Search Results for author:
Found 18 papers, 10 papers with code
Commonsense Prototype for Outdoor Unsupervised 3D Object Detection
The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes.
Sunshine to Rainstorm: Cross-Weather Knowledge Distillation for Robust 3D Object Detection
LiDAR-based 3D object detection models have traditionally struggled under rainy conditions due to the degraded and noisy scanning signals.
DiffCollage: Parallel Generation of Large Content with Diffusion Models
We present DiffCollage, a compositional diffusion model that can generate large content by leveraging diffusion models trained on generating pieces of the large content.
Magic3D: High-Resolution Text-to-3D Content Creation
DreamFusion has recently demonstrated the utility of a pre-trained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF), achieving remarkable text-to-3D synthesis results.
Ranked #2 on
Text to 3D
on T$^3$Bench
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
Therefore, in contrast to existing works, we propose to train an ensemble of text-to-image diffusion models specialized for different synthesis stages.
Ranked #14 on
Text-to-Image Generation
on MS COCO
Multimodal Conditional Image Synthesis with Product-of-Experts GANs
Existing conditional image synthesis frameworks generate images based on user inputs in a single modality, such as text, segmentation, sketch, or style reference.
Implementing an Improved Test of Matrix Rank in Stata
We develop a Stata command, bootranktest, for implementing the matrix rank test of Chen and Fang (2019) in linear instrumental variable regression models.
Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications
The generative adversarial network (GAN) framework has emerged as a powerful tool for various image and video synthesis tasks, allowing the synthesis of visual content in an unconditional or input-conditional manner.
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #4 on
Point Cloud Generation
on ShapeNet Car
Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.
Learning to Evaluate Image Captioning
To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions.
Controllable Video Generation With Sparse Trajectories
Video generation and manipulation is an important yet challenging task in computer vision.
Multimodal Unsupervised Image-to-Image Translation
To translate an image to another domain, we recombine its content code with a random style code sampled from the style space of the target domain.
Multimodal Unsupervised Image-To-Image Translation
Translation
+1
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer.
Stacked Generative Adversarial Networks
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network.
Ranked #11 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks
Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention.
Top-Down Learning for Structured Labeling with Convolutional Pseudoprior
Current practice in convolutional neural networks (CNN) remains largely bottom-up and the role of top-down process in CNN for pattern analysis and visual inference is not very clear.
Learning of Proto-object Representations via Fixations on Low Resolution
Visualizations also show that these features are selective to potential objects in the scene and the responses of these features work well in predicting eye fixations on the images when combined with learned weights.
