Search Results for author: De-An Huang

Found 27 papers, 3 papers with code

Pre-Trained Language Models for Interactive Decision-Making

no code implementations3 Feb 2022 Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, Jacob Andreas, Igor Mordatch, Antonio Torralba, Yuke Zhu

Together, these results suggest that language modeling induces representations that are useful for modeling not just language, but also goals and plans; these representations can aid learning and generalization even outside of language processing.

Decision Making Imitation Learning +1

Scaling Fair Learning to Hundreds of Intersectional Groups

no code implementations29 Sep 2021 Eric Zhao, De-An Huang, Hao liu, Zhiding Yu, Anqi Liu, Olga Russakovsky, Anima Anandkumar

In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.

Fairness Knowledge Distillation

Auditing AI models for Verified Deployment under Semantic Specifications

no code implementations25 Sep 2021 Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg

We enable such unit tests through variations in a semantically-interpretable latent space of a generative model.

Face Recognition

SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies

no code implementations17 Jun 2021 Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, Anima Anandkumar

A student network then learns to mimic the expert policy by supervised learning with strong augmentations, making its representation more robust against visual variations compared to the expert.

Autonomous Driving Image Augmentation +1

Transferable Unsupervised Robust Representation Learning

no code implementations1 Jan 2021 De-An Huang, Zhiding Yu, Anima Anandkumar

We upend this view and show that URRL improves both the natural accuracy of unsupervised representation learning and its robustness to corruptions and adversarial noise.

Data Augmentation Representation Learning +1

Motion Reasoning for Goal-Based Imitation Learning

no code implementations13 Nov 2019 De-An Huang, Yu-Wei Chao, Chris Paxton, Xinke Deng, Li Fei-Fei, Juan Carlos Niebles, Animesh Garg, Dieter Fox

We further show that by using the automatically inferred goal from the video demonstration, our robot is able to reproduce the same task in a real kitchen environment.

Imitation Learning Motion Planning

Regression Planning Networks

1 code implementation NeurIPS 2019 Danfei Xu, Roberto Martín-Martín, De-An Huang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

Recent learning-to-plan methods have shown promising results on planning directly from observation space.

Imitation Learning for Human Pose Prediction

no code implementations ICCV 2019 Borui Wang, Ehsan Adeli, Hsu-kuang Chiu, De-An Huang, Juan Carlos Niebles

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 various architectures of recurrent neural networks.

Human Pose Forecasting Imitation Learning +2

Continuous Relaxation of Symbolic Planner for One-Shot Imitation Learning

no code implementations16 Aug 2019 De-An Huang, Danfei Xu, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei, Juan Carlos Niebles

The key technical challenge is that the symbol grounding is prone to error with limited training data and leads to subsequent symbolic planning failures.

Imitation Learning

Procedure Planning in Instructional Videos

no code implementations ECCV 2020 Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, Juan Carlos Niebles

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 everyday settings such as cooking.

Action-Agnostic Human Pose Forecasting

1 code implementation23 Oct 2018 Hsu-kuang Chiu, Ehsan Adeli, Borui Wang, De-An Huang, Juan Carlos Niebles

In this paper, we propose a new action-agnostic method for short- and long-term human pose forecasting.

Human Dynamics Human Pose Forecasting

Neural Graph Matching Networks for Fewshot 3D Action Recognition

no code implementations ECCV 2018 Michelle Guo, Edward Chou, De-An Huang, Shuran Song, Serena Yeung, Li Fei-Fei

We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples.

Few-Shot Learning Graph Matching +1

Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

no code implementations ECCV 2018 Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles

A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions.

Video Retrieval

Neural Task Graphs: Generalizing to Unseen Tasks from a Single Video Demonstration

no code implementations CVPR 2019 De-An Huang, Suraj Nair, Danfei Xu, Yuke Zhu, Animesh Garg, Li Fei-Fei, Silvio Savarese, Juan Carlos Niebles

We hypothesize that to successfully generalize to unseen complex tasks from a single video demonstration, it is necessary to explicitly incorporate the compositional structure of the tasks into the model.

Finding "It": Weakly-Supervised Reference-Aware Visual Grounding in Instructional Videos

no code implementations CVPR 2018 De-An Huang, Shyamal Buch, Lucio Dery, Animesh Garg, Li Fei-Fei, Juan Carlos Niebles

In this work, we propose to tackle this new task with a weakly-supervised framework for reference-aware visual grounding in instructional videos, where only the temporal alignment between the transcription and the video segment are available for supervision.

Multiple Instance Learning Visual Grounding

Visual Forecasting by Imitating Dynamics in Natural Sequences

no code implementations ICCV 2017 Kuo-Hao Zeng, William B. Shen, De-An Huang, Min Sun, Juan Carlos Niebles

This allows us to apply IRL at scale and directly imitate the dynamics in high-dimensional continuous visual sequences from the raw pixel values.

Action Anticipation Frame

Unsupervised Visual-Linguistic Reference Resolution in Instructional Videos

no code implementations CVPR 2017 De-An Huang, Joseph J. Lim, Li Fei-Fei, Juan Carlos Niebles

We propose an unsupervised method for reference resolution in instructional videos, where the goal is to temporally link an entity (e. g., "dressing") to the action (e. g., "mix yogurt") that produced it.

Referring Expression

Unsupervised Learning of Long-Term Motion Dynamics for Videos

no code implementations CVPR 2017 Zelun Luo, Boya Peng, De-An Huang, Alexandre Alahi, Li Fei-Fei

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos.

Representation Learning

Connectionist Temporal Modeling for Weakly Supervised Action Labeling

no code implementations28 Jul 2016 De-An Huang, Li Fei-Fei, Juan Carlos Niebles

We propose a weakly-supervised framework for action labeling in video, where only the order of occurring actions is required during training time.

Frame General Classification

Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

no code implementations CVPR 2017 Wei-Chiu Ma, De-An Huang, Namhoon Lee, Kris M. Kitani

We develop predictive models of pedestrian dynamics by encoding the coupled nature of multi-pedestrian interaction using game theory, and deep learning-based visual analysis to estimate person-specific behavior parameters.

Decision Making

Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis

no code implementations19 Nov 2015 Yanwei Fu, De-An Huang, Leonid Sigal

Collecting datasets in this way, however, requires robust and efficient ways for detecting and excluding outliers that are common and prevalent.

Classification General Classification +2

How Do We Use Our Hands? Discovering a Diverse Set of Common Grasps

no code implementations CVPR 2015 De-An Huang, Minghuang Ma, Wei-Chiu Ma, Kris M. Kitani

Furthermore, we develop a hierarchical extension to the DPP clustering algorithm and show that it can be used to discover appearance-based grasp taxonomies.

Online Clustering

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