planning, hypothetical).
  • question: The text of the question.
  • choices1: Multiple-choice answers.
  • correct1: The correct answer for choices1 (removed from the test set).
  • choices2: A perturbed and reordered set of multiple-choice answers.
  • correct2: The correct answer for choices2 (removed from the test set).
  • difficulty: The difficulty level based on human baselines.
  • renamed_video: The name of the video file.

These annotations provide detailed context for evaluating model performance.

Limitations and Future Directions for CausalVQA

Current Limitations

While CausalVQA represents a significant advancement in VQA benchmarking, it has several limitations:

  1. Limited Data Coverage: Although grounded in real-world scenarios, CausalVQA may not encompass all possible physical phenomena or complex situations. Some rare or extreme cases might not be adequately represented, potentially affecting model performance in those contexts.

  2. Incomplete Evaluation of Model Abilities: CausalVQA primarily focuses on causal reasoning, but VQA also involves other skills like semantic understanding and emotional analysis. A comprehensive evaluation of model capabilities requires addressing these additional dimensions.

  3. Subjectivity in Human Annotation: The difficulty levels of questions are determined through human trials, which introduces subjectivity. Different annotators may perceive the same question differently, potentially affecting the accuracy of difficulty rankings.

Future Development Directions

To address these limitations, future research can focus on:

  1. Expanding Data Coverage: Collect more diverse video data featuring a wider range of scenarios and physical phenomena to enhance the dataset’s comprehensiveness and representativeness. This expansion will help models generalize better to real-world situations.

  2. Holistic Model Evaluation: Incorporate additional evaluation metrics for skills like semantic understanding and emotional analysis to develop a more comprehensive assessment framework. This approach will provide a more accurate picture of overall model performance.

  3. Improving Annotation Methods: Develop more scientific and objective annotation techniques to reduce subjectivity. For example, use multiple annotators and statistical methods to determine difficulty levels, enhancing annotation reliability.

  4. Cross-Domain Integration: Integrate CausalVQA with other fields such as robotics and autonomous driving. This integration will enable practical applications of causal reasoning research, driving advancements in real-world technologies.

Conclusion

CausalVQA emerges as a transformative benchmark dataset, reshaping how we evaluate video question answering systems. With its diverse question types, rigorous quality control, and focus on real-world causality, it provides a robust framework for assessing model capabilities. The detailed usage guide ensures researchers can easily access and utilize the dataset, fostering innovation in the field.

While CausalVQA has limitations, ongoing improvements will only enhance its utility. As technology advances, this benchmark will play an increasingly important role in driving AI development, enabling models to better understand and interact with the physical world. For both academic research and industrial applications, CausalVQA offers invaluable insights, paving the way for more intelligent and capable AI systems that can reason causally and make accurate predictions in real-world scenarios. The journey toward truly human-like AI reasoning begins with benchmarks like CausalVQA, and the possibilities for future advancements are boundless.