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Found 9 papers, 3 papers with code
Context-based Transfer and Efficient Iterative Learning for Unbiased Scene Graph Generation
Thus, we introduce a plug-and-play method named CITrans, which iteratively trains SGG models with progressively enhanced data.
Informative Scene Graph Generation via Debiasing
It integrates two components: Semantic Debiasing (SD) and Balanced Predicate Learning (BPL), for these imbalances.
Local-Global Information Interaction Debiasing for Dynamic Scene Graph Generation
Long-temporal human actions supervise the model to generate multiple scene graphs that conform to the global constraints and avoid the model being unable to learn the tail predicates.
Generalized Unbiased Scene Graph Generation
To the end, we propose the Multi-Concept Learning (MCL) framework, which ensures a balanced learning process across rare/ uncommon/ common concepts.
Prototype-based Embedding Network for Scene Graph Generation
Current Scene Graph Generation (SGG) methods explore contextual information to predict relationships among entity pairs.
Dual-branch Hybrid Learning Network for Unbiased Scene Graph Generation
Experiments show that our approach achieves a new state-of-the-art performance on VG and GQA datasets and makes a trade-off between the performance of tail predicates and head ones.
Adaptive Fine-Grained Predicates Learning for Scene Graph Generation
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.
Learning To Generate Scene Graph from Head to Tail
SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations.
Fine-Grained Predicates Learning for Scene Graph Generation
The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e. g., "woman-on/standing on/walking on-beach" or "woman-near/looking at/in front of-child".
