Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels

Core Concepts

Introducing a ranking distillation framework to address the insufficient labeling problem in open-ended video question answering.

Abstract

Introduction

Focus on OE-VQA, a multi-label classification task.
Existing benchmarks have insufficient labels.

Ranking Distillation Framework (RADI)

RADI uses a teacher model to generate rankings for potential answers.
Two robust distillation approaches: pairwise and listwise RADIs.

Experiments

Comparison with SOTA models on popular datasets.
Evaluation of RADI's performance on the insufficient labeling problem.

Ablation Study

Impact of pairwise and listwise ranking distillation strategies.

Qualitative Results

Comparison of top-5 predictions between baseline and RADI models.

Stats

Due to annotation costs, existing benchmarks typically have one answer per question.
Extensive experiments show that both pairwise and listwise RADIs outperform state-of-the-art methods.

Quotes

"RADI employs a teacher model trained with incomplete labels to generate rankings for potential answers."
"In this work, we introduce a simple yet effective ranking distillation framework (RADI) to mitigate this problem without additional manual annotation."

Key Insights Distilled From

Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels

by Tianming Lia... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14430.pdf

Deeper Inquiries

How can the RADI framework be adapted for other tasks beyond OE-VQA

The RADI framework can be adapted for other tasks beyond OE-VQA by modifying the input data and adjusting the loss functions accordingly. For tasks that involve multi-label classification or ranking, similar to OE-VQA, the teacher model can be trained with incomplete labels to generate rankings for potential answers. The student model can then be trained using both the original labels and the ranking labels provided by the teacher model. This approach enriches the label information available during training and helps improve generalization capabilities.

What are the potential drawbacks or limitations of using an imperfect teacher model in the ranking distillation process

Using an imperfect teacher model in the ranking distillation process may introduce biases or noise into the training procedure, leading to suboptimal performance of the student model. Some potential drawbacks or limitations include:

Biased Rankings: The imperfect teacher model may provide inaccurate rankings based on its training data, which could mislead the student model.
Noise in Label Information: Inaccurate rankings from an imperfect teacher could introduce noise into the distillation process, affecting how well the student learns from these rankings.
Overfitting: If not properly handled, relying solely on an imperfect teacher's rankings without appropriate adjustments could lead to overfitting of the student model to this noisy information.

To mitigate these limitations, it is crucial to design robust distillation strategies like adaptive soft margins in pairwise ranking distillation or partial listwise learning in listwise ranking distillation as demonstrated in RADI.

How might the insights gained from this study be applied to improve other machine learning models or systems

The insights gained from this study can be applied to improve other machine learning models or systems by:

Handling Insufficient Labels: Techniques like distribution distillation and uncertainty estimation used in RADI can help address challenges posed by insufficient labeling across various tasks.
Robust Learning Strategies: Implementing adaptive soft margins and partial listwise learning approaches showcased in RADI can enhance models' ability to learn effectively even with noisy data.
Model Generalization: By leveraging a combination of labeled data and ranked lists generated by a pre-trained model (teacher), models across different domains can benefit from improved generalization capabilities.
Efficient Training Paradigms: Parameter-free training frameworks like RADI offer efficient ways of incorporating additional knowledge without increasing computational complexity significantly.

By applying these principles derived from RADI, machine learning models across diverse applications stand to benefit from enhanced performance and robustness when dealing with limited label information or uncertain data distributions within their respective domains.