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Related Questions
- What are the key challenges in labeling negative examples in multimodal datasets?
- How do annotators ensure consistency in labeling negative examples across different modalities?
- What are the potential pitfalls of relying on human annotators for labeling negative examples?
- What are some strategies for reducing annotation bias in negative example labeling?
- How do annotators handle ambiguous or uncertain cases when labeling negative examples?
- What are the best practices for ensuring that negative examples are representative of the underlying distribution?
- Can you explain the importance of active learning in improving the quality of negative example labeling?
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