Información - Video Question Answering - # Modular reasoning for video question answering

Modular Reasoning for Effective Video Question Answering: Decomposing Planning and Execution for Interpretable and Accurate Multimodal Understanding

Q: How can the multi-stage modular design of MoReVQA be extended to other video-language tasks beyond question answering, such as video captioning or video retrieval

The multi-stage modular design of MoReVQA can be extended to other video-language tasks by adapting the stages to suit the specific requirements of each task. For video captioning, the event parsing stage can focus on identifying key elements in the video that need to be described, the grounding stage can determine the most relevant frames for generating captions, and the reasoning stage can combine the visual and language information to create coherent and informative captions. For video retrieval, the event parsing stage can extract important details about the query or search intent, the grounding stage can identify relevant videos based on the query, and the reasoning stage can rank and retrieve the most suitable videos. By customizing the prompts, APIs, and memory management in each stage, MoReVQA can effectively tackle a variety of video-language tasks beyond question answering.

Q: What are the potential limitations or failure modes of the MoReVQA approach, and how could they be addressed in future work

One potential limitation of MoReVQA could be the complexity and computational cost associated with running multiple stages for each inference. This could lead to slower processing times and higher resource requirements. To address this, future work could focus on optimizing the architecture and algorithms used in each stage to improve efficiency without compromising accuracy. Another limitation could be the reliance on pre-trained models for the APIs in each stage, which may not always generalize well to new tasks or domains. To mitigate this, ongoing model updates and fine-tuning on task-specific data could enhance the performance of MoReVQA across a wider range of scenarios. Additionally, the interpretability of the intermediate outputs generated by each stage could be further improved to provide more detailed insights into the decision-making process of the model. This could involve developing visualization techniques or explanation methods to enhance the transparency of the system.

Q: Given the strong performance of the simple JCEF baseline, how can the modular components of MoReVQA be further improved or combined in novel ways to push the boundaries of video question answering capabilities

To further enhance the modular components of MoReVQA and push the boundaries of video question answering capabilities, several strategies can be considered: Dynamic Module Selection: Implement a mechanism that dynamically selects and combines different modules based on the complexity of the question or the characteristics of the video. This adaptive approach can improve the efficiency and accuracy of the system. Attention Mechanisms: Integrate attention mechanisms within each stage to focus on relevant information and ignore irrelevant details. This can help improve the model's ability to extract key features from the video and question. Transfer Learning: Explore transfer learning techniques to fine-tune the pre-trained models used in MoReVQA on specific video question answering datasets. This can help the model adapt to the nuances of different tasks and improve overall performance. Ensemble Methods: Combine the outputs of multiple instances of MoReVQA with variations in hyperparameters or input data to create an ensemble model. This ensemble approach can enhance robustness and accuracy by leveraging diverse perspectives. By incorporating these advanced strategies and continuously refining the modular components, MoReVQA can achieve even higher levels of performance and set new benchmarks in video question answering capabilities.

Conceptos Básicos

This paper proposes a multi-stage, modular reasoning framework called MoReVQA that decomposes video question answering into event parsing, grounding, and reasoning stages. This approach outperforms prior single-stage modular methods and a strong baseline, while providing interpretable intermediate outputs.

Resumen

The paper addresses the task of video question answering (videoQA) by proposing a new multi-stage, modular reasoning framework called MoReVQA. The key insights are:

Event Parsing Stage:

Focuses on parsing the input question to identify relevant events, temporal relationships, and question types.
Generates a set of API calls based on the parsed information to populate the external memory.

Grounding Stage:

Grounds the identified events from the previous stage in the video content.
Leverages vision-language models to locate relevant temporal regions and entities in the video.
Verifies and resolves any ambiguities in the grounding.

Reasoning Stage:

Performs grounded reasoning on the video and question context stored in the external memory.
Decomposes the original question into sub-questions to unravel different aspects.
Combines the grounded video context and the outputs of the previous stages to generate the final answer.

The modular and multi-stage design of MoReVQA is shown to outperform prior single-stage modular methods as well as a strong baseline called JCEF, which simply captions every frame and uses an LLM to predict the answer. MoReVQA achieves state-of-the-art results on four standard videoQA benchmarks, while also providing interpretable intermediate outputs at each stage.

Personalizar resumen

Reescribir con IA

Generar citas

Traducir fuente

A otro idioma

Generar mapa mental

del contenido fuente

Ver fuente

arxiv.org

Estadísticas

The paper reports the following key metrics:
"why was the cat lying on its back near the end?"
[frame 42] what is the cat doing?: playing
[frame 48] what surrounds the cat?: a person
[frame 48] why was the cat lying on its back?: to be petted

Citas

"The core issue of the overall system lies in the difficult task given to its single-stage planner: before performing visual reasoning, the model must output a full program without any grounding in the actual video itself."
"Through this decomposition, our MoReVQA model Mmulti-stage = {M1, M2, M3} relies on smaller focused prompts {P1, P2, P3} for each stage; furthermore, intermediate reasoning outputs {z1, z2, z3} are able to handle different aspects of the overall task, and incorporate grounding in the video itself to resolve ambiguities and inform new intermediate reasoning steps in a more effective manner than the ungrounded single-stage setting."

Ideas clave extraídas de

MoReVQA

by Juhong Min,S... a las arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06511.pdf

Consultas más profundas

How can the multi-stage modular design of MoReVQA be extended to other video-language tasks beyond question answering, such as video captioning or video retrieval

The multi-stage modular design of MoReVQA can be extended to other video-language tasks by adapting the stages to suit the specific requirements of each task. For video captioning, the event parsing stage can focus on identifying key elements in the video that need to be described, the grounding stage can determine the most relevant frames for generating captions, and the reasoning stage can combine the visual and language information to create coherent and informative captions.
For video retrieval, the event parsing stage can extract important details about the query or search intent, the grounding stage can identify relevant videos based on the query, and the reasoning stage can rank and retrieve the most suitable videos. By customizing the prompts, APIs, and memory management in each stage, MoReVQA can effectively tackle a variety of video-language tasks beyond question answering.

What are the potential limitations or failure modes of the MoReVQA approach, and how could they be addressed in future work

One potential limitation of MoReVQA could be the complexity and computational cost associated with running multiple stages for each inference. This could lead to slower processing times and higher resource requirements. To address this, future work could focus on optimizing the architecture and algorithms used in each stage to improve efficiency without compromising accuracy.
Another limitation could be the reliance on pre-trained models for the APIs in each stage, which may not always generalize well to new tasks or domains. To mitigate this, ongoing model updates and fine-tuning on task-specific data could enhance the performance of MoReVQA across a wider range of scenarios.
Additionally, the interpretability of the intermediate outputs generated by each stage could be further improved to provide more detailed insights into the decision-making process of the model. This could involve developing visualization techniques or explanation methods to enhance the transparency of the system.

Given the strong performance of the simple JCEF baseline, how can the modular components of MoReVQA be further improved or combined in novel ways to push the boundaries of video question answering capabilities

To further enhance the modular components of MoReVQA and push the boundaries of video question answering capabilities, several strategies can be considered:

Dynamic Module Selection: Implement a mechanism that dynamically selects and combines different modules based on the complexity of the question or the characteristics of the video. This adaptive approach can improve the efficiency and accuracy of the system.

Attention Mechanisms: Integrate attention mechanisms within each stage to focus on relevant information and ignore irrelevant details. This can help improve the model's ability to extract key features from the video and question.

Transfer Learning: Explore transfer learning techniques to fine-tune the pre-trained models used in MoReVQA on specific video question answering datasets. This can help the model adapt to the nuances of different tasks and improve overall performance.

Ensemble Methods: Combine the outputs of multiple instances of MoReVQA with variations in hyperparameters or input data to create an ensemble model. This ensemble approach can enhance robustness and accuracy by leveraging diverse perspectives.

By incorporating these advanced strategies and continuously refining the modular components, MoReVQA can achieve even higher levels of performance and set new benchmarks in video question answering capabilities.