55 citations · 121 across the 20 of their papers we have counts for
21 papers
A-SelecT: Automatic Timestep Selection for Diffusion Transformer Representation Learning
Changyu Liu, James Chenhao Liang, Wenhao Yang +6
Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation l…
All You Need is One: Capsule Prompt Tuning with a Single Vector
Yiyang Liu, James C. Liang, Heng Fan +7
Prompt-based learning has emerged as a parameter-efficient finetuning (PEFT) approach to facilitate Large Language Model (LLM) adaptation to downstream tasks by conditioning genera…
MPT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Taowen Wang, Yiyang Liu, James Chenhao Liang +11
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization cap…
ProMotion: Prototypes As Motion Learners
Yawen Lu, Dongfang Liu, Qifan Wang +6
In this work, we introduce ProMotion, a unified prototypical framework engineered to model fundamental motion tasks. ProMotion offers a range of compelling attributes that set it a…
SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving
Yiming Cui, Cheng Han, Dongfang Liu
Visual-based perception is the key module for autonomous driving. Among those visual perception tasks, video object detection is a primary yet challenging one because of feature de…
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?
Cheng Han, Qifan Wang, Yiming Cui +4
As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning (VPT) as a parameter-efficient transfer learning technique has gained attention due to its su…