Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer

Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large number of distribution parameters. In this paper, we discover that the first layer of a deep network possesses multiple disparate optima when solely retrained. This indicates a large posterior variance when the first layer is altered by a Bayesian layer, which motivates us to design a spatial-temporal-fusion BNN (STF-BNN) for efficiently scaling BNNs to large models: (1) first normally train a neural network from scratch to realize fast training; and (2) the first layer is converted to Bayesian and inferred by employing stochastic variational inference, while other layers are fixed. Compared to vanilla BNNs, our approach can greatly reduce the training time and the number of parameters, which contributes to scale BNNs efficiently. We further provide theoretical guarantees on the generalizability and the capability of mitigating overconfidence of STF-BNN. Comprehensive experiments demonstrate that STF-BNN (1) achieves the state-of-the-art performance on prediction and uncertainty quantification; (2) significantly improves adversarial robustness and privacy preservation; and (3) considerably reduces training time and memory costs.

An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving

Understanding how trust is built over time is essential, as trust plays an important role in the acceptance and adoption of automated vehicles (AVs). This study aimed to investigate the effects of system performance and participants' trust preconditions on dynamic situational trust during takeover transitions. We evaluated the dynamic situational trust of 42 participants using both self-reported and behavioral measures while watching 30 videos with takeover scenarios. The study was a 3 by 2 mixed-subjects design, where the within-subjects variable was the system performance (i.e., accuracy levels of 95\%, 80\%, and 70\%) and the between-subjects variable was the preconditions of the participants' trust (i.e., overtrust and undertrust). Our results showed that participants quickly adjusted their self-reported situational trust (SST) levels which were consistent with different accuracy levels of system performance in both trust preconditions. However, participants' behavioral situational trust (BST) was affected by their trust preconditions across different accuracy levels. For instance, the overtrust precondition significantly increased the agreement fraction compared to the undertrust precondition. The undertrust precondition significantly decreased the switch fraction compared to the overtrust precondition. These results have important implications for designing an in-vehicle trust calibration system for conditional AVs.

Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline

The advancement in machine learning and artificial intelligence is promoting the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of Motor Vehicles (CA DMV) has launched the Autonomous Vehicle Tester Program, which collects and releases reports related to Autonomous Vehicle Disengagement (AVD) from autonomous driving. Understanding the causes of AVD is critical to improving the safety and stability of the AV system and provide guidance for AV testing and deployment. In this work, a scalable end-to-end pipeline is constructed to collect, process, model, and analyze the disengagement reports released from 2014 to 2020 using natural language processing deep transfer learning. The analysis of disengagement data using taxonomy, visualization and statistical tests revealed the trends of AV testing, categorized cause frequency, and significant relationships between causes and effects of AVD. We found that (1) manufacturers tested AVs intensively during the Spring and/or Winter, (2) test drivers initiated more than 80% of the disengagement while more than 75% of the disengagement were led by errors in perception, localization & mapping, planning and control of the AV system itself, and (3) there was a significant relationship between the initiator of AVD and the cause category. This study serves as a successful practice of deep transfer learning using pre-trained models and generates a consolidated disengagement database allowing further investigation for other researchers.

Learning Compatible Embeddings

Achieving backward compatibility when rolling out new models can highly reduce costs or even bypass feature re-encoding of existing gallery images for in-production visual retrieval systems. Previous related works usually leverage losses used in knowledge distillation which can cause performance degradations or not guarantee compatibility. To address these issues, we propose a general framework called Learning Compatible Embeddings (LCE) which is applicable for both cross model compatibility and compatible training in direct/forward/backward manners. Our compatibility is achieved by aligning class centers between models directly or via a transformation, and restricting more compact intra-class distributions for the new model. Experiments are conducted in extensive scenarios such as changes of training dataset, loss functions, network architectures as well as feature dimensions, and demonstrate that LCE efficiently enables model compatibility with marginal sacrifices of accuracies. The code will be available at https://github.com/IrvingMeng/LCE.

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g., in cases of surveillance and photo-tagging). To address it, current methods either deploy pose-specific models or frontalize faces by additional modules. Still, they ignore the fact that identity information should be consistent across poses and are not realizing the data imbalance between frontal and profile face images during training. In this paper, we propose an efficient PoseFace framework which utilizes the facial landmarks to disentangle the pose-invariant features and exploits a pose-adaptive loss to handle the imbalance issue adaptively. Extensive experimental results on the benchmarks of Multi-PIE, CFP, CPLFW and IJB have demonstrated the superiority of our method over the state-of-the-arts.

Predicting Driver Takeover Time in Conditionally Automated Driving

It is extremely important to ensure a safe takeover transition in conditionally automated driving. One of the critical factors that quantifies the safe takeover transition is takeover time. Previous studies identified the effects of many factors on takeover time, such as takeover lead time, non-driving tasks, modalities of the takeover requests (TORs), and scenario urgency. However, there is a lack of research to predict takeover time by considering these factors all at the same time. Toward this end, we used eXtreme Gradient Boosting (XGBoost) to predict the takeover time using a dataset from a meta-analysis study [1]. In addition, we used SHAP (SHapley Additive exPlanation) to analyze and explain the effects of the predictors on takeover time. We identified seven most critical predictors that resulted in the best prediction performance. Their main effects and interaction effects on takeover time were examined. The results showed that the proposed approach provided both good performance and explainability. Our findings have implications on the design of in-vehicle monitoring and alert systems to facilitate the interaction between the drivers and the automated vehicle.

Bias-Tolerant Fair Classification

The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decision is disturbed by sensitive features, while the selection bias occurs when subjective bias exists during the data sampling. Even worse, models trained on such data can inherit or even intensify the discrimination. Most algorithmic fairness approaches perform an empirical risk minimization with predefined fairness constraints, which tends to trade-off accuracy for fairness. However, such methods would achieve the desired fairness level with the sacrifice of the benefits (receive positive outcomes) for individuals affected by the bias. Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias. B-FARL takes the biased data as input, calls a model that approximates the one trained with fair but latent data, and thus prevents discrimination without constraints required. In addition, we show the effective components by decomposing B-FARL, and we utilize the meta-learning framework for the B-FARL optimization. The experimental results on real-world datasets show that our method is empirically effective in improving fairness towards the direction of true but latent labels.

Nonlinear Hawkes Processes in Time-Varying System

Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of applications, their expressive ability is limited due to three key hypotheses: parametric, linear and homogeneous. Recent work has attempted to address these limitations separately. This work aims to overcome all three assumptions simultaneously by proposing the flexible state-switching Hawkes processes: a flexible, nonlinear and nonhomogeneous variant where a state process is incorporated to interact with the point processes. The proposed model empowers Hawkes processes to be applied to time-varying systems. For inference, we utilize the latent variable augmentation technique to design two efficient Bayesian inference algorithms: Gibbs sampler and mean-field variational inference, with analytical iterative updates to estimate the posterior. In experiments, our model achieves superior performance compared to the state-of-the-art competitors.

Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior

Although various clustering methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image clustering tasks, most of the available approaches fail to realize automatic determination of cluster number, nor have they derived an exact distribution for the number of looks. To overcome these limitations and achieve robust unsupervised classification of PolSAR images, this paper proposes the variational Bayesian Wishart mixture model (VBWMM), where variational Bayesian expectation maximization (VBEM) technique is applied to estimate the variational posterior distribution of model parameters iteratively. Besides, covariance matrix similarity and geometric similarity are combined to incorporate spatial information of PolSAR images. Furthermore, we derive a new distribution named inverse gamma-gamma (IGG) prior that originates from the log-likelihood function of proposed model to enable efficient handling of number of looks. As a result, we obtain a closed-form variational lower bound, which can be used to evaluate the convergence of proposed model. We validate the superiority of proposed method in clustering performance on four real-measured datasets and demonstrate significant improvements towards conventional methods. As a by-product, the experiments show that our proposed IGG prior is effective in estimating the number of looks.

MagFace: A Universal Representation for Face Recognition and Quality Assessment

The performance of face recognition system degrades when the variability of the acquired faces increases. Prior work alleviates this issue by either monitoring the face quality in pre-processing or predicting the data uncertainty along with the face feature. This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face. Under the new loss, it can be proven that the magnitude of the feature embedding monotonically increases if the subject is more likely to be recognized. In addition, MagFace introduces an adaptive mechanism to learn a wellstructured within-class feature distributions by pulling easy samples to class centers while pushing hard samples away. This prevents models from overfitting on noisy low-quality samples and improves face recognition in the wild. Extensive experiments conducted on face recognition, quality assessments as well as clustering demonstrate its superiority over state-of-the-arts. The code is available at https://github.com/IrvingMeng/MagFace.