Knowledge graphs (KGs) play a central role in enabling structured reasoning for AI applications. Recent research has advanced two prominent extensions of KGs: multimodal KGs, which integrate diverse data sources such as text, images, audio, and video; and temporal KGs, which capture the dynamic evolution of knowledge over time. However, these two paradigms remain largely disjoint—multimodal KGs typically ignore temporal dynamics, while temporal KGs overlook rich multimodal context. To bridge this gap, we introduceFocusKG, the first discrete-time multimodal knowledge graph that unifies four modalities (text, image, audio, and video) with temporal annotations. Built from the Focus news program, FocusKG captures real-world events as temporally grounded multimodal facts, offering new expressiveness for time-sensitive tasks. We further propose aDiscrete-TimeMultimodal Knowledge GraphEmbedding (DTME) method, a novel framework tailored for learning representations over temporal multimodal KGs. DTME is composed of three key modules: (1) Multimodal Preprocessing, which encodes raw inputs from each modality into vector representations using modality-specific encoders; (2) Cross-Modal Graph Enhancer, which captures semantic interplay between entity and relation modalities through role-aware subgraph construction and a modality interaction network; and (3) Multi-Branch Time Aggregation, which injects temporal signals by modeling interactions between time and modality-aware features. Extensive experiments demonstrate FocusKG's value and DTME's efficacy. On link prediction, DTME outperforms state-of-the-art multimodal KG models and temporal KG models. We release FocusKG as a benchmark to foster research in multimodal temporal reasoning.

Crack segmentation is crucial in monitoring infrastructure degradation. However, the strong contextual dependencies of long-spanned morphology and numerous fine-grained branches hinder high-precision segmentation performance. To tackle the above obstacles, we provide a pure Mamba-based model termed CrackMamba for accurate crack segmentation. CrackMamba utilizes the Mamba-based Feature Extractor (MFE) to effectively model global dependencies of long-spanned cracks with linear computational complexity for powerful representation. To capture fine-grained crack branches, we design a novel texture refinement module, which employs a multi-scale aggregation strategy and utilizes the MFE and a Normalized Soft-Frangi-Filter (NSFF) module to integrate hierarchical features from the decoder. The CrackMamba and NSFF exhibit strong complementarity. The NSFF module enhances the capability of CrackMamba to segment fine-grained crack textures, while CrackMamba effectively eliminating crack-unrelated curves extracted by NSFF module. Extensive experiments are conducted on two benchmark crack datasets, and the results demonstrate that the proposed CrackMamba achieves the state-of-the-art (SOTA) performance with fewer parameters and higher computational efficiency.

Recent advances in Multimodal Entity Linking leverage multimodal information to link target mentions to corresponding entities. However, existing methods uniformly adopt a “one-size-fits-all” approach, which overlooks the unique requirements of individual samples and fails to adequately balance modality-assisted disambiguation and modality-induced noise. Also, the commonly used separate large-scale visual and text pretrained models for feature extraction do not address inter-modal heterogeneity and the high computational cost of fine-tuning. To resolve these two issues, we introduce a novel approach named Multimodal Entity Linking with Dynamic Modality Selection and Interactive Prompt Learning (DSMIP). First, we design three expert networks that utilize different subsets of modalities tailored to the task and train them individually. Specifically, for the multimodal expert network, we enhance entity and mention feature extraction by updating multimodal prompts and setting up a coupling function to realize the interaction of prompts between modalities. Subsequently, to select the best-suited expert network for each specific sample, we devise a Modality Selection Gating Network to gain the optimal one-hot selection vector by applying a specialized reparameterization technique and a two-stage training process. Experimental results on three public benchmark datasets demonstrate that the proposed DSMIP outperforms all state-of-the-art baselines. The code is released on https://github.com/mayy-seu/DSMIP-code.