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.

Segmenting vein from leaf images is challenging because of the strong expansibility, high connectivity and irregular distribution of vein patterns. Although several recent attempts have been made to apply CNNs to leaf vein segmentation, they are weak to capture global contexts of lower-order veins and fine-grained features about higher-order veins due to the continuous stacking of convolution and pooling operations. Inspired by the great successes of transformers on semantic image segmentation, we for the first time develop a novel transformer based model, named LV-SegFormer for leaf vein segmentation. The LV-SegFormer leverages the powerful global modeling capability of transformer to generate advanced representations. The multi-stage alignment module and the hybrid decoder are designed to incorporate contexts from different stages and recover fine-grained features for performance boosting. Extensive experiments are conducted on two challenging benchmark datasets and the results demonstrate that the proposed LV-SegFormer achieves higher performance over the state-of-the-arts.