arXiv Machine Learning
Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data
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arXiv Machine Learning
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Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification
The paper introduces an unsupervised framework that merges manifold learning with rank‑based interpretable graph embeddings to address the Geometric and Interpretability Gaps in visual representation learning. By first analyzing contextual information on the dataset manifold and then producing sparse, self‑explainable embeddings, the method achieves dimensionality reduction while preserving or improving performance in image retrieval and semi‑supervised Graph Convolutional Network classification. Experiments across varied datasets confirm that these context‑aware representations maintain high downstream effectiveness.
arXiv Computer Vision
3d ago
Aggregating Neighbor Embedding Projection and Rank-Based Manifold Learning for Image Retrieval
arXiv:2609.01963v1 Announce Type: new Abstract: Content-based image retrieval (CBIR) has advanced significantly with deep learning, yet effectively ranking similar images remains challenging, particu...
arXiv AI
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Integrating Large Language Models and Graph Convolutional Networks for Semi-Supervised Image Classification
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 Machine Learning
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Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
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arXiv AI
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RADD: Retrieval-Augmented Discrete Diffusion for Multi-Modal Knowledge Graph Completion
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arXiv AI
2d ago
CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation
CORE improves compositional reasoning in multimodal language models by distilling a cross‑attentive reranker’s fine‑grained judgments into the embedding model. It generates candidate lists across five compositional matching levels and trains with a Rank‑KL objective to replicate the reranker’s ranking. Experiments on COLA, SUGARCREPE++, and NEGBENCH show CORE‑RERANKER‑8B outperforms Jina‑Reranker by 10.7 points, while CORE‑EMBED‑8B achieves the best overall average among evaluated embeddings, with gains also transferring to the MCMR benchmark without harming COCO or Flickr30K retrieval.