53 citations · 54 across the 5 of their papers we have counts for
5 papers
TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge
Shu-Hao Zhang, Wei-Cheng Tang, Chen Wu +5
Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, w…
Visual Position Prompt for MLLM based Visual Grounding
Wei Tang, Yanpeng Sun, Qinying Gu +1
Although Multimodal Large Language Models (MLLMs) excel at various image-related tasks, they encounter challenges in precisely aligning coordinates with spatial information within…
SynthDoc: Bilingual Documents Synthesis for Visual Document Understanding
Chuanghao Ding, Xuejing Liu, Wei Tang +5
This paper introduces SynthDoc, a novel synthetic document generation pipeline designed to enhance Visual Document Understanding (VDU) by generating high-quality, diverse datasets…
Context Disentangling and Prototype Inheriting for Robust Visual Grounding
Wei Tang, Liang Li, Xuejing Liu +3
Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the…
What Large Language Models Bring to Text-rich VQA?
Xuejing Liu, Wei Tang, Xinzhe Ni +4
Text-rich VQA, namely Visual Question Answering based on text recognition in the images, is a cross-modal task that requires both image comprehension and text recognition. In this…