Liyi Xu

Liyi Xu, Lin Qi

Stereo image super-resolution aims to generate high-resolution images by leveraging complementary information from binocular systems. Although previous studies have achieved impressive results, the potential of intra-view and cross-view information has not been fully exploited. To address this issue, we propose a novel multi-scale interaction network for stereo image super-resolution. Specifically, we design a Multi-scale Spatial-Channel Attention Module that utilizes multi-scale large separable kernel attention and simple channel attention to improve intra-view feature extraction. Additionally, we propose a Dual-View Epipolar Attention Module, utilizing an optimal transport algorithm to achieve more accurate matching along the epipolar line. Extensive experimental and ablation studies show that our method achieves competitive results that outperform most SOTA methods.

Kexin Bella Yang, Menghan Liu, Liyi Xu et al.

In human-AI interaction, respecting user agency is essential for fostering trust and sustaining effective use of technology. In educational settings, dynamically integrating individual and collaborative learning offers pedagogical value by supporting personalized, self-paced learning experiences. Prior research has demonstrated the feasibility of this approach through intelligent tutoring systems and human-AI co-orchestration tools. However, how to balance teacher and student control in this process remains largely unexplored. This work explores the design space of how control can be distributed between teachers and students across the orchestration process, using participatory speed dating and a mixed-method analysis. We focus on three stages of the pairing process: before, during, and after, taking context in designing classroom orchestration tools that support teachers in dynamically coordinating student transitions between individual practice and collaborative problem-solving. It contributes empirical insights to the fields of educational technology and HCI by framing these findings within a theoretical design space, emphasizing the balance of multi-stakeholder agency and control. We propose design recommendations for achieving hybrid-control in analytic-based orchestration tools in pairing contexts. We recommend ensuring structured teacher guidance in the beginning, while progressively increasing student autonomy over time as activities unfold.

Shanglong Liu, Lin Qi, Junyu Dong et al.

In this work, we concentrate on exciting the intrinsic local consistency of stereo matching through the incorporation of superpixel soft constraints, with the objective of mitigating inaccuracies at the boundaries of predicted disparity maps. Our approach capitalizes on the observation that neighboring pixels are predisposed to belong to the same object and exhibit closely similar intensities within the probability volume of superpixels. By incorporating this insight, our method encourages the network to generate consistent probability distributions of disparity within each superpixel, aiming to improve the overall accuracy and coherence of predicted disparity maps. Experimental evalua tions on widely-used datasets validate the efficacy of our proposed approach, demonstrating its ability to assist cost volume-based matching networks in restoring competitive performance.