Junyu Dong

Bosen Lin, Feng Gao, Yanwei Yu et al.

Underwater Image Enhancement (UIE) is an ill-posed problem where natural clean references are not available, and the degradation levels vary significantly across semantic regions. Existing UIE methods treat images with a single global model and ignore the inconsistent degradation of different scene components. This oversight leads to significant color distortions and loss of fine details in heterogeneous underwater scenes, especially where degradation varies significantly across different image regions. Therefore, we propose SUCode (Semantic-aware Underwater Codebook Network), which achieves adaptive UIE from semantic-aware discrete codebook representation. Compared with one-shot codebook-based methods, SUCode exploits semantic-aware, pixel-level codebook representation tailored to heterogeneous underwater degradation. A three-stage training paradigm is employed to represent raw underwater image features to avoid pseudo ground-truth contamination. Gated Channel Attention Module (GCAM) and Frequency-Aware Feature Fusion (FAFF) jointly integrate channel and frequency cues for faithful color restoration and texture recovery. Extensive experiments on multiple benchmarks demonstrate that SUCode achieves state-of-the-art performance, outperforming recent UIE methods on both reference and no-reference metrics. The code will be made public available at https://github.com/oucailab/SUCode.

Feng Gao, Sheng Liu, Chuanzheng Gong et al.

Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification by leveraging the complementary information from multiple data sources. Existing methods confront two challenges: inter-frequency multi-source feature coupling and inconsistency of complementary information exploration. To solve these issues, we present a Prototype-based Information Compensation Network (PICNet) for land cover classification based on HSI and SAR/LiDAR data. Specifically, we first design a frequency interaction module to enhance the inter-frequency coupling in multi-source feature extraction. The multi-source features are first decoupled into high- and low-frequency components. Then, these features are recoupled to achieve efficient inter-frequency communication. Afterward, we design a prototype-based information compensation module to model the global multi-source complementary information. Two sets of learnable modality prototypes are introduced to represent the global modality information of multi-source data. Subsequently, cross-modal feature integration and alignment are achieved through cross-attention computation between the modality-specific prototype vectors and the raw feature representations. Extensive experiments on three public datasets demonstrate the significant superiority of our PICNet over state-of-the-art methods. The codes are available at https://github.com/oucailab/PICNet.

Haosheng He, Jianpeng Qi, Chao Liu et al.

In compute-first networking, maintaining fresh and accurate status information at the network edge is crucial for effective access to remote services. This process typically involves three phases: Status updating, user accessing, and user requesting. However, current studies on status effectiveness, such as Age of Information at Query (QAoI), do not comprehensively cover all these phases. Therefore, this paper introduces a novel metric, TPAoI, aimed at optimizing update decisions by measuring the freshness of service status. The stochastic nature of edge environments, characterized by unpredictable communication delays in updating, requesting, and user access times, poses a significant challenge when modeling. To address this, we model the problem as a Markov Decision Process (MDP) and employ a Dueling Double Deep Q-Network (D3QN) algorithm for optimization. Extensive experiments demonstrate that the proposed TPAoI metric effectively minimizes AoI, ensuring timely and reliable service updates in dynamic edge environments. Results indicate that TPAoI reduces AoI by an average of 47\% compared to QAoI metrics and decreases update frequency by an average of 48\% relative to conventional AoI metrics, showing significant improvement.