16 citations · 37 across the 15 of their papers we have counts for
17 papers
FlashPrefill V2: Block-Sparse Prefill Attention for Long-Context LLM Serving
Qihang Fan, Huaibo Huang, Zhiying Wu +2
Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-in…
UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification
Qihang Fan, Huaibo Huang, Zhiying Wu +2
As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer context lengths. To improve the infer…
Advancing Vision Transformer with Enhanced Spatial Priors
Qihang Fan, Huaibo Huang, Mingrui Chen +2
In recent years, the Vision Transformer (ViT) has garnered significant attention within the computer vision community. However, the core component of ViT, Self-Attention, lacks exp…
FlashPrefill: Instantaneous Pattern Discovery and Thresholding for Ultra-Fast Long-Context Prefilling
Qihang Fan, Huaibo Huang, Zhiying Wu +3
Long-context modeling is a pivotal capability for Large Language Models, yet the quadratic complexity of attention remains a critical bottleneck, particularly during the compute-in…
Expand and Prune: Maximizing Trajectory Diversity for Effective GRPO in Generative Models
Shiran Ge, Chenyi Huang, Yuang Ai +3
Group Relative Policy Optimization (GRPO) is a powerful technique for aligning generative models, but its effectiveness is bottlenecked by the conflict between large group sizes an…
Rectifying Magnitude Neglect in Linear Attention
Qihang Fan, Huaibo Huang, Yuang Ai +1
As the core operator of Transformers, Softmax Attention exhibits excellent global modeling capabilities. However, its quadratic complexity limits its applicability to vision tasks.…
DiCo: Revitalizing ConvNets for Scalable and Efficient Diffusion Modeling
Yuang Ai, Qihang Fan, Xuefeng Hu +3
Diffusion Transformer (DiT), a promising diffusion model for visual generation, demonstrates impressive performance but incurs significant computational overhead. Intriguingly, ana…
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…
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…
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
Haogeng Liu, Qihang Fan, Tingkai Liu +5
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text…
DeVAn: Dense Video Annotation for Video-Language Models
Tingkai Liu, Yunzhe Tao, Haogeng Liu +5
We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeV…
RMT: Retentive Networks Meet Vision Transformers
Qihang Fan, Huaibo Huang, Mingrui Chen +2
Vision Transformer (ViT) has gained increasing attention in the computer vision community in recent years. However, the core component of ViT, Self-Attention, lacks explicit spatia…
Lightweight Vision Transformer with Bidirectional Interaction
Qihang Fan, Huaibo Huang, Xiaoqiang Zhou +1
Recent advancements in vision backbones have significantly improved their performance by simultaneously modeling images' local and global contexts. However, the bidirectional inter…