37 citations · 59 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
Generation Models Know Space: Unleashing Implicit 3D Priors for Scene Understanding
Xianjin Wu, Dingkang Liang, Tianrui Feng +5
While Multimodal Large Language Models demonstrate impressive semantic capabilities, they often suffer from spatial blindness, struggling with fine-grained geometric reasoning and…
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…
Demystifying CLIP Data
Hu Xu, Saining Xie, Xiaoqing Ellen Tan +7
Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced research and applications in computer vision, fueling modern recognition systems and generative mode…