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Found 5 papers, 0 papers with code
Scene Graph Generation Strategy with Co-occurrence Knowledge and Learnable Term Frequency
Scene graph generation (SGG) is an important task in image understanding because it represents the relationships between objects in an image as a graph structure, making it possible to understand the semantic relationships between objects intuitively.
Semantic Scene Graph Generation Based on an Edge Dual Scene Graph and Message Passing Neural Network
Along with generative AI, interest in scene graph generation (SGG), which comprehensively captures the relationships and interactions between objects in an image and creates a structured graph-based representation, has significantly increased in recent years.
Cross-Modal Learning with 3D Deformable Attention for Action Recognition
The 3D deformable transformer consists of three attention modules: 3D deformability, local joint stride, and temporal stride attention.
Ranked #8 on
Action Recognition
on NTU RGB+D
STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition
In action recognition, although the combination of spatio-temporal videos and skeleton features can improve the recognition performance, a separate model and balancing feature representation for cross-modal data are required.
Ranked #1 on
Action Recognition
on Penn Action
Interpretation and Simplification of Deep Forest
This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination.
