3D Vision-Language Models' Fragility in Understanding Natural Language

핵심 개념

Existing 3D-VL models struggle with natural language variations, highlighting the need for improved language robustness.

초록

• The article discusses the limitations of current 3D Vision-Language (3D-VL) models in understanding natural language variations.
• It introduces a new task and dataset to evaluate language robustness in 3D-VL models systematically.
• The study identifies a significant drop in performance across various tasks due to the fragility of existing models when faced with diverse language styles.
• A pre-alignment module is proposed as a solution to enhance model performance without retraining.
• Data augmentation is discussed as a potential method to improve model robustness but requires large and diverse datasets.

통계

"The chair is black with wheels. It is to the right of the desk." - Original sentence used for evaluation.
"Even the state-of-the-art 3D-LLM fails to understand some variants of the same sentences." - Mention of model failure.

인용구

"The fusion module is biased towards the training dataset, rather than genuinely understanding the semantics of natural language."
"Existing 3D-VL models exhibit sensitivity to styles of language input, struggling with sentences written in different variants."

핵심 통찰 요약

Can 3D Vision-Language Models Truly Understand Natural Language?

by Weipeng Deng... 게시일 arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.14760.pdf

더 깊은 질문

データ拡張は既存の3D-VLモデルの頑健性不足にどのように対処できますか？

データ拡張は、既存の3D-VLモデルが特定の言語スタイルに過剰適合している問題を解決するために有効なアプローチです。通常、モデルをさまざまな言語バリエーションやスタイルでトレーニングすることで、そのモデルがより柔軟かつ一般的な情報を学習しやすくなります。例えば、異なる文法構造や表現方法を持つ大規模かつ多様なトレーニングセットを使用することで、モデルは新しいパターンやスタイルに対応する能力を向上させることが期待されます。