Spatial-Temporal Transformer for Dynamic Scene Graph Generation
Yuren Cong, Wentong Liao, Hanno Ackermann, Bodo Rosenhahn, Michael, Ying Yang

TL;DR
This paper introduces STTran, a spatial-temporal Transformer model for dynamic scene graph generation from videos, capturing spatial relationships within frames and temporal dependencies across frames, validated on the Action Genome dataset.
Contribution
The paper proposes a novel flexible neural network architecture combining spatial and temporal modules for dynamic scene graph generation from videos.
Findings
Superior performance on Action Genome dataset
Effective modeling of spatial and temporal relationships
Ablative studies validate module contributions
Abstract
Dynamic scene graph generation aims at generating a scene graph of the given video. Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation. In this paper, we propose Spatial-temporal Transformer (STTran), a neural network that consists of two core modules: (1) a spatial encoder that takes an input frame to extract spatial context and reason about the visual relationships within a frame, and (2) a temporal decoder which takes the output of the spatial encoder as input in order to capture the temporal dependencies between frames and infer the dynamic relationships. Furthermore, STTran is flexible to take varying lengths of videos as input without clipping, which is especially important for long videos. Our method is…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Human Pose and Action Recognition · Video Analysis and Summarization
MethodsAttention Is All You Need · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Residual Connection · Layer Normalization · Dense Connections · Multi-Head Attention · Transformer
