Unbiased Scene Graph Generation
15 papers with code • 1 benchmarks • 1 datasets
Unbiased Scene Graph Generation (Unbiased SGG) aims to predict more informative scene graphs composed of more "tail predicates" *(in contrast to "head predicates" in terms of class frequencies) by dealing with the skewed, long-tailed predicate class distribution. (Definition from Chiou et al. "Recovering the Unbiased Scene Graphs from the Biased Ones")
Benchmarks
These leaderboards are used to track progress in Unbiased Scene Graph Generation
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Visual Genome
Most implemented papers
Unbiased Scene Graph Generation from Biased Training
KaihuaTang/Scene-Graph-Benchmark.pytorch
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CVPR 2020
Today's scene graph generation (SGG) task is still far from practical, mainly due to the severe training bias, e. g., collapsing diverse "human walk on / sit on / lay on beach" into "human on beach".
Bipartite Graph Network with Adaptive Message Passing for Unbiased Scene Graph Generation
Scarecrow0/BGNN-SGG
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CVPR 2021
Scene graph generation is an important visual understanding task with a broad range of vision applications.
Fine-Grained Scene Graph Generation with Data Transfer
waxnkw/ietrans-sgg.pytorch
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Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images.
PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation
coldmanck/recovering-unbiased-scene-graphs
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Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution.
CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation
CYVincent/Scene-Graph-Transformer-CogTree
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We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model.
Recovering the Unbiased Scene Graphs from the Biased Ones
coldmanck/recovering-unbiased-scene-graphs
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Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
Resistance Training using Prior Bias: toward Unbiased Scene Graph Generation
chch1999/rtpb
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To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation.
Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation
dongxingning/sha-gcl-for-sgg
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CVPR 2022
Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph.
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.
Skew Class-balanced Re-weighting for Unbiased Scene Graph Generation
An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-balanced Re-weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution.
