activity
20232025
most citedTowards Few-Shot Adaptation of Foundation Models via Multitask Finetuning

1 citations · 1 across the 3 of their papers we have counts for

collaborators

5 papers

eess.IV2025

BodyWave: Egocentric Body Tracking using mmWave Radars on an MR Headset

Yin Li, Sean Korphi, Sam Shiu +3

Egocentric body tracking, also known as inside-out body tracking (IOBT), is an essential technology for applications like gesture control and codec avatar in mixed reality (MR), in…

eess.IV2025

TUS-REC2024: A Challenge to Reconstruct 3D Freehand Ultrasound Without External Tracker

Qi Li, Shaheer U. Saeed, Yuliang Huang +24

Trackerless freehand ultrasound reconstruction aims to reconstruct 3D volumes from sequences of 2D ultrasound images without relying on external tracking systems. By eliminating th…

cs.CV2024

SnAG: Scalable and Accurate Video Grounding

Fangzhou Mu, Sicheng Mo, Yin Li

Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalabil…

cs.LG20241 cited

Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning

Zhuoyan Xu, Zhenmei Shi, Junyi Wei +3

Foundation models have emerged as a powerful tool for many AI problems. Despite the tremendous success of foundation models, effective adaptation to new tasks, particularly those w…

cs.CV2023

FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition

Sicheng Mo, Fangzhou Mu, Kuan Heng Lin +4

Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However, auxiliary modules have to be trained for each type…