GitHub

SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference

In vision-language models (VLMs), visual tokens usually consume a significant amount of computational overhead, despite their sparser information density compared to text tokens. To address this, ...
GitHub

PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction (CVPR 2025)

[2025.3.6] [Update🔥] Please place any models that need to be compatible with pdrop in this issue [2025.2.27] 🚀 Our paper has been accepted by CVPR 2025!!! [2024.10.24] 🚀 Our paper has been featured ...
IEEE

NVILA: Efficient Frontier Visual Language Models

Abstract: Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of ...
Communications of the ACM

The LLVM Compiler Infrastructure

LLVM powers the core development tools, operating systems, and most applications at Apple Computer, where it long ago ...
IEEE

Mitigating Object Hallucination in Large Vision-Language Models via Visual Attention Direct Preference Optimization

Abstract: Large Vision-Language Models (LVLMs) suffer from severe object hallucinations, leading them to frequently generate outputs that do not correspond to the image content, significantly reducing ...