16 citations · 24 across the 23 of their papers we have counts for
8 papers · 1 filter
Breaking the Low-Rank Dilemma of Linear Attention
Qihang Fan, Huaibo Huang, Ran He
The Softmax attention mechanism in Transformer models is notoriously computationally expensive, particularly due to its quadratic complexity, posing significant challenges in visio…
InfiMM-WebMath-40B: Advancing Multimodal Pre-Training for Enhanced Mathematical Reasoning
Xiaotian Han, Yiren Jian, Xuefeng Hu +8
Pre-training on large-scale, high-quality datasets is crucial for enhancing the reasoning capabilities of Large Language Models (LLMs), especially in specialized domains such as ma…
Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model
Haogeng Liu, Quanzeng You, Xiaotian Han +4
In the realm of Multimodal Large Language Models (MLLMs), vision-language connector plays a crucial role to link the pre-trained vision encoders with Large Language Models (LLMs).…
Semantic Equitable Clustering: A Simple and Effective Strategy for Clustering Vision Tokens
Qihang Fan, Huaibo Huang, Mingrui Chen +1
The Vision Transformer (ViT) has gained prominence for its superior relational modeling prowess. However, its global attention mechanism's quadratic complexity poses substantial co…
Vision Transformer with Sparse Scan Prior
Yuguang Zhang, Qihang Fan, Huaibo Huang
In recent years, Transformers have achieved remarkable progress in computer vision tasks. However, their global modeling often comes with substantial computational overhead, in sta…
Band-Attention Modulation Network for Robust Face Forgery Detection
Zhida Zhang, Jie Cao, Wenkui Yang +4
Face forgery detection faces critical challenges in generalizing to unseen manipulation techniques and remaining robust under image compression, which often obscures subtle artifac…
ViTAR: Vision Transformer with Any Resolution
Qihang Fan, Quanzeng You, Xiaotian Han +5
This paper tackles a significant challenge faced by Vision Transformers (ViTs): their constrained scalability across different image resolutions. Typically, ViTs experience a perfo…
InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding
Haogeng Liu, Quanzeng You, Xiaotian Han +7
Multimodal Large Language Models (MLLMs) have experienced significant advancements recently. Nevertheless, challenges persist in the accurate recognition and comprehension of intri…