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The paper introduces FlexDataset, a composition-to-data (C2D) framework that generates high-fidelity, pixel-level annotated synthetic datasets for tasks like salient object detection, depth estimation, and segmentation. It addresses limitations of existing text-to-data methods in generating complex scenes by offering precise positional and categorical control through a composition-to-image (C2I) framework. The proposed Versatile Annotation Generation (VAG) Plan A leverages tuned perception decoders to exploit rich latent representations, achieving a nearly fivefold reduction in annotation time and enabling unlimited generation of customized, multi-instance and multi-category (MIMC) annotated data.
Forget tedious manual annotation: FlexDataset crafts customized, high-fidelity annotated datasets with 5x faster annotation times using a composition-to-data approach.
High-quality, pixel-level annotated datasets are crucial for training deep learning models, while their creation is often labor-intensive, time-consuming, and costly. Generative diffusion models have then gained prominence for producing synthetic datasets, yet existing text-to-data methods struggle with generating complex scenes involving multiple objects and intricate spatial arrangements. To address these limitations, we introduce FlexDataset, a framework that pioneers the composition-to-data (C2D) paradigm. FlexDataset generates high-fidelity synthetic datasets with versatile annotations, tailored for tasks like salient object detection, depth estimation, and segmentation. Leveraging a meticulously designed composition-to-image (C2I) framework, it offers precise positional and categorical control. Our Versatile Annotation Generation (VAG) Plan A further enhances efficiency by exploiting rich latent representations through tuned perception decoders, reducing annotation time by nearly fivefold. FlexDataset allows unlimited generation of customized, multi-instance and multi-category (MIMC) annotated data. Extensive experiments show that FlexDataset sets a new standard in synthetic dataset generation across multiple datasets and tasks, including zero-shot and long-tail scenarios.
CMU ML