Mission Balance: Generating Under-represented Class Samples using Video Diffusion Models

TL;DR

This paper presents a novel two-stage, text-conditioned video diffusion method to generate high-quality surgical videos for under-represented classes, improving data balance and model performance in surgical video analysis.

Contribution

The work introduces a unique diffusion-based approach that decouples spatial and temporal modeling and uses rejection sampling to synthesize realistic surgical videos for data augmentation.

Findings

Synthetic videos improve action recognition accuracy.

Enhanced intra-operative event prediction performance.

Effective mitigation of data imbalance in surgical datasets.

Abstract

Computer-assisted interventions can improve intra-operative guidance, particularly through deep learning methods that harness the spatiotemporal information in surgical videos. However, the severe data imbalance often found in surgical video datasets hinders the development of high-performing models. In this work, we aim to overcome the data imbalance by synthesizing surgical videos. We propose a unique two-stage, text-conditioned diffusion-based method to generate high-fidelity surgical videos for under-represented classes. Our approach conditions the generation process on text prompts and decouples spatial and temporal modeling by utilizing a 2D latent diffusion model to capture spatial content and then integrating temporal attention layers to ensure temporal consistency. Furthermore, we introduce a rejection sampling strategy to select the most suitable synthetic samples, effectively augmenting existing datasets to address class imbalance. We evaluate our method on two downstream tasks-surgical action recognition and intra-operative event prediction-demonstrating that incorporating synthetic videos from our approach substantially enhances model performance. We open-source our implementation at https://gitlab.com/nct_tso_public/surgvgen.