แนวคิดหลัก
Existing 3D vision-language models struggle with natural language variations, highlighting a need for improved robustness.
บทคัดย่อ
The content explores the limitations of 3D vision-language models in understanding natural language. It introduces a new task and dataset to evaluate language robustness, identifies fragility in existing models, proposes a pre-alignment module for performance enhancement, and discusses the impact of data augmentation on model robustness.
Directory:
- Introduction
- Progress in connecting vision and language tasks.
- Data Extraction Challenges
- Fragility of 3D-VL models in understanding natural language variations.
- Proposed Language Robustness Task
- Designing a task to evaluate generalization capabilities across diverse language variants.
- 3D Language Robustness Dataset
- Construction pipeline and quality assessment.
- Experiments and Results
- Evaluation of various models on 3D-VG and 3D-VQA tasks.
- Analysis and Improved Model
- Identification of fusion module fragility and proposal of a pre-alignment module.
- Discussion on Data Augmentation
- Comparison of data augmentation with proposed method.
- Conclusion
สถิติ
The chair is black with wheels.
The chair with wheels is black.
You see the desk? To the right of it, there's a black chair with wheels.
The chair's got wheels and it's on the right side of the desk, mate.
คำพูด
"The model fails on common human language variations."
"Existing datasets lack diversity hindering model training."
"Our proposed pre-alignment module enhances model performance."