Large-scale multimedia retrieval systems increasingly rely on compressed visual representations for storage and transmission efficiency. However, aggressive lossy compression risks degrading the structural cues (edges, boundaries, and semantic layouts) on which retrieval models depend. Generative image compression can synthesize perceptually plausible details at low bitrates, yet existing methods separate structure-critical from synthesizable information either statically or implicitly, leaving retrieval-relevant structure unprotected. We introduce Uncertainty-Gated Variational Inference (UG-VI), a framework that embeds deterministic gating into hierarchical VAEs. Its ELBO derivation yields a decoupling KL term that explicitly penalizes withholding predictable, structure-critical information from transmission. The resulting codec, Uncertainty-gated Structural Compression (USC), ranks latent elements by hyperprior-predicted uncertainty (available at the decoder without side information), transmitting low-uncertainty structural latents and synthesizing high-uncertainty stochastic details from the conditional prior. On CLIC2020, USC achieves 41.38% FID BD-Rate reduction over MS-ILLM while maintaining distortion (PSNR BD-Rate: \(-1.13\%\)). Retrieval-after-compression evaluation on Oxford5k and RParis6k shows that USC preserves downstream retrieval accuracy (mAP) more effectively than both traditional and generative baselines, validating uncertainty-guided structure preservation as a retrieval-aware compression strategy.

Graph-based fraud detection (GFD) aims to identify fraud nodes within graph-structured data that significantly deviate from the majority of benign nodes. However, existing graph neural networks (GNNs) often struggle in GFD scenarios due to their reliance on homophily assumption, which is frequently violated by the inherent homophily-heterophily mixture of fraud graphs. Moreover, most methods focus primarily on local topology, overlooking mesoscopic community structures, making them less efficient in detecting suspicious patterns like densely connected subgraphs. To address the aforementioned issues, we present NeCo, a novel approach that integrates mixture of neighborhood and community experts for graph-based fraud detection. Specifically, we first introduce a fraud-discriminative representation preservation mechanism from a neighborhood perspective, leveraging the empirical finding that fraud nodes tend to exhibit larger feature propagation discrepancies compared to benign nodes. We then design a community-oriented node representation module that models structural compactness among nodes, enabling the detection of suspicious topological patterns associated with fraud behaviors. By integrating these two complementary perspectives, NeCo can effectively captures both local inconsistency and global structural irregularity. Extensive experiments across five real-world datasets demonstrate the effectiveness of our proposed NeCo over state-of-the-art baselines.

High-quality, low-latency video compression is essential for real-time multimedia applications, particularly in resource-constrained edge computing scenarios and large-scale systems. However, improving rate-distortion (RD) performance in neural video codecs (NVCs) often increases encoding complexity, hindering their adoption in latency-sensitive applications such as real-time video retrieval, robot navigation, and large-scale video indexing. To address this, we propose a novel distributed neural video codec (DNVC) that significantly improves RD performance while reducing encoder-side complexity. Our approach introduces a novel feature-channel conditional coding paradigm, integrating two key components: (1) a feature-channel conditioned entropy model that leverages implicit feature extraction to capture complex patterns and exploits cross-channel dependencies for efficient compression; (2) a high-precision side information generator that enables low-latency encoding and enhances decoder-side information quality by leveraging multi-frame reference. Experimental results show that our DNVC outperforms current state-of-the-art (SOTA) distributed video codecs and several NVCs in RD performance. Specifically, our codec achieves an average PSNR improvement of 1.2 dB compared to the current SOTA DNVC and 2 dB more than the widely-used H.264 codec. In low-latency scenarios, our method achieves a 5.5× to 14.3× speedup in encoding compared to previous SOTA NVCs.

Traffic prediction plays a pivotal role in intelligent transportation systems. Most existing studies only predict traffic flow for a specific time period based on traffic data from a short period, such as an hour, overlooking the influence of periodicity present in traffic data. Moreover, most of the existing advanced methods rely on manually constructed spatio-temporal graphs for joint modeling, or use pure spatial and pure temporal modules to separately model spatial and temporal features, which limits the learning of complex spatio-temporal patterns in traffic data due to structural inadequacies in the model. To address these issues, we propose a novel approach by constructing a learnable long-range spatio-temporal graph, which can better capture complex patterns in traffic data. We introduce a new model, LLGformer, which improves upon traditional Transformer-style models, facilitating more efficient learning of traffic flow data by integrating long-range historical information. Leveraging attention mechanisms on a spatiotemporal graph enables direct interaction of information across different time slices and locations. Additionally, we propose two optimization strategies to further boost the speed of training and inference. Extensive experiments on four real-world datasets show that the new model significantly outperforms state-of-the-art methods.

Medical image segmentation is a critical task in the healthcare field. While deep learning techniques have shown promise in this area, they often require a large number of accurately labeled images. To address this issue, semisupervised learning has emerged as a potential solution by reducing the reliance on precise annotations. Among these approaches, the student-teacher framework has garnered attention, but it is limited in its reliance solely on the teacher model for information. To overcome this limitation, we propose a prototype-based mutual consistency learning (PMCL) framework. This framework utilizes two branches that learn from each other, incorporating supervision loss and consistency loss to adapt to minor data perturbations and structural differences. By employing prototype consistency learning, we are able to achieve reliable consistency loss. Our experiments on three public medical image datasets demonstrate that PMCL outperforms other state-of-the-art methods, indicating its potential in semisupervised medical image segmentation. Our framework has the potential to assist medical professionals in enhancing their diagnoses and delivering improved patient care.

In this paper, a discretization‐free approach based on the physics‐informed neural network (PINN) is proposed for solving the forward and inverse problems governed by the nonlinear convection‐diffusion‐reaction (CDR) systems. By embedding physical information described by the CDR system in the feedforward neural networks, PINN is trained to approximate the solution of the system without the need of labeled data. The good performance of PINN in solving the forward problem of the nonlinear CDR systems is verified by studying the problems of gas‐solid adsorption and autocatalytic reacting flow. For CDR systems with different Péclet number, PINN can largely eliminate the numerical diffusion and unphysical oscillations in traditional numerical methods caused by high Péclet number. Meanwhile, the PINN framework is implemented to solve the inverse problem of nonlinear CDR systems and the results show that the unknown parameters can be effectively recognized even with high noisy data. It is concluded that the established PINN algorithm has good accuracy, convergence, and robustness for both the forward and inverse problems of CDR systems.