SCENEVERSE: Scaling 3D Vision-Language Learning for Grounded Scene Understanding

المفاهيم الأساسية

The authors introduce SCENEVERSE, a million-scale 3D vision-language dataset, to address challenges in grounding language in 3D scenes and propose GPS, a pre-training framework that achieves state-of-the-art performance on existing benchmarks.

الملخص

SCENEVERSE is a groundbreaking dataset that aims to enhance 3D vision-language learning by providing extensive scene-language pairs. The GPS pre-training framework demonstrates remarkable results in achieving state-of-the-art performance on various 3D visual grounding benchmarks.
Key points:

SCENEVERSE introduces a million-scale 3D vision-language dataset.
Challenges in grounding language in 3D scenes are addressed through the GPS pre-training framework.
GPS achieves state-of-the-art performance on existing 3D visual grounding benchmarks.

الإحصائيات

SCENEVERSE comprises about 68K 3D indoor scenes and 2.5M vision-language pairs.
Human annotations include 190,836 pairs with a total of 2.5M scene-language pairs.
The quality check achieved a pass rate of 96.93% for generated object-level descriptions.

اقتباسات

"In comparison to recent advancements in the 2D domain, grounding language in 3D scenes faces several significant challenges."
"We demonstrate that this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (GPS), for 3D vision-language learning."

الرؤى الأساسية المستخلصة من

SceneVerse

by Baoxiong Jia... في arxiv.org 03-07-2024

https://arxiv.org/pdf/2401.09340.pdf

استفسارات أعمق

How can the principles of data scaling from the success of the model be applied to other domains?

The success of data scaling in improving model performance can be applied to various other domains within artificial intelligence research. By increasing the scale of datasets, models can learn more diverse patterns and generalize better to unseen data. This principle can be particularly beneficial in natural language processing tasks, computer vision applications, reinforcement learning environments, and healthcare analytics. For example, in NLP tasks like machine translation or sentiment analysis, larger datasets could help capture a wider range of language nuances and improve translation accuracy. Similarly, in computer vision tasks such as object detection or image classification, scaling up datasets could lead to better recognition performance across different scenarios.

What potential ethical considerations should be taken into account when using large-scale datasets like SCENEVERSE?

When utilizing large-scale datasets like SCENEVERSE for AI research, several ethical considerations need to be addressed:

Data Privacy: Ensuring that personal information is anonymized and protected within the dataset.
Bias Mitigation: Being aware of biases present in the dataset and taking steps to mitigate them during model training.
Informed Consent: Ensuring that individuals whose data is included have given informed consent for its use.
Transparency: Providing transparency about how the data was collected and used.
Security: Implementing robust security measures to protect sensitive information from unauthorized access.

How might the findings from this study impact future developments in artificial intelligence research?

The findings from this study have several implications for future developments in AI research:

Advancements in 3D Vision-Language Understanding: The development of SCENEVERSE and GPS sets a new benchmark for 3D vision-language understanding tasks.
Scalability: The scalability demonstrated by this study shows the importance of large-scale datasets for enhancing model performance across various domains.
Generalization: The zero-shot transfer experiments highlight how pre-training on extensive data can lead to improved generalization capabilities in AI models.
Future Research Directions: These findings pave the way for further exploration into multi-level alignment between scenes and texts as well as advancements in grounded scene understanding techniques.

By leveraging these insights, researchers can build upon this work to create more robust AI systems capable of handling complex real-world scenarios with greater accuracy and efficiency.