Lingyun Zhu

Chenghao Li, Chaoning Zhang, Boheng Zeng et al.

Attention mechanisms have significantly advanced visual models by capturing global context effectively. However, their reliance on large-scale datasets and substantial computational resources poses challenges in data-scarce and resource-constrained scenarios. Moreover, traditional self-attention mechanisms lack inherent spatial inductive biases, making them suboptimal for modeling local features critical to tasks involving smaller datasets. In this work, we introduce Large Kernel Convolutional Attention (LKCA), a novel formulation that reinterprets attention operations as a single large-kernel convolution. This design unifies the strengths of convolutional architectures locality and translation invariance with the global context modeling capabilities of self-attention. By embedding these properties into a computationally efficient framework, LKCA addresses key limitations of traditional attention mechanisms. The proposed LKCA achieves competitive performance across various visual tasks, particularly in data-constrained settings. Experimental results on CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet demonstrate its ability to excel in image classification, outperforming conventional attention mechanisms and vision transformers in compact model settings. These findings highlight the effectiveness of LKCA in bridging local and global feature modeling, offering a practical and robust solution for real-world applications with limited data and resources.

Mingming Fan, Lingyun Zhu

Subtle patterns in users' think-aloud (TA) verbalizations (i.e., utterances) are shown to be telltale signs of user experience (UX) problems and used to build artificial intelligence (AI) models or AI-assisted tools to help UX evaluators identify UX problems automatically or semi-automatically. Despite the potential of such verbalization patterns, they were uncovered with native English speakers. As most people who speak English are non-native speakers, it is important to investigate whether similar patterns exist in non-native English speakers' TA verbalizations. As a first step to answer this question, we conducted think-aloud usability testing with Chinese non-native English speakers and native English speakers using three common TA protocols. We compared their verbalizations and UX problems that they encountered to understand the effects of language and TA protocols. Our findings show that both language groups had similar amounts and proportions of verbalization categories, encountered similar problems, and had similar verbalization patterns that indicate UX problems. Furthermore, TA protocols did not significantly affect the correlations between verbalizations and problems. Based on the findings, we present three design implications for UX practitioners and the design of AI-assisted analysis tools.