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. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail 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. However, three major technical challenges remain to be solved. The first is the privacy and access control of PII. In fact, each entry in PII can be shared to different users with different access rights. Hence, flexible and fine-grained access control is needed. Second, a reliable user revocation mechanism is required to ensure that users can be revoked efficiently, even if few cloud servers are compromised or collapse, to avoid data leakage. Third, verifying the correctness of received PII and locating a misbehaved server when wrong data are returned is crucial to guarantee user's privacy, but challenging to realize. In this paper, we propose Rainbow, a secure and practical PII retrieval scheme to solve the above issues. In particular, we design an important cryptographic tool, called Reliable Outsourced Attribute Based Encryption (ROABE) which provides data privacy, flexible and fine-grained access control, reliable immediate user revocation and verification for multiple servers simultaneously, to support Rainbow. Moreover, we present how to build Rainbow with ROABE and several necessary cloud techniques in real world. To evaluate the performance, we deploy Rainbow on multiple mainstream clouds, namely, AWS, GCP and Microsoft Azure, and experiment in browsers on mobile phones and computers. Both theoretical analysis and experimental results indicate that Rainbow is secure and practical.

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. The latter causes these methods to be redesigned when facing new models or tasks, which is very time-consuming and laborious. This article proposes a feature-matching-based uncertainty that resamples selected uncertainty data by feature matching, thus removing similar data to alleviate the data bias issue. To ensure that our proposed method does not introduce a lot of additional costs, we specially design a unsupervised fusion feature matching (UFFM), which does not require any training in our novel AL framework. Besides, we also redesign several classic uncertainty methods to be applied to more complex visual tasks. We conduct rigorous experiments on lots of standard benchmark datasets to validate our work. The experimental results show that our UFFM is better than the similar unsupervised feature matching technologies, and our proposed uncertainty calculation method outperforms random sampling, classic uncertainty approaches, and recent state-of-the-art (SOTA) uncertainty approaches.