activity
20152023
most citedTest-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

112 citations · 559 across the 26 of their papers we have counts for

collaborators
Showing 2019Show all

6 papers · 1 filter

cs.RO2019★ 1 cited

Motion Reasoning for Goal-Based Imitation Learning

De-An Huang, Yu-Wei Chao, Chris Paxton +5

We address goal-based imitation learning, where the aim is to output the symbolic goal from a third-person video demonstration. This enables the robot to plan for execution and rep…

cs.AI2019★ 9 cited

Regression Planning Networks

Danfei Xu, Roberto Martín-Martín, De-An Huang +3

Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accura…

cs.CV2019★ 5 cited

Imitation Learning for Human Pose Prediction

Borui Wang, Ehsan Adeli, Hsu-kuang Chiu +2

Modeling and prediction of human motion dynamics has long been a challenging problem in computer vision, and most existing methods rely on the end-to-end supervised training of var…

cs.AI2019★ 12 cited

Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

De-An Huang, Danfei Xu, Yuke Zhu +4

We address one-shot imitation learning, where the goal is to execute a previously unseen task based on a single demonstration. While there has been exciting progress in this direct…

cs.CV2019★ 10 cited

Procedure Planning in Instructional Videos

Chien-Yi Chang, De-An Huang, Danfei Xu +3

In this paper, we study the problem of procedure planning in instructional videos, which can be seen as a step towards enabling autonomous agents to plan for complex tasks in every…

cs.CV2019★ 10 cited

D3TW: Discriminative Differentiable Dynamic Time Warping for Weakly Supervised Action Alignment and Segmentation

Chien-Yi Chang, De-An Huang, Yanan Sui +2

We address weakly supervised action alignment and segmentation in videos, where only the order of occurring actions is available during training. We propose Discriminative Differen…