Search Results for author:
Found 17 papers, 10 papers with code
Geometry-Informed Neural Operator for Large-Scale 3D PDEs
GINO uses a signed distance function and point-cloud representations of the input shape and neural operators based on graph and Fourier architectures to learn the solution operator.
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning.
Does unsupervised grammar induction need pixels?
Are extralinguistic signals such as image pixels crucial for inducing constituency grammars?
WiCV 2021: The Eighth Women In Computer Vision Workshop
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021.
Language-driven Semantic Segmentation
We present LSeg, a novel model for language-driven semantic image segmentation.
Ranked #1 on
Few-Shot Semantic Segmentation
on FSS-1000
Fixed Neural Network Steganography: Train the images, not the network
Recent attempts at image steganography make use of advances in deep learning to train an encoder-decoder network pair to hide and retrieve secret messages in images.
SITTA: Single Image Texture Translation for Data Augmentation
In this paper, we propose and explore the problem of image translation for data augmentation.
WiCV 2020: The Seventh Women In Computer Vision Workshop
It provides a voice to a minority (female) group in computer vision community and focuses on increasingly the visibility of these researchers, both in academia and industry.
On Feature Normalization and Data Augmentation
The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.
Ranked #17 on
Domain Generalization
on ImageNet-A
Integrated Triaging for Fast Reading Comprehension
This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.
Neural Network Out-of-Distribution Detection for Regression Tasks
Neural network out-of-distribution (OOD) detection aims to identify when a model is unable to generalize to new inputs, either due to covariate shift or anomalous data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Positional Normalization
A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.
FastFusionNet: New State-of-the-Art for DAWNBench SQuAD
In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE).
AOD-Net: All-In-One Dehazing Network
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
Ranked #12 on
Image Dehazing
on SOTS Outdoor
End-to-End United Video Dehazing and Detection
Furthermore, we build an End-to-End United Video Dehazing and Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with a video object detection model.
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
