Jincheng Zhu | Searcharxiv

arXiv2026

Pinching Antenna Systems (PASS) for Air-Ground Integrated Sensing and Communications (ISAC)

This paper proposes a multi-user single-target air-ground integrated sensing and communication (ISAC) system, in which a ground pinching-antenna system (PASS) transmits communication and sensing signals and an unmanned aerial vehicle (UAV) receives the target echo. The communication and sensing beamforming vectors, pinching antenna positions, and UAV position are jointly optimized to minimize the weighted Cramér-Rao bound (CRB). A penalty dual decomposition (PDD) algorithm is developed for the resulting nonconvex problem. The auxiliary variable and beamforming subproblems admit globally optimal solutions in semi-closed form, while the positions of the pinching antennas and the UAV are updated by successive convex approximation (SCA). Under mild conditions, every accumulation point of the sequence generated by our proposed algorithm is a Karush-Kuhn-Tucker (KKT) point of the reformulated problem. Numerical results show that the proposed PASS-UAV design achieves a lower weighted CRB than conventional multiple-input multiple-output (MIMO) and PASS benchmarks.

eess.SP↗

arXiv2022

CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation

Acquiring the most representative examples via active learning (AL) can benefit many data-dependent computer vision tasks by minimizing efforts of image-level or pixel-wise annotations. In this paper, we propose a novel Collaborative Panoptic-Regional Active Learning framework (CPRAL) to address the semantic segmentation task. For a small batch of images initially sampled with pixel-wise annotations, we employ panoptic information to initially select unlabeled samples. Considering the class imbalance in the segmentation dataset, we import a Regional Gaussian Attention module (RGA) to achieve semantics-biased selection. The subset is highlighted by vote entropy and then attended by Gaussian kernels to maximize the biased regions. We also propose a Contextual Labels Extension (CLE) to boost regional annotations with contextual attention guidance. With the collaboration of semantics-agnostic panoptic matching and regionbiased selection and extension, our CPRAL can strike a balance between labeling efforts and performance and compromise the semantics distribution. We perform extensive experiments on Cityscapes and BDD10K datasets and show that CPRAL outperforms the cutting-edge methods with impressive results and less labeling proportion.

cs.CV↗