Ping Ma

To address the challenge of satisfying strict Service-Level Agreement (SLA) requirements for concurrent smart video surveillance tasks in heterogeneous edge computing environments, an SLA-aware adaptive scheduling algorithm for heterogeneous computing collaboration is proposed. First, a mixed-task flow model is constructed, and a finite-state Markov chain is utilized to dynamically model the time-varying wireless channel. Second, a Dueling Double Deep Q-Network (Dueling DDQN) scheduling algorithm based on SLA awareness and channel adaptation is proposed, with a designed SLA action-masking mechanism. This mechanism advances hard delay constraints to the decision-generation stage, dynamically prunes the action space based on real-time channel conditions and node loads, and filters out actions predicted to violate the SLA before execution. Experimental results show that the proposed algorithm coordinates heterogeneous computing resources between the cloud center and the edge and exhibits earlier empirical reward stabilization and lower task-violation rates than the compared learning-based baselines under the tested workload conditions.

Joint semantic segmentation and monocular depth estimation are essential for environmental perception of Autonomous Underwater Vehicles (AUVs), yet achieving high-precision real-time multi-task perception remains challenging due to severe underwater optical degradation and the limited computational resources of AUV platforms. This paper proposes an efficient MobileMamba-Based Multi-Task Learning Network (MM-MTL) for underwater scene perception. The network employs MobileMamba, a lightweight State Space Model (SSM) architecture, as the backbone to balance prediction accuracy and computational efficiency. An efficient multi-task collaborative decoder is further designed, comprising a Local Detail Enhancement (LDE) module that sharpens edge features in degraded underwater images via multi-scale parallel pooling, and a Dynamic Attention Fusion Strategy (DAFS) that adaptively fuses semantic and depth features across channel and spatial dimensions, exploiting cross-task synergies while suppressing underwater noise. To mitigate the scarcity of jointly annotated underwater data, we construct a high-quality multi-task dataset, FLSea-MTL, from the real-world FLSea monocular depth dataset through foundation-model-guided pseudo-label generation and meticulous manual refinement. Experiments on FLSea-MTL and the MIMIR-UW benchmark demonstrate that MM-MTL achieves state-of-the-art accuracy-efficiency trade-off, with only 8.66M parameters and 10.67G FLOPs, and ablation studies validate the efficacy of the collaborative decoder and the MobileMamba backbone over traditional CNN and Vision Transformer architectures.

We propose Text-Aware Salient Object Ranking (TA-SOR), a multimodal framework for Salient Object Ranking (SOR) that addresses the limitations of purely visual models in capturing semantic dominance and contextual competition among multiple salient instances. TA-SOR integrates visual representations with instance-level text–image attention extracted from a vision–language model, enabling semantic-aware ranking beyond appearance-based cues. An Multimodal-Aware Module performs hierarchical semantic injection through cross-modal interaction, while Semantic-Aligned Cross-modal Attention Guidance and a Ranking Graph Network explicitly model inter-object competition in a unified feature space. To support multimodal learning and evaluation, we construct TASD-ASSR, a semantically aligned extension of the ASSR dataset, and introduce Rb-SOR, a robustness metric based on image utilization rate that complements existing SOR metrics. With approximately 31.7M trainable parameters and frozen visual and language backbones, TA-SOR achieves state-of-the-art performance on ASSR, reaching 0.781 SA-SOR and 0.838 Rb-SOR. The results demonstrate that cross-modal semantic guidance effectively improves ranking accuracy, robustness, and segmentation quality, highlighting the potential of multimodal reasoning for salient object ranking.

Artificial intelligence is profoundly reshaping visual image analysis. This study addresses the core challenges in the intelligent classification of Impressionist oil paintings: significant global stylistic similarities exist across different painters' works (manifested as high consistency in compositional patterns, light and shadow representation, and color application). On the other hand, substantial intra-artist variations arise due to differences in subject matter selection and brushstroke techniques within individual painters' oeuvres. To tackle this image understanding problem, this paper proposes an Adaptive Heterogeneous Decision Network (AHDNet). Its key technical innovations include: 1) a decision-level heterogeneous fusion framework based on a semantic complementary mechanism, which deeply integrates the global contextual modeling capability of Swin Transformer with the local texture feature extraction ability of MobileNet; and 2) a novel lightweight Expert Selector that employs differentiable policy gradients to construct an adaptive gating mechanism for dynamic path optimization. Experiments conducted on our self-constructed large-scale Impressionist painting dataset (Impressionism-5.5K, containing 5,543 expert-annotated works from different creative periods of six artists) demonstrate that AHDNet achieves an accuracy of 94.77% in painter classification tasks, surpassing baseline models such as Swin Transformer and MobileNetV3 by 0.91% and 3.86%, respectively, while reducing computational costs by 14.40% compared to the best performing baseline. This research provides an effective computational framework for the understanding and analysis of artistic images, with potential applications in related fields such as cultural heritage digitization and intelligent image annotation. The dataset and code are publicly available at https://doi.org/10.5281/zenodo.17045140,K

Accurate fish volume estimation is essential for sustainable aquaculture management, yet traditional methods are invasive and costly, while existing non-invasive approaches rely on expensive multi-sensor setups. This study proposes a cost-effective infrared (IR)-only pipeline that reconstructs depth and Red Green Blue (RGB) from low-cost infrared videos (3 and coefficient of determination (R²) of 0.961 for volume estimation, outperforming state-of-the-art methods by 19–41% while reducing hardware costs by 80%. This work advances precision aquaculture by providing robust, deployable tools for feed optimization and health monitoring, promoting environmental sustainability amid rising global seafood demand.

In Hyperspectral Imaging (HSI), the detrimental influence of noise and distortions on data quality is profound, which has severely affected the following‐on analytics and decision‐making such as land mapping. This study presents an innovative framework for assessing HSI band quality and reconstructing the low‐quality bands, based on the Prophet model. By introducing a comprehensive quality metric to start, the authors approach factors in both spatial and spectral characteristics across local and global scales. This metric effectively captures the intricate noise and distortions inherent in the HSI data. Subsequently, the authors employ the Prophet model to forecast information within the low‐quality bands, leveraging insights from neighbouring high‐quality bands. To validate the effectiveness of the authors’ proposed model, extensive experiments on three publicly available uncorrected datasets are conducted. In a head‐to‐head comparison, the framework against six state‐of‐the‐art band reconstruction algorithms including three spectral methods, two spatial‐spectral methods and one deep learning method is benchmarked. The authors’ experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands. In addition, the authors assess the classification accuracy utilising these reconstructed bands. In various experiments, the results consistently affirm the efficacy of the authors’ method in HSI quality assessment and band reconstruction. Notably, the authors’ approach obviates the need for manually prefiltering of noisy bands. This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.