Xiangyun Hu

Yuhao Wang, Xiang Hu, Lixin Wang et al.

Aerial-Ground Person Re-IDentification (AG-ReID) aims to retrieve specific persons across cameras with different viewpoints. Previous works focus on designing discriminative models to maintain the identity consistency despite drastic changes in camera viewpoints. The core idea behind these methods is quite natural, but designing a view-robust model is a very challenging task. Moreover, they overlook the contribution of view-specific features in enhancing the model's ability to represent persons. To address these issues, we propose a novel generative framework named SD-ReID for AG-ReID, which leverages generative models to mimic the feature distribution of different views while extracting robust identity representations. More specifically, we first train a ViT-based model to extract person representations along with controllable conditions, including identity and view conditions. We then fine-tune the Stable Diffusion (SD) model to enhance person representations guided by these controllable conditions. Furthermore, we introduce the View-Refined Decoder (VRD) to bridge the gap between instance-level and global-level features. Finally, both person representations and all-view features are employed to retrieve target persons. Extensive experiments on five AG-ReID benchmarks (i.e., CARGO, AG-ReIDv1, AG-ReIDv2, LAGPeR and G2APS-ReID) demonstrate the effectiveness of our proposed method. The source code will be available.

Zhili Zhang, Xiangyun Hu, Jiabo Xu

In this research, we introduce the enhanced automated quality assessment network (IBS-AQSNet), an innovative solution for assessing the quality of interactive building segmentation within high-resolution remote sensing imagery. This is a new challenge in segmentation quality assessment, and our proposed IBS-AQSNet allievate this by identifying missed and mistaken segment areas. First of all, to acquire robust image features, our method combines a robust, pre-trained backbone with a lightweight counterpart for comprehensive feature extraction from imagery and segmentation results. These features are then fused through a simple combination of concatenation, convolution layers, and residual connections. Additionally, ISR-AQSNet incorporates a multi-scale differential quality assessment decoder, proficient in pinpointing areas where segmentation result is either missed or mistaken. Experiments on a newly-built EVLab-BGZ dataset, which includes over 39,198 buildings, demonstrate the superiority of the proposed method in automating segmentation quality assessment, thereby setting a new benchmark in the field.