Retrieving-to-Answer: Zero-Shot Video Question Answering with Frozen Large Language Models
R2A framework surpasses Flamingo-80B in zero-shot video QA with only 1.3B parameters.
Key Findings
Methodology
The R2A framework retrieves semantically similar texts from a generic text corpus using a pre-trained multi-modal model (e.g., CLIP) and combines them with the question to generate answers using a large language model (e.g., DeBERTa). This approach requires no cross-modal fine-tuning, allowing all key components to be plug-and-play.
Key Results
- R2A achieved 18.3% accuracy on the MSRVTT-QA dataset, surpassing Flamingo-80B's 17.4%, without additional training.
- On the MSVD-QA dataset, R2A achieved 37.0% accuracy, significantly outperforming Flamingo-80B's 35.6%.
- R2A reached 52.2% accuracy on the TGIF-QA dataset, leading Flamingo-80B's 41.9%.
Significance
The R2A framework offers a training-free solution in the video QA domain, significantly reducing computational costs and data requirements. Its modular design allows flexible component selection and replacement, adapting to various tasks and domains.
Technical Contribution
R2A avoids expensive cross-modal training by retrieving rather than generating text descriptions, providing an efficient zero-shot video QA method, demonstrating strong capabilities in multi-modal retrieval.
Novelty
R2A is the first method to exploit cross-modal retrieval from open-world knowledge for video QA tasks, significantly enhancing zero-shot learning efficiency and accuracy.
Limitations
- R2A relies on the quality and diversity of the text corpus, which may perform poorly in specific domains.
- The retrieval process might not capture all detailed information of the video.
Future Work
Future work could explore richer text corpora and more efficient retrieval algorithms to further enhance R2A's performance and applicability.
AI Executive Summary
Video Question Answering (VideoQA) aims to answer questions regarding a reference video. Existing methods typically require costly cross-modal data training or rely on description models with limited domain generalization. To address these issues, researchers have proposed a simple yet effective Retrieving-to-Answer (R2A) framework.
R2A retrieves semantically similar texts from a generic text corpus using a pre-trained multi-modal model (e.g., CLIP) and combines them with the question to generate answers using a large language model (e.g., DeBERTa). This approach requires no cross-modal fine-tuning, allowing all key components to be plug-and-play.
Extensive experiments on several VideoQA benchmarks show that despite having only 1.3B parameters and no fine-tuning, R2A can outperform the 61 times larger Flamingo-80B model, which is additionally trained on nearly 2.1B multi-modal data.
Deep Analysis
Background
VideoQA is a multi-modal task involving natural language understanding, question answering, and video processing. Traditional methods rely on supervised learning with large labeled datasets, limiting model generalization. Recently, zero-shot learning has shown potential in scaling to unseen samples, attracting widespread attention.
Core Problem
Existing VideoQA methods face high training costs and limited domain generalization. Effectively bridging the gap between text and video to fully exploit the capabilities of large language models is a pressing issue.
Innovation
The R2A framework retrieves rather than generates text descriptions, avoiding expensive cross-modal training. It uses pre-trained multi-modal models for efficient text retrieval and combines them with large language models to generate answers, significantly enhancing zero-shot learning efficiency and accuracy.
Methodology
- �� Use CLIP model to retrieve semantically similar texts from a generic text corpus.
- �� Combine retrieved texts with the question and use DeBERTa model to generate answers.
- �� No cross-modal fine-tuning required, allowing all components to be plug-and-play.
Experiments
Experiments were conducted on several VideoQA benchmarks, including MSRVTT-QA, MSVD-QA, and TGIF-QA. CLIP was used for video-to-text retrieval, and DeBERTa was used for answer generation. Results showed R2A outperformed Flamingo-80B on multiple datasets.
Results
R2A achieved 18.3% accuracy on the MSRVTT-QA dataset, surpassing Flamingo-80B's 17.4%. On the MSVD-QA dataset, R2A achieved 37.0% accuracy, significantly outperforming Flamingo-80B's 35.6%.
Applications
R2A can be applied in video search, summarization, and understanding, significantly reducing computational costs and data requirements, suitable for various domains.
Limitations & Outlook
R2A relies on the quality and diversity of the text corpus, which may perform poorly in specific domains. The retrieval process might not capture all detailed information of the video. Future work could explore richer text corpora and more efficient retrieval algorithms.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a book, and there are many books on the shelves, but you don't know which one has the information you need. R2A is like a smart librarian who can quickly find books related to the video content and tell you the answer. This process doesn't require complex training; it just uses existing books and questions to get the answer.
ELI14 Explained like you're 14
Imagine you're playing a game, and you need to answer questions about the game video. R2A is like a super helper that can quickly find text information related to the game and tell you the answer. You don't need to train the helper; just tell it the question, and it can give you the correct answer! Isn't that cool?
Glossary
CLIP (Contrastive Language-Image Pretraining)
A powerful multi-modal model for contrastive learning of images and text.
Used for retrieving semantically similar texts from a text corpus.
DeBERTa (Decoding-enhanced BERT)
An improved BERT model with stronger language understanding capabilities.
Used for generating answers in video QA.
VideoQA (Video Question Answering)
A task combining video and natural language processing to answer questions about videos.
Application scenario for the R2A framework.
Zero-Shot Learning
A learning method that makes predictions without training samples.
Core advantage of the R2A framework.
Cross-Modal Retrieval
The process of retrieving relevant information from different modalities.
Key step in the R2A framework.
Open Questions Unanswered questions from this research
- 1 How to improve the quality and diversity of the text corpus to enhance R2A's performance?
- 2 In specific domains, how to optimize the retrieval process to capture more detailed information?
Applications
Immediate Applications
Video Search
Utilize the R2A framework to quickly retrieve text information related to video content, improving search efficiency.
Long-term Vision
Intelligent Video Understanding
Continuously optimize the R2A framework to achieve smarter video content analysis and understanding.
Abstract
Video Question Answering (VideoQA) has been significantly advanced from the scaling of recent Large Language Models (LLMs). The key idea is to convert the visual information into the language feature space so that the capacity of LLMs can be fully exploited. Existing VideoQA methods typically take two paradigms: (1) learning cross-modal alignment, and (2) using an off-the-shelf captioning model to describe the visual data. However, the first design needs costly training on many extra multi-modal data, whilst the second is further limited by limited domain generalization. To address these limitations, a simple yet effective Retrieving-to-Answer (R2A) framework is proposed.Given an input video, R2A first retrieves a set of semantically similar texts from a generic text corpus using a pre-trained multi-modal model (e.g., CLIP). With both the question and the retrieved texts, a LLM (e.g., DeBERTa) can be directly used to yield a desired answer. Without the need for cross-modal fine-tuning, R2A allows for all the key components (e.g., LLM, retrieval model, and text corpus) to plug-and-play. Extensive experiments on several VideoQA benchmarks show that despite with 1.3B parameters and no fine-tuning, our R2A can outperform the 61 times larger Flamingo-80B model even additionally trained on nearly 2.1B multi-modal data.