New AI Dataset Pushes Boundaries While Tackling Challenges in Ethics and Precision

Table of Links

• Abstract and 1 Introduction
• 2. Related Work
• 3. Method
• 4. Data Annotation Pipeline
• 5. Experiments
• 6. Conclusion and References

Supplementary Material (Part 1)

• A. Additional Implementation Details
• B. Additional Downstream Tasks
• C. Additional Qualitative Results

Supplementary Material (Part 2)

• D. Dataset Visualization
• E. Limitations and Future Work
• F. Ethics and Societal Impact

D. Dataset Visualization

In this section, we provide additional dataset samples of our GranD and GranDf datasets to better understand the functionalities they offer. Please see Fig. 15 and Fig. 14.

E. Limitations and Future Work

The large-scale automated pipeline provides dense labelings that are important for our pretraining but still contains some noise. A high-quality, clean dataset could help further improve the pretrained representations, although this comes at a significantly higher annotation cost. A potential research direction is to develop a cost-effective annotation pipeline aimed at reducing noise in dense labeling. Additionally, expanding the GLaMM framework to include modalities such as video and 3D is also a future research direction.

F. Ethics and Societal Impact

Our Grounding-anything Dataset (GranD) utilizes SAM images that have de-identified personal information, with all faces and license plates obscured. To the best of our knowledge, the dataset does not portray any strong biases or discrimination. We urge for the responsible use of GranD and GLaMM, promoting research progress while safeguarding privacy.