Search Results for author: Yunzhu Li

Found 14 papers, 7 papers with code

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing

no code implementations9 Sep 2021 Qiang Zhang, Yunzhu Li, Yiyue Luo, Wan Shou, Michael Foshey, Junchi Yan, Joshua B. Tenenbaum, Wojciech Matusik, Antonio Torralba

This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.

Contrastive Learning Imitation Learning

Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals

no code implementations CVPR 2021 Yiyue Luo, Yunzhu Li, Michael Foshey, Wan Shou, Pratyusha Sharma, Tomas Palacios, Antonio Torralba, Wojciech Matusik

In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input.

3D Human Pose Estimation Multi-Person Pose Estimation

Causal Discovery in Physical Systems from Videos

1 code implementation NeurIPS 2020 Yunzhu Li, Antonio Torralba, Animashree Anandkumar, Dieter Fox, Animesh Garg

We assume access to different configurations and environmental conditions, i. e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions.

Causal Discovery

Learning Physical Graph Representations from Visual Scenes

1 code implementation NeurIPS 2020 Daniel M. Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins

To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.

Scene Segmentation

Visual Grounding of Learned Physical Models

no code implementations ICML 2020 Yunzhu Li, Toru Lin, Kexin Yi, Daniel M. Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba

The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.

Visual Grounding

Learning Compositional Koopman Operators for Model-Based Control

no code implementations ICLR 2020 Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, Antonio Torralba

Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis.

CLEVRER: CoLlision Events for Video REpresentation and Reasoning

3 code implementations ICLR 2020 Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum

While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations.

Visual Reasoning

Connecting Touch and Vision via Cross-Modal Prediction

1 code implementation CVPR 2019 Yunzhu Li, Jun-Yan Zhu, Russ Tedrake, Antonio Torralba

To connect vision and touch, we introduce new tasks of synthesizing plausible tactile signals from visual inputs as well as imagining how we interact with objects given tactile data as input.

Propagation Networks for Model-Based Control Under Partial Observation

1 code implementation28 Sep 2018 Yunzhu Li, Jiajun Wu, Jun-Yan Zhu, Joshua B. Tenenbaum, Antonio Torralba, Russ Tedrake

There has been an increasing interest in learning dynamics simulators for model-based control.

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

4 code implementations NeurIPS 2017 Yunzhu Li, Jiaming Song, Stefano Ermon

The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.

Imitation Learning

Skin Cancer Detection and Tracking using Data Synthesis and Deep Learning

no code implementations4 Dec 2016 Yunzhu Li, Andre Esteva, Brett Kuprel, Rob Novoa, Justin Ko, Sebastian Thrun

Dense object detection and temporal tracking are needed across applications domains ranging from people-tracking to analysis of satellite imagery over time.

Dense Object Detection

Face Detection with End-to-End Integration of a ConvNet and a 3D Model

1 code implementation2 Jun 2016 Yunzhu Li, Benyuan Sun, Tianfu Wu, Yizhou Wang

The proposed method addresses two issues in adapting state- of-the-art generic object detection ConvNets (e. g., faster R-CNN) for face detection: (i) One is to eliminate the heuristic design of prede- fined anchor boxes in the region proposals network (RPN) by exploit- ing a 3D mean face model.

Face Detection Face Model +2

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