We introduce Daytime-Memory Guided Nighttime Image Enhancement (DMGNIE) framework, the first framework that turns long-running daytime surveillance videos of a single intersection into persistent “daytime memory” to guide nighttime image enhancement in traffic scenes. Our key insight is simple yet powerful: for a static scene, perfectly exposed daytime frames are, pixel-for-pixel, high-quality illumination prior for the same location under extreme low-light. Due to the complex lighting conditions in real-world traffic scenes, existing low-light image enhancement (LLIE) methods suffer from issues such as overexposure in highlight regions and noise amplification in low-light condition regions, which degrades the performance of downstream computer vision tasks. DMGNIE tackles these issues in two steps: (1) SegBMN, a semantic prior-based background modeling network, distills a clean, static daytime background from hours of video as scene prior guiding the enhancement of nighttime image; (2) a Foreground Localization-Guided Contrastive Learning module avoid the interference from the background prior with foreground objects during the guidance by maximizing the differences between foreground and background features. Finally, We conduct comprehensive experiments on real traffic surveillance datasets of two cities to evaluate the effectiveness. And the experimental results demonstrate that DMGNIE outperforms state-of-the-art baselines and achieves superior performance in challenging low-light conditions.

Knowledge graphs (KGs) have caught more and more attention in recent years. Currently, in some practical scenarios, KG embedding (KGE) models are expected to reduce their spatial complexity without losing much performance to address the challenges of storage limitations and knowledge reasoning efficiency. To achieve this, existing works use one or more large and high-performance teacher models to improve the performance of a lightweight student model via knowledge distillation (KD), thus meeting the requirements of some practical complicated applications. However, in resource-constrained scenarios, obtaining high-performance teacher models is challenging due to high training costs and significant storage requirements. Thus, enhancing the student model's performance without large teacher models is crucial. To address this issue, we propose Dual-View Mutual Distillation Framework for Knowledge Graph Embeddings (DMutDE), a distillation framework leveraging mutual learning for peer-to-peer distillation between two KGE models with different architectures. In KGE models, we notice that the way of modeling relational directed edges determines the model view of KGE model for learning KG data. Thus, integrating the model views from two different KGE models by KD into a student KGE model can improve its generalization, so as to increase its performance. To identify an effective dual-view fusion method, we design two modules in the DMutDE framework. Specifically, we design a novel soft-label fusion (SLF) module for noise filtering and response knowledge transfer. Then, we propose an entity embedding distillation (EED) module to distill structural features from each other. Finally, we conduct several comprehensive experiments on the standard open-source benchmarks to demonstrate that our framework achieves the state-of-the-art results. The code is available at https://github.com/RuizhouLiu/DMutDE.

Knowledge graph (KG) completion is a challenging yet essential task that has attracted increasing attention in recent years. While entities in KGs typically present complex semantics (a phenomenon known as polysemy), previous works primarily focus on holistic but often inaccurate representations of entities, neglecting the diversity of their semantics. This limitation results in suboptimal representations for entities within KGs. To address this issue, we propose a new method termed semantically guided KG embeddings (SGKGE), which captures the precise semantics of entities in KGs from a semantics-guided perspective. Specifically, SGKGE first guides the learning of holistic semantics of entities through a hyperbolic manifold with learnable shared curvature and a geometric attention-fusion module, facilitating efficient reasoning. Subsequently, SGKGE captures fine-grained semantics through a set of Cartesian product Riemannian manifolds with distinct curvatures, coupled with a semantic interactions module. This approach enables SGKGE to produce more accurate entity semantics and enhance downstream applications. Experimental results demonstrate that our model achieves state-of-the-art performance on six well-established KG completion benchmarks. The release code is available at https://github.com/RuizhouLiu/SGKGE.

Multimodal knowledge graph embeddings (MKGE) have recently garnered significant attention. Unlike traditional unimodal knowledge graph embeddings, MKGE integrates both structural and multimodal knowledge to represent entities within a unified framework. However, real-world entities exhibit heterogeneity, often resulting in semantic inconsistencies where structurally similar embeddings may diverge significantly in their multimodal representations. Previous approaches primarily focus on directly fusing structural and multimodal embeddings, thus overlooking the issue of semantic-embedding inconsistency. To tackle this issue, we propose a new multimodal knowledge graph embedding method via Lorentz-based contrastive learning (LCKGE). we firstly introduce a well-designed nearest-neighbor fusion module via contrastive learning for multimodal fusion. Then, an attention-based Lorentz transformation is proposed for capturing more complex geometric information in MKGs. Furthermore, a series of comprehensive experiments are conducted to demonstrate the effectiveness of our model. We provide the code and appendix of LCKGE in https://github.com/RuizhouLiu/LCKGE