Stop Labeling Duplicates: Semantic Deduplication for Multimodal Datasets

Semantic deduplication is a strategy for reducing labeling costs in video datasets by eliminating redundant data without losing valuable information. Traditional hash-based methods fail to identify duplicates in datasets such as dashcam or security footage due to slight pixel differences, necessitating the use of embeddings from models like CLIP or ResNet to map images to vectors and identify semantic similarities. This process involves generating embeddings for the entire dataset, using clustering approaches to find similar images, and then pruning the dataset by retaining only the most representative images from each cluster. Additionally, visual querying can be employed to find specific rare cases within the dataset, enhancing the data's quality and utility. By focusing on data quality rather than volume, this method enables the creation of smaller, more efficient datasets that are less costly to label while still facilitating effective model training.

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