Visually Grounded Video Question Answering: Assessing the Reliability of Current Models

核心概念

Current vision-language models excel at video question answering but struggle to ground their predictions in relevant video content, often relying on language shortcuts and irrelevant visual context.

摘要

The paper studies the extent to which current vision-language models (VLMs) for video question answering (VideoQA) are genuinely grounded in the relevant video content, versus relying on language shortcuts or spurious correlations.
The authors construct the NExT-GQA dataset, which extends the existing NExT-QA dataset with temporal grounding annotations for the validation and test sets. They analyze a series of state-of-the-art VLMs and find that despite their strong QA performance, these models are extremely weak in substantiating their answers with the relevant video evidence.
To address this limitation, the authors propose a grounded-QA method that learns differentiable Gaussian masks along the temporal dimension of the videos, optimizing both the QA and video-question grounding objectives. Experiments with different model backbones demonstrate that this grounding mechanism improves both grounding and QA performance, especially on questions that require video understanding and temporal reasoning.
The paper highlights the need for continued research efforts to develop more trustworthy VLMs that can reliably ground their predictions in the relevant visual content.

統計資料

49.3% of the overall predictions of the BlindQA model (a language-only model) are shared with the state-of-the-art VQA model.
60.6% of the overall predictions of the SigFQA model (an image-text model) are shared with the state-of-the-art VQA model.
63.2% of the overall predictions of the state-of-the-art VQA model are shared with the BlindQA model.

引述

"Despite significant advancements in QA performance, a fundamental concern arises – whether or to what extent are the answers of such techniques grounded on the relevant visual content? Alternatively, are they relying on the language short-cut for the use of powerful language models or spurious vision-language correlation captured via cross-modal pretraining?"
"Our findings reveal that all these models struggle to predict visually grounded answers, despite their strong QA performance. For example, the SoTA model [62] achieves QA accuracy of 69%, but only 16% of the correctly predicted answers are grounded in the video. In contrast, humans can ground 82% out of the 93% of the correctly answered questions."

從以下內容提煉的關鍵洞見

Can I Trust Your Answer? Visually Grounded Video Question Answering

by Junbin Xiao,... 於 arxiv.org 04-02-2024

https://arxiv.org/pdf/2309.01327.pdf

深入探究

How can we better align the language and visual understanding capabilities of VLMs to ensure their predictions are genuinely grounded in the relevant video content?

To better align the language and visual understanding capabilities of Vision-Language Models (VLMs) for genuinely grounded predictions, several strategies can be implemented:

Multi-Modal Pretraining: Pretraining VLMs on multi-modal data that includes both images and text can help the model learn to associate visual and textual information effectively. This can improve the model's ability to generate answers that are grounded in the visual content of the videos.

Cross-Modal Learning: Implementing cross-modal learning techniques can enhance the model's understanding of the relationships between visual and textual inputs. By training the model to align visual and textual representations, it can make more accurate and grounded predictions.

Fine-Grained Temporal Grounding: Incorporating fine-grained temporal grounding mechanisms can help VLMs identify specific moments in the video that correspond to the questions being asked. This can ensure that the model's predictions are based on relevant video content.

Attention Mechanisms: Enhancing the attention mechanisms within the model to focus on key visual elements in the video can improve the model's grounding capabilities. By attending to relevant visual features, the model can provide more accurate and contextually relevant answers.

Weakly-Supervised Learning: Implementing weakly-supervised learning techniques that encourage the model to learn from the video content itself, rather than relying solely on language priors, can help improve the grounding of predictions.

By incorporating these strategies and techniques, VLMs can be better aligned to ensure that their predictions are genuinely grounded in the relevant video content, leading to more reliable and trustworthy results.

What are the potential drawbacks or limitations of the proposed Gaussian masking approach, and how can it be further improved or extended?

The Gaussian masking approach, while effective in improving grounding and QA performance, may have some drawbacks and limitations:

Complexity: The Gaussian masking approach adds complexity to the model architecture and training process, requiring additional computational resources and time.

Hyperparameter Sensitivity: The performance of the Gaussian masking approach can be sensitive to hyperparameters such as the width of the confidence interval. Tuning these hyperparameters effectively can be challenging.

Generalization: The Gaussian masking approach may struggle to generalize to unseen data or different types of videos, potentially leading to overfitting on the training data.

To improve and extend the Gaussian masking approach, the following steps can be taken:

Regularization Techniques: Implement regularization techniques to prevent overfitting and improve the generalization capabilities of the model.

Hyperparameter Optimization: Conduct thorough hyperparameter optimization to find the optimal settings for the Gaussian masking approach, ensuring robust performance across different scenarios.

Data Augmentation: Augmenting the training data with diverse video content and question-answer pairs can help the model learn more robust grounding patterns and improve its performance on unseen data.

Ensemble Methods: Combining multiple instances of the model trained with different hyperparameters or initializations can help improve the overall performance and robustness of the Gaussian masking approach.

By addressing these limitations and incorporating these improvements, the Gaussian masking approach can be further enhanced to provide more reliable and accurate grounding in VideoQA systems.

What other techniques or architectural designs could help bridge the gap between the VLMs' QA performance and their ability to provide reliable visual explanations for their predictions?

Several techniques and architectural designs can help bridge the gap between VLMs' QA performance and their ability to provide reliable visual explanations for their predictions:

Graph Neural Networks (GNNs): Integrating GNNs into the VLM architecture can help capture complex relationships between visual elements in the video, enabling the model to generate more accurate visual explanations for its predictions.

Temporal Convolutional Networks (TCNs): TCNs can be used to model temporal dependencies in videos, allowing the model to better understand the sequential nature of video content and provide more contextually relevant visual explanations.

Spatial Attention Mechanisms: Enhancing spatial attention mechanisms within the model can help focus on specific regions of interest in the video, improving the model's ability to ground its predictions in relevant visual content.

Memory-Augmented Networks: Implementing memory-augmented networks can enable the model to store and retrieve relevant visual information over time, enhancing its ability to provide consistent and reliable visual explanations for its predictions.

Adversarial Training: Incorporating adversarial training techniques can help the model learn to generate more robust and accurate visual explanations by exposing it to challenging and diverse visual stimuli during training.

By incorporating these techniques and architectural designs, VLMs can bridge the gap between their QA performance and their ability to provide reliable visual explanations, leading to more trustworthy and interpretable predictions in VideoQA systems.