36 citations · 56 across the 6 of their papers we have counts for
6 papers
When Parameter-efficient Tuning Meets General-purpose Vision-language Models
Yihang Zhai, Haixin Wang, Jianlong Chang +4
Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate mul…
PastNet: Introducing Physical Inductive Biases for Spatio-temporal Video Prediction
Hao Wu, Fan Xu, Chong Chen +3
In this paper, we investigate the challenge of spatio-temporal video prediction task, which involves generating future video frames based on historical spatio-temporal observation…
Parameter-efficient Tuning of Large-scale Multimodal Foundation Model
Haixin Wang, Xinlong Yang, Jianlong Chang +5
Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has gained immense popularity across different subfields of Artificial Intelligence. The c…
Visual Tuning
Bruce X. B. Yu, Jianlong Chang, Haixin Wang +9
Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation models, visu…
LION: Implicit Vision Prompt Tuning
Haixin Wang, Jianlong Chang, Xiao Luo +3
Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs. Recently, vision prompt learning has pr…
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Zhiyang Chen, Yousong Zhu, Zhaowen Li +8
Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensi…