3 papers
cs.CV2026
P-MTP: Efficient Document Parsing via Multi-Token Prediction with Progressive Depth Scaling
Le Xiang, Chenxi Zhai, Shu Wei +5
Vision-Language Models (VLMs) have revolutionized document parsing by enabling end-to-end mapping from images to structured text, imposing a significant latency bottleneck, particu…
cs.CV2026
Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval
Guosheng Zhang, Linkai Liu, Keyao Wang +3
Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus on sample-level objectives…
cs.CV2024
Altogether: Image Captioning via Re-aligning Alt-text
Hu Xu, Po-Yao Huang, Xiaoqing Ellen Tan +10
This paper focuses on creating synthetic data to improve the quality of image captions. Existing works typically have two shortcomings. First, they caption images from scratch, ign…