Performance Comparison of Pre-trained Video Encoders with CausalMixFT and Non-causal Diffusion Synthetic Data for Out-of-domain

Abstract Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data

Research goal: How does the k-nearest neighbors classification performance of pre-trained video encoders compare when fine-tuning with CausalMixFT-generated synthetic data versus non-causal diffusion-generated samples on out-of-domain sparse video gesture recognition benchmarks?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.5/10.