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Found 6 papers, 3 papers with code
Triple Correlations-Guided Label Supplementation for Unbiased Video Scene Graph Generation
Video-based scene graph generation (VidSGG) is an approach that aims to represent video content in a dynamic graph by identifying visual entities and their relationships.
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection
Without bells and whistles, our RePro achieves a new state-of-the-art performance on two VidVRD benchmarks of not only the base training object and predicate categories, but also the unseen ones.
Rethinking Multi-Modal Alignment in Video Question Answering from Feature and Sample Perspectives
From the view of feature, we break down the video into trajectories and first leverage trajectory feature in VideoQA to enhance the alignment between two modalities.
Classification-Then-Grounding: Reformulating Video Scene Graphs as Temporal Bipartite Graphs
To this end, we propose a new classification-then-grounding framework for VidSGG, which can avoid all the three overlooked drawbacks.
Video Relation Detection via Tracklet based Visual Transformer
Video Visual Relation Detection (VidVRD), has received significant attention of our community over recent years.
Julia Language in Machine Learning: Algorithms, Applications, and Open Issues
It first surveys the popular machine learning algorithms that are developed in the Julia language.
