A Causal Adjustment Module for Debiasing Scene Graph Generation.

While recent debiasing methods for Scene Graph Generation (SGG) have shown impressive performance, these efforts often attribute model bias solely to the long-tail distribution of relationships, overlooking the more profound causes stemming from skewed object and object pair distributions. In this paper, we employ causal inference techniques to model the causality among these observed skewed distributions. Our insight lies in the ability of causal inference to capture the unobservable causal effects between complex distributions, which is crucial for tracing the roots of model bias.

Unbiased Scene Graph Generation via Two-Stage Causal Modeling.

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships.

Rainbow: reliable personally identifiable information retrieval across multi-cloud.

Personally identifiable information (PII) refers to any information that links to an individual. Sharing PII is extremely useful in public affairs yet hard to implement due to the worries about privacy violations. Building a PII retrieval service over multi-cloud, which is a modern strategy to make services stable where multiple servers are deployed, seems to be a promising solution.

Unsupervised Fusion Feature Matching for Data Bias in Uncertainty Active Learning.

Active learning (AL) aims to sample the most valuable data for model improvement from the unlabeled pool. Traditional works, especially uncertainty-based methods, are prone to suffer from a data bias issue, which means that selected data cannot cover the entire unlabeled pool well. Although there have been lots of literature works focusing on this issue recently, they mainly benefit from the huge additional training costs and the artificially designed complex loss.