.png){kind=icon}
返回
Relation-Specific Feature Augmentation for unbiased scene graph generation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2024.110936.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Scene Graph Generation (SGG) models suffer from the long-tailed distribution of relations, which results in biased predictions that favor head relations (e.g. , on) over informative tail ones (e.g. , sitting on, laying on, standing on). Existing solutions typically adopt class re-balancing strategies to balance data distribution. However, they do not essentially solve the lack of information due to insufficient tail data. To this end, we propose a Relation-Specific Feature Augmentation (RSFA) framework to mitigate the longtailed bias by augmenting relations in the feature space. To perform augmentation effectively, we design an augmentation scheme and a novel Dual Attention Network (DAN). The augmentation scheme augments each relation uniformly based on the reciprocal number of samples to avoid over-fitting. By extracting relation- specific information from new object features generated by a Conditional Variational AutoEncoder (CVAE), DAN generates reliable virtual relation representations to provide useful information to guide optimizing relation classifier. Extensive ablation studies and comprehensive analysis demonstrate the effectiveness of our method in debiasing. And results on the Visual Genome benchmark show that our method significantly outperforms the existing state-of-the-art methods.
Keyword:
Image understanding
Scene graph generation
Long-tailed distribution
Feature augmentation
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏A unified deep sparse graph attention network for scene graph generationPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏Local context attention learning for fine-grained scene graph generation用于细粒度场景图生成的局部上下文注意学习PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容