most citedTowards Accurate Post-Training Quantization for Vision Transformer

66 citations · 83 across the 7 of their papers we have counts for

collaborators
Showing cs.CVShow all

7 papers · 1 filter

cs.CV202312 cited

MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices

Xiangxiang Chu, Limeng Qiao, Xinyang Lin +8

We present MobileVLM, a competent multimodal vision language model (MMVLM) targeted to run on mobile devices. It is an amalgamation of a myriad of architectural designs and techniq…

cs.CV2023

3rd Place Solution for PVUW Challenge 2023: Video Panoptic Segmentation

Jinming Su, Wangwang Yang, Junfeng Luo +1

In order to deal with the task of video panoptic segmentation in the wild, we propose a robust integrated video panoptic segmentation solution. In our solution, we regard the video…

cs.CV202366 cited

Towards Accurate Post-Training Quantization for Vision Transformer

Yifu Ding, Haotong Qin, Qinghua Yan +4

Vision transformer emerges as a potential architecture for vision tasks. However, the intense computation and non-negligible delay hinder its application in the real world. As a wi…

cs.CV20231 cited

3D Colored Shape Reconstruction from a Single RGB Image through Diffusion

Bo Li, Xiaolin Wei, Fengwei Chen +1

We propose a novel 3d colored shape reconstruction method from a single RGB image through diffusion model. Diffusion models have shown great development potentials for high-quality…

cs.CV2022

PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding

Zihan Ding, Zi-han Ding, Tianrui Hui +4

Panoptic Narrative Grounding (PNG) is an emerging task whose goal is to segment visual objects of things and stuff categories described by dense narrative captions of a still image…

cs.CV20223 cited

MT-Net Submission to the Waymo 3D Detection Leaderboard

Shaoxiang Chen, Zequn Jie, Xiaolin Wei +1

In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard. Our network is based on the Centerpoint architecture, but with significant improvements…