Facial Expression Recognition (FER) has received considerable research attention owing to its poor robustness in real-world scenarios. This issue, defined as the uncertainty problem in FER, is often solved by recognizing the noise samples in FER datasets. Unlike noise samples with incorrect labels, ambiguous samples exhibit mixed emotions that align with multiple basic expressions. It makes them indistinguishable in training and harms model robustness. To address this issue, we propose an ambiguity-aware FER framework called Co-dance with Ambiguity (CoA). CoA combines an Emotion Extraction Module (EEM) and an Expression Description Module (EDM) to leverage ambiguity for better performance and robustness. Specifically, EEM employs a coupled-stream structure to extract both representative and detailed features through diverse-scale fusion and patch-attention sensing. EDM adjusts ground-truth labels of ambiguous samples by introducing label pairs derived from the top two highest predictions, describing the mixed-emotion nature. The pairs guide the model to align feature extraction with the inherent ambiguity of ambiguous samples during training. Extensive experiments on five in-the-wild FER datasets demonstrate the superiority of CoA over advanced methods. Moreover, introducing ambiguity-aware strategies enriches feature representations and significantly enhances robustness when faced with a high ratio of ambiguous samples in FER.

Clouds in optical remote sensing images (ORSI) significantly limit image utilization. Traditional cloud removal methods using single or multi-temporal data sources struggle to ensure reliable reconstruction for thick cloud areas. Synthetic Aperture Radar (SAR) images are increasingly used to recover information obscured by clouds, but their performance in cloud-obscured regions is unstable. Therefore, an adaptive cloud removal network for remote sensing images, named DG2-TCR, is proposed based on SAR-driven dual-flow fusion guidance (DFG). DG2-TCR uses SAR and ORSI to construct DFG, including local spatial-spectral feature reconstruction (LSSFR) flow and global texture feature compensation (GTFC). LSSFR, driven by ORSI and SAR, efficiently extracts useful features in non-cloud areas and focuses on local information reconstruction using the designed spatial-spectral features inference reconstruction block (SSIRB). Based on SAR images, GTFC guides the compensation of global texture information. DFG can adaptively extract features and reconstruct missing information from local and global scales. The public SEN12MS-CR-TS dataset is divided into four sub-datasets with different coverage to evaluate the recovering capability in varying clouds. Experiments show that the PSNR, SSIM, RMSE, FID, and NCC indicator values on four sub-datasets and the SIMLE-CR dataset are better than the seven comparison methods. Furthermore, the ablation experiments show that the generalization and robustness of this proposed method on images with different cloud coverage are better than other comparison methods. Therefore, DG2-TCR can reliably recover information on cloud occlusions with various coverage and thickness, which is significant for cloud removal in practical applications.

The ultrasonic radar is a crucial part of the automatic parking system (APS) and requires thorough testing to ensure safety. The field operational test (FOT) is expensive and inefficient, making simulation testing the preferred method for evaluating autonomous driving systems. However, a gap remains between the performance of current simulation ultrasonic radar models and the real-world ultrasonic radars in real driving environments. To address these challenges, an ultrasonic radar in-the-loop accelerated test method for APS is proposed. First, a high-fidelity ultrasonic radar in-the-loop test bench is established and validated. To reduce the high cost and time duration of hardware-in-the-loop (HIL) testing, a Bayesian optimization (BO)-based accelerated testing framework is designed, minimizing test numbers by using the prior information to guide the search process. Finally, experiments are conducted using the ultrasonic radar in-the-loop test bench to compare the Bayesian accelerated test with the combination test (CT) for the automatic parking algorithm. The test results indicate that the ultrasonic radar in-the-loop test bench demonstrates greater authenticity and accuracy than the simulation software, while the HIL testing offers improved efficiency and safety compared to the FOT. Moreover, the efficiency of the Bayesian accelerated test is 4.9 times greater than that of the CT while still ensuring adequate scenario coverage.