Yang Zhao

Yang Zhao, Lei Fan, Hyungjoon Seo

Retaining walls are often built to prevent excessive lateral movements of the ground surrounding an excavation site. During an excavation, failure of retaining walls could cause catastrophic accidents and hence their lateral deformations are monitored regularly. Laser scanning can rapidly acquire the spatial data of a relatively large area at fine spatial resolutions, which is ideal for monitoring retaining walls' deformations. This paper attempts to apply laser scanning to measurements of the lateral deformations of a soil mixing retaining wall at an ongoing excavation site. Reference measurements by total station and inclinometer were also conducted to verify those from the laser scanning. The deformations derived using laser scanning data were consistent with the reference measurements at the top part of the retaining wall (i.e., mainly the ring beam of the wall). This research also shows that the multi-scale-model-to-model method was the most accurate deformation estimation method on the research data.

Hongjie Zhu, Zeyu Zhang, Guansong Pang et al.

Weakly supervised semantic segmentation (WSSS) typically utilizes limited semantic annotations to obtain initial Class Activation Maps (CAMs). However, due to the inadequate coupling between class activation responses and semantic information in high-dimensional space, the CAM is prone to object co-occurrence or under-activation, resulting in inferior recognition accuracy. To tackle this issue, we propose DOEI, Dual Optimization of Embedding Information, a novel approach that reconstructs embedding representations through semantic-aware attention weight matrices to optimize the expression capability of embedding information. Specifically, DOEI amplifies tokens with high confidence and suppresses those with low confidence during the class-to-patch interaction. This alignment of activation responses with semantic information strengthens the propagation and decoupling of target features, enabling the generated embeddings to more accurately represent target features in high-level semantic space. In addition, we propose a hybrid-feature alignment module in DOEI that combines RGB values, embedding-guided features, and self-attention weights to increase the reliability of candidate tokens. Comprehensive experiments show that DOEI is an effective plug-and-play module that empowers state-of-the-art visual transformer-based WSSS models to significantly improve the quality of CAMs and segmentation performance on popular benchmarks, including PASCAL VOC (+3.6%, +1.5%, +1.2% mIoU) and MS COCO (+1.2%, +1.6% mIoU). Code will be available at https://github.com/AIGeeksGroup/DOEI.