most citedModelScope Text-to-Video Technical Report

47 citations · 160 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV202322 cited

I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models

Shiwei Zhang, Jiayu Wang, Yingya Zhang +6

Video synthesis has recently made remarkable strides benefiting from the rapid development of diffusion models. However, it still encounters challenges in terms of semantic accurac…

cs.CV20232 cited

Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer Learning

Zhiwu Qing, Shiwei Zhang, Ziyuan Huang +4

Recently, large-scale pre-trained language-image models like CLIP have shown extraordinary capabilities for understanding spatial contents, but naively transferring such models to…

cs.CV2023

Towards Real-World Visual Tracking with Temporal Contexts

Ziang Cao, Ziyuan Huang, Liang Pan +3

Visual tracking has made significant improvements in the past few decades. Most existing state-of-the-art trackers 1) merely aim for performance in ideal conditions while overlooki…

cs.CV20232 cited

RLIPv2: Fast Scaling of Relational Language-Image Pre-training

Hangjie Yuan, Shiwei Zhang, Xiang Wang +7

Relational Language-Image Pre-training (RLIP) aims to align vision representations with relational texts, thereby advancing the capability of relational reasoning in computer visio…

cs.CV202347 cited

ModelScope Text-to-Video Technical Report

Jiuniu Wang, Hangjie Yuan, Dayou Chen +3

This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-t…

cs.CV20237 cited

Temporally-Adaptive Models for Efficient Video Understanding

Ziyuan Huang, Shiwei Zhang, Liang Pan +4

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in diffe…