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.
Abstract (translated)
从视频动态场景Graph(SGG)的生成任务变得复杂且具有挑战性，因为场景本身具有动态特性、模型预测时间的随机波动以及视觉关系长长尾分布的特性，而基于图像的SGG已经面临了上述挑战。现有的动态SGG方法主要关注使用复杂的架构捕捉时空上下文，而没有解决上述挑战，特别是关系长长尾分布的问题。这可能导致生成偏差的场景Graph。为了应对这些挑战，我们提出了名为TEMPURA的新框架，它利用集体一致性和记忆原型引导的无偏差动态SGG性能衰减。 Tempura使用对象级别的时间一致性通过Transformer序列建模实现，学习使用记忆引导训练合成无偏差的关系表示，并通过高斯混合模型(GMM)衰减视觉关系预测的不确定性。广泛的实验表明，我们的方法比现有方法实现了显著的性能提升(在某些情况下高达10%)，突出了它在生成更多无偏差场景Graph方面的优越性。
URL
https://arxiv.org/abs/2304.00733
