Unbiased Scene Graph Generation in Videos

• URL: http://arxiv.org/abs/2304.00733v3
• Date: Thu, 29 Jun 2023 23:52:24 GMT
• Title: Unbiased Scene Graph Generation in Videos
• Authors: Sayak Nag, Kyle Min, Subarna Tripathi, Amit K. Roy Chowdhury
• Abstract summary: We introduce TEMPURA: TEmporal consistency and Memory-guided UnceRtainty Attenuation for unbiased dynamic SGG. TEMPURA employs object-level temporal consistencies via transformer sequence modeling, learns to synthesize unbiased relationship representations. Our method achieves significant (up to 10% in some cases) performance gain over existing methods.
• Score: 36.889659781604564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The task of dynamic scene graph generation (SGG) from videos is complicated and challenging due to the inherent dynamics of a scene, temporal fluctuation of model predictions, and the long-tailed distribution of the visual relationships in addition to the already existing challenges in image-based SGG. Existing methods for dynamic SGG have primarily focused on capturing spatio-temporal context using complex architectures without addressing the challenges mentioned above, especially the long-tailed distribution of relationships. This often leads to the generation of biased scene graphs. To address these challenges, we introduce a new framework called TEMPURA: TEmporal consistency and Memory Prototype guided UnceRtainty Attenuation for unbiased dynamic SGG. TEMPURA employs object-level temporal consistencies via transformer-based sequence modeling, learns to synthesize unbiased relationship representations using memory-guided training, and attenuates the predictive uncertainty of visual relations using a Gaussian Mixture Model (GMM). Extensive experiments demonstrate that our method achieves significant (up to 10% in some cases) performance gain over existing methods highlighting its superiority in generating more unbiased scene graphs.

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