5 citations · 10 across the 4 of their papers we have counts for
4 papers
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation
Xinyu Lyu, Jingwei Liu, Yuyu Guo +1
The task of dynamic scene graph generation (DynSGG) aims to generate scene graphs for given videos, which involves modeling the spatial-temporal information in the video. However,…
Prototype-based Embedding Network for Scene Graph Generation
Chaofan Zheng, Xinyu Lyu, Lianli Gao +2
Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs. However, due to the diverse visual appearance of numerous p…
Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
Chaofan Zheng, Lianli Gao, Xinyu Lyu +3
The current studies of Scene Graph Generation (SGG) focus on solving the long-tailed problem for generating unbiased scene graphs. However, most de-biasing methods overemphasize th…
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation
Xinyu Lyu, Lianli Gao, Pengpeng Zeng +2
The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., woman-on/standing on/walking on-beach. As general SGG m…