Search Results for author: Huan Ling

Found 13 papers, 5 papers with code

Structural Realization with GGNNs

no code implementations NAACL (TextGraphs) 2021 Jinman Zhao, Gerald Penn, Huan Ling

In this paper, we define an abstract task called structural realization that generates words given a prefix of words and a partial representation of a parse tree.

Language Modelling

BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations

no code implementations CVPR 2022 Daiqing Li, Huan Ling, Seung Wook Kim, Karsten Kreis, Adela Barriuso, Sanja Fidler, Antonio Torralba

By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator.

EditGAN: High-Precision Semantic Image Editing

1 code implementation NeurIPS 2021 Huan Ling, Karsten Kreis, Daiqing Li, Seung Wook Kim, Antonio Torralba, Sanja Fidler

EditGAN builds on a GAN framework that jointly models images and their semantic segmentations, requiring only a handful of labeled examples, making it a scalable tool for editing.

Semantic Segmentation

DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort

1 code implementation CVPR 2021 Yuxuan Zhang, Huan Ling, Jun Gao, Kangxue Yin, Jean-Francois Lafleche, Adela Barriuso, Antonio Torralba, Sanja Fidler

To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts.

Semantic Segmentation

Variational Amodal Object Completion

no code implementations NeurIPS 2020 Huan Ling, David Acuna, Karsten Kreis, Seung Wook Kim, Sanja Fidler

In images of complex scenes, objects are often occluding each other which makes perception tasks such as object detection and tracking, or robotic control tasks such as planning, challenging.

object-detection Object Detection

Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering

no code implementations ICLR 2021 Yuxuan Zhang, Wenzheng Chen, Huan Ling, Jun Gao, Yinan Zhang, Antonio Torralba, Sanja Fidler

Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.

Neural Rendering

ScribbleBox: Interactive Annotation Framework for Video Object Segmentation

no code implementations ECCV 2020 Bo-Wen Chen, Huan Ling, Xiaohui Zeng, Gao Jun, Ziyue Xu, Sanja Fidler

Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy.

Semantic Segmentation Video Object Segmentation +1

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

1 code implementation NeurIPS 2019 Wenzheng Chen, Jun Gao, Huan Ling, Edward J. Smith, Jaakko Lehtinen, Alec Jacobson, Sanja Fidler

Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering.

Single-View 3D Reconstruction

Fast Interactive Object Annotation with Curve-GCN

2 code implementations CVPR 2019 Huan Ling, Jun Gao, Amlan Kar, Wenzheng Chen, Sanja Fidler

Our model runs at 29. 3ms in automatic, and 2. 6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.

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