arXiv:2606. 01747v1 Announce Type: cross Abstract: Through digital humanities research and scale-up historical data analysis, a significant amount of traditional historical text is converted into structured knowledge graphs.
arXiv:2607. 09104v1 Announce Type: cross Abstract: While the growing availability of image data has driven significant advances, labeling datasets remains costly and time-consuming.
arXiv:2607. 05841v1 Announce Type: cross Abstract: Structured representation can characterize semantic objects and relationships in images.
arXiv:2511.04473v3 Announce Type: replace Abstract: Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various...
arXiv:2610.14531v2 Announce Type: replace Abstract: Chinese historical documents are invaluable cultural treasures, but their complexity poses significant comprehension challenges for non-specialists...
The paper introduces ExpArt-KG, a knowledge graph tailored to the artwork domain, and a retrieval‑augmented generation framework that alternates between generating answers and retrieving relevant facts from the graph. By using a correctness judgment to guide the search, the method efficiently gathers the necessary factual information, improving the detail of image explanations while reducing external knowledge retrieval costs. Experimental results demonstrate that the approach maintains generation quality comparable to fixed‑iteration methods.
arXiv:2608. 05833v1 Announce Type: new Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images.
arXiv:2606. 14047v1 Announce Type: cross Abstract: Long-context language modeling requires not only extending context windows but maintaining coherent understanding of entity states and relationships across thousands of tokens -- a challenge that semantic similarity alone cannot address.
The paper introduces ReT-2, a unified retrieval model that handles multimodal queries containing both images and text and searches across multimodal document collections. It employs a recurrent Transformer architecture with LSTM-inspired gating to integrate information across layers and modalities, capturing fine-grained visual and textual details. Evaluations on M2KR and M-BEIR benchmarks show state‑of‑the‑art performance, faster inference, and lower memory usage, and the model also boosts downstream tasks in retrieval‑augmented generation pipelines.
arXiv:2505.03654v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-spec...
The paper introduces PRISm, an image-to-image retrieval framework that prioritizes important elements in scene graphs for semantic image search. PRISm uses an Importance Prediction Module, trained with caption-derived relevance, to prune irrelevant objects and relations, and an Edge-Aware Contextual GNN to fuse the refined graph with global visual features into a single embedding. Experiments on PSG and Flickr show that PRISm outperforms strong baselines in MAP@1 and top‑1 metrics while adding only 33 ms per query beyond scene‑graph generation.
arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.
arXiv:2607. 24799v1 Announce Type: cross Abstract: Large Language Models tend to hallucinate when answering domain-specific ques tions from scientific documents without prior fine-tuning.