With the easy availability of low-quality 3D facial data, research on low-quality 3D face recognition (FR) has gained widespread attention. However, most existing methods struggle to strike a balance between accuracy and computational complexity, with the enormous parameters being one of the primary challenges. To address this issue, we propose a novel lightweight multi-scale group shift fusion network (LMGSNet) for low-quality 3D FR. Specifically, we construct a multi-scale group attention layer-by-layer shift fusion module (GALSF) based on our proposed channel shift fusion (CSF) method, which integrates attention convolution and grouping operation to capture critical local features (such as the nose, mouth, and forehead) while significantly reducing parameters. Furthermore, we design a novel split-aggregate local feature fusion module (SALF) to enhance local features representation and capturing rich discriminative features. Extensive experiments on three challenging low-quality 3D face datasets demonstrate that our model achieves competitive recognition accuracy with the lowest parameters.

Domain generalization aims to learn a model with good generalization ability, that is, the learned model should not only perform well on several seen domains but also on unseen domains with different data distributions. State-of-the-art domain generalization methods typically train a representation function followed by a classifier jointly to minimize both the classification risk and the domain discrepancy. However, when it comes to model selection, most of these methods rely on traditional validation routines that select models solely based on the lowest classification risk on the validation set. In this paper, we theoretically demonstrate a trade-off between minimizing classification risk and mitigating domain discrepancy, i.e., it is impossible to achieve the minimum of these two objectives simultaneously. Motivated by this theoretical result, we propose a novel model selection method suggesting that the validation process should account for both the classification risk and the domain discrepancy. We validate the effectiveness of the proposed method by numerical results on several domain generalization datasets.

Automatic speech recognition (ASR) is a fundamental technology in the field of artificial intelligence. End-to-end (E2E) ASR is favored for its state-of-the-art performance. However, E2E speech recognition still faces speech spatial information loss and text local information loss, which results in the increase of deletion and substitution errors during inference. To overcome this challenge, we propose a novel Enhancing Hybrid Architecture with Fast Attention and Capsule Network (termed En-HACN), which can model the position relationships between different acoustic unit features to improve the discriminability of speech features while providing the text local information during inference. Firstly, a new CNN-Capsule Network (CNN-Caps) module is proposed to capture the spatial information in the spectrogram through the capsule output and dynamic routing mechanism. Then, we design a novel hybrid structure of LocalGRU Augmented Decoder (LA-decoder) that generates text hidden representations to obtain text local information of the target sequences. Finally, we introduce fast attention instead of self-attention in En-HACN, which improves the generalization ability and efficiency of the model in long utterances. Experiments on corpora Aishell-1 and Librispeech demonstrate that our En-HACN has achieved the state-of-the-art compared with existing works. Besides, experiments on the long utterances dataset based on Aishell-1-long show that our model has a high generalization ability and efficiency.

Image captioning is a challenging task that generates a natural language description based on the visual understanding of the given image. Significant region representation is a milestone in image captioning. Despite the great success of existing region-based works, they only focus on the salient objects and encode these objects independently, still plagued by the lack of global contextual information and visual relationships. In fact, the global contextual information and structured visual relationships are exactly the merits of traditional grid features and emerging scene graph features. In this paper, we present a Triple-Steam Feature Fusion Network (TSFNet) to leverage the complementary advantages of the grid, region, and scene graph triple-steam visual representations in image captioning. Concretely, in our TSFNet, a novel Dual-level Attention (DA) mechanism is proposed to simultaneously explore visual intrinsic properties and word-related attributes uniformly of different features. Then attention enhanced features of different modalities are mapped into a joint representation to guide the caption generation. Moreover, we design a new global-aware decoder, which leverages the concatenated representation of triple-steam features and the joint attention representation to obtain global visual guidance information, further refine the complex multimodal reasoning. To verify the effectiveness of our feature fusion model, we perform extensive experiments on the highly competitive MSCOCO dataset to evaluate the model quantitatively and qualitatively. The results illustrate that the proposed framework outperforms many state-of-the-art image captioning approaches in various evaluation metrics, and generates more accurate and abundant captions.

Assessing the cognitive workload of human interactants in mixed-initiative teams is a critical capability for autonomous interactive systems to enable adaptations that improve team performance. Yet, it is still unclear, due to diverging evidence, which sensing modality might work best for the determination of human workload. In this paper, we report results from an empirical study that was designed to answer this question by collecting eye gaze and electroencephalogram (EEG) data from human subjects performing an interactive multi-modal driving task. Different levels of cognitive workload were generated by introducing secondary tasks like dialogue, braking events, and tactile stimulation in the course of driving. Our results show that pupil diameter is a more reliable indicator for workload prediction than EEG. And more importantly, none of the five different machine learning models combining the extracted EEG and pupil diameter features were able to show any improvement in workload classification over eye gaze alone, suggesting that eye gaze is a sufficient modality for assessing human cognitive workload in interactive, multi-modal, multi-task settings.

Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other unseen (unknown but related) domains. To deal with challenging settings in DG where both data and label of the unseen domain are not available at training time, the most common approach is to design the classifiers based on the domain-invariant representation features, i.e., the latent representations that are unchanged and transferable between domains. Contrary to popular belief, we show that designing classifiers based on invariant representation features alone is necessary but insufficient in DG. Our analysis indicates the necessity of imposing a constraint on the reconstruction loss induced by representation functions to preserve most of the relevant information about the label in the latent space. More importantly, we point out the trade-off between minimizing the reconstruction loss and achieving domain alignment in DG. Our theoretical results motivate a new DG framework that jointly optimizes the reconstruction loss and the domain discrepancy. Both theoretical and numerical results are provided to justify our approach.

Invariance-principle-based methods such as Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite promising theory, such approaches fail in common classification tasks due to mixing of true invariant features and spurious invariant features1. To address this, we propose a framework based on the conditional entropy minimization (CEM) principle to filter-out the spurious invariant features leading to a new algorithm with a better generalization capability. We show that our proposed approach is closely related to the well-known Information Bottleneck (IB) framework and prove that under certain assumptions, entropy minimization can exactly recover the true invariant features. Our approach provides competitive classification accuracy compared to recent theoretically-principled state-of-the-art alternatives across several DG datasets.

Threads have recently been demonstrated as thin and flexible substrates for diagnostics and wearable devices. These substrates provide flexible 3D integration with cloth using standard textile methods. Our group has recently demonstrated a pH mapping smart-bandage using thread-based polyaniline pH sensors for chronic wound monitoring. Here, we utilize high-surface area sensing regions to present an improved pH mapping bandage with more intimate wound contact and a faster response time compared to our previous work. Additionally, we condense our readout electronics into a 3D printed 25mm button form factor capable of easy integration with our thread-based sensors. We demonstrate our modified bandage's improved performance through sensitivity and response time measurements. Furthermore, we present pH measurements in spiked biological serum demonstrating that our bandage remains sensitive in complex biological environments.