Self-taught learning (STL) is a promising solution that reduces the performance gap between weakly supervised and fully supervised learning for easily accessible, label-free images. The success of traditional STL solutions relies on the assumption that the target appearance is completely visible and well-defined. In real-world facial expression recognition scenarios, however, saliency regions are often partially occluded, which significantly hampers the generalization capability of STL methods. Nevertheless, few studies have investigated the impact of occlusion on STL. In this paper, we propose an interweaved autoencoder network for weakly supervised facial expression recognition in occlusion scenarios. The key innovation of our network lies in the Residual Connection Union (RCU) blocks that can integrate the Convolutional Neural Network (CNN) and Transformer layers into a multi-scale structure. The RCU enables a progressive masking strategy to accurately identify and focus on contributive yet often overlooked image patches by analyzing the relationships among region-level target representations. In addition, we introduce a self-knowledge distillation module for the effective training of the proposed autoencoder network. Extensive experiments are conducted on four public datasets to demonstrate the superiority of our method over related works.

As the market demand for fast-charging lithium-ion batteries grows, the ability to prognose battery degradation based on high-rate charging profiles becomes more valuable, offering a potential solution to alleviate consumer anxiety about the safety and reliability of electric vehicles. However, accurate degradation prognosis remains challenging due to the high variability of charging protocols across cells. To this end, this study proposes a degradation prognosis approach for fast-charging batteries with improved domain adaptation. Incremental capacity analysis of primary phase transition is determined by partial charge voltage within 8 min. Then, a probabilistic neural network is constructed to collaboratively predict two key degradation metrics. A domain-adaptation method that incorporates uncertainty-aware pseudolabeling is introduced, enabling more accurate modeling of battery degradation across varying fast-charging protocols and cell types. Validation using two battery datasets demonstrates that future degradation metrics can be predicted with high accuracy, reduced uncertainty, and lower computational costs. The research outcome also suggests that the data-driven model can be more transparent and reliable by encoding charging voltage into incremental capacity rather than directly predicting battery performance.

This paper introduces RealityEffects, a desktop authoring interface designed for editing and augmenting 3D volumetric videos with object-centric annotations and visual effects. RealityEffects enhances volumetric capture by introducing a novel method for augmenting captured physical motion with embedded, responsive visual effects, referred to as object-centric augmentation. In RealityEffects, users can interactively attach various visual effects to physical objects within the captured 3D scene, enabling these effects to dynamically move and animate in sync with the corresponding physical motion and body movements. The primary contribution of this paper is the development of a taxonomy for such object-centric augmentations, which includes annotated labels, highlighted objects, ghost effects, and trajectory visualization. This taxonomy is informed by an analysis of 120 edited videos featuring object-centric visual effects. The findings from our user study confirm that our direct manipulation techniques lower the barriers to editing and annotating volumetric captures, thereby enhancing interactive and engaging viewing experiences of 3D volumetric videos.

We introduce RealityCanvas, a mobile AR sketching tool that can easily augment real-world physical motion with responsive hand-drawn animation. Recent research in AR sketching tools has enabled users to not only embed static drawings into the real world but also dynamically animate them with physical motion. However, existing tools often lack the flexibility and expressiveness of possible animations, as they primarily support simple line-based geometry. To address this limitation, we explore both expressive and improvisational AR sketched animation by introducing a set of responsive scribble animation techniques that can be directly embedded through sketching interactions: 1) object binding, 2) flip-book animation, 3) action trigger, 4) particle effects, 5) motion trajectory, and 6) contour highlight. These six animation effects were derived from the analysis of 172 existing video-edited scribble animations. We showcase these techniques through various applications, such as video creation, augmented education, storytelling, and AR prototyping. The results of our user study and expert interviews confirm that our tool can lower the barrier to creating AR-based sketched animation, while allowing creative, expressive, and improvisational AR sketching experiences.