6 papers · 1 filter
MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
Zhong Wang, Zukang Xu, Xing Hu +1
Vision-Language Models (VLMs) achieve outstanding performance, yet their huge model size severely hinders deployment on edge devices with limited resources. As an efficient model c…
CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model
Houji Wen, Jiangyong Yu, Jun Li +1
Segment Anything Models (SAMs) are extensively used in computer vision for universal image segmentation, but deploying them on resource-constrained devices is challenging due to th…
VAEVQ: Enhancing Discrete Visual Tokenization through Variational Modeling
Sicheng Yang, Xing Hu, Qiang Wu +1
Vector quantization (VQ) transforms continuous image features into discrete representations, providing compressed, tokenized inputs for generative models. However, VQ-based framewo…
MQuant: Unleashing the Inference Potential of Multimodal Large Language Models via Full Static Quantization
JiangYong Yu, Sifan Zhou, Dawei Yang +7
Multimodal large language models (MLLMs) have garnered widespread attention due to their ability to understand multimodal input. However, their large parameter sizes and substantia…
EA-ViT: Efficient Adaptation for Elastic Vision Transformer
Chen Zhu, Wangbo Zhao, Huiwen Zhang +9
Vision Transformers (ViTs) have emerged as a foundational model in computer vision, excelling in generalization and adaptation to downstream tasks. However, deploying ViTs to suppo…
Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder
Sifan Zhou, Zhihang Yuan, Dawei Yang +5
Real-time and high-performance 3D object detection plays a critical role in autonomous driving and robotics. Recent pillar-based 3D object detectors have gained significant attenti…