Search Results for author: Daiqing Li

Found 12 papers, 2 papers with code

RadImageGAN -- A Multi-modal Dataset-Scale Generative AI for Medical Imaging

no code implementations10 Dec 2023 Zelong Liu, Alexander Zhou, Arnold Yang, Alara Yilmaz, Maxwell Yoo, Mikey Sullivan, Catherine Zhang, James Grant, Daiqing Li, Zahi A. Fayad, Sean Huver, Timothy Deyer, Xueyan Mei

We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning.

Segmentation

DreamTeacher: Pretraining Image Backbones with Deep Generative Models

no code implementations ICCV 2023 Daiqing Li, Huan Ling, Amlan Kar, David Acuna, Seung Wook Kim, Karsten Kreis, Antonio Torralba, Sanja Fidler

In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.

Knowledge Distillation Representation Learning

NeuralField-LDM: Scene Generation with Hierarchical Latent Diffusion Models

no code implementations CVPR 2023 Seung Wook Kim, Bradley Brown, Kangxue Yin, Karsten Kreis, Katja Schwarz, Daiqing Li, Robin Rombach, Antonio Torralba, Sanja Fidler

We first train a scene auto-encoder to express a set of image and pose pairs as a neural field, represented as density and feature voxel grids that can be projected to produce novel views of the scene.

Scene Generation

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

3 code implementations22 Sep 2022 Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, Sanja Fidler

As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident.

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.

Segmentation Semantic Segmentation +1

Fed-Sim: Federated Simulation for Medical Imaging

no code implementations1 Sep 2020 Daiqing Li, Amlan Kar, Nishant Ravikumar, Alejandro F. Frangi, Sanja Fidler

Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.

Federated Learning

Neural Turtle Graphics for Modeling City Road Layouts

no code implementations ICCV 2019 Hang Chu, Daiqing Li, David Acuna, Amlan Kar, Maria Shugrina, Xinkai Wei, Ming-Yu Liu, Antonio Torralba, Sanja Fidler

We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.

A Face-to-Face Neural Conversation Model

no code implementations CVPR 2018 Hang Chu, Daiqing Li, Sanja Fidler

The decoder consists of two layers, where the lower layer aims at generating the verbal response and coarse facial expressions, while the second layer fills in the subtle gestures, making the generated output more smooth and natural.

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