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The paper investigates the use of 3D fractals generated via Iterated Function Systems (IFS) as a synthetic pre-training dataset for action recognition models. It identifies limitations in standard fractal generation methods, including slow speed and degenerate fractal structures, and finds that overly restrictive filtering hurts downstream performance. The authors introduce Targeted Smart Filtering, a novel method that significantly accelerates fractal generation (100x speedup) while maintaining fractal diversity, leading to improved action recognition performance after pre-training.
Forget slow, degenerate fractals: Targeted Smart Filtering unlocks 100x faster generation of diverse 3D fractals, boosting action recognition pre-training.
Synthetic datasets are being recognized in the deep learning realm as a valuable alternative to exhaustively labeled real data. One such synthetic data generation method is Formula Driven Supervised Learning (FDSL), which can provide an infinite number of perfectly labeled data through a formula driven approach, such as fractals or contours. FDSL does not have common drawbacks like manual labor, privacy and other ethical concerns. In this work we generate 3D fractals using 3D Iterated Function Systems (IFS) for pre-training an action recognition model. The fractals are temporally transformed to form a video that is used as a pre-training dataset for downstream task of action recognition. We find that standard methods of generating fractals are slow and produce degenerate 3D fractals. Therefore, we systematically explore alternative ways of generating fractals and finds that overly-restrictive approaches, while generating aesthetically pleasing fractals, are detrimental for downstream task performance. We propose a novel method, Targeted Smart Filtering, to address both the generation speed and fractal diversity issue. The method reports roughly 100 times faster sampling speed and achieves superior downstream performance against other 3D fractal filtering methods.
