research
∙
09/14/2023
Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models
Text-to-image diffusion models understand spatial relationship between o...
share
research
∙
09/13/2023
Generalizable Neural Fields as Partially Observed Neural Processes
Neural fields, which represent signals as a function parameterized by a ...
research
∙
06/15/2023
LOVM: Language-Only Vision Model Selection
Pre-trained multi-modal vision-language models (VLMs) are becoming incre...
research
∙
05/27/2023
Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models
Language models have been shown to exhibit positive scaling, where perfo...
research
∙
05/25/2023
ZeroAvatar: Zero-shot 3D Avatar Generation from a Single Image
Recent advancements in text-to-image generation have enabled significant...
research
∙
05/11/2023
Hyperbolic Deep Learning in Computer Vision: A Survey
Deep representation learning is a ubiquitous part of modern computer vis...
research
∙
04/02/2023
Video Pretraining Advances 3D Deep Learning on Chest CT Tasks
Pretraining on large natural image classification datasets such as Image...
research
∙
03/16/2023
Diffusion-HPC: Generating Synthetic Images with Realistic Humans
Recent text-to-image generative models have exhibited remarkable abiliti...
research
∙
02/08/2023
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation
Given the prevalence of 3D medical imaging technologies such as MRI and ...
research
∙
02/08/2023
Diagnosing and Rectifying Vision Models using Language
Recent multi-modal contrastive learning models have demonstrated the abi...
research
∙
12/28/2022
NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action
The task of reconstructing 3D human motion has wideranging applications....
research
∙
12/02/2022
PROB: Probabilistic Objectness for Open World Object Detection
Open World Object Detection (OWOD) is a new and challenging computer vis...
research
∙
07/20/2022
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning (ML) research has generally focused on models, while th...
research
∙
07/07/2022
Adaptation of Surgical Activity Recognition Models Across Operating Rooms
Automatic surgical activity recognition enables more intelligent surgica...
research
∙
06/21/2022
Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery
The ability to perceive 3D human bodies from a single image has a multit...
research
∙
03/03/2022
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
We present modality gap, an intriguing geometric phenomenon of the repre...
research
∙
12/14/2021
A real-time spatiotemporal AI model analyzes skill in open surgical videos
Open procedures represent the dominant form of surgery worldwide. Artifi...
research
∙
11/20/2021
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation
We consider the task of semi-supervised video object segmentation (VOS)....
research
∙
07/08/2021
Staying in Shape: Learning Invariant Shape Representations using Contrastive Learning
Creating representations of shapes that are invari-ant to isometric or a...
research
∙
04/03/2021
DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images
In the biomedical domain, there is an abundance of dense, complex data w...
research
∙
04/02/2021
Unsupervised Discovery of the Long-Tail in Instance Segmentation Using Hierarchical Self-Supervision
Instance segmentation is an active topic in computer vision that is usua...
research
∙
12/15/2020
Personalized Federated Learning with First Order Model Optimization
While federated learning traditionally aims to train a single global mod...
research
∙
12/13/2020
Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery
Open, or non-laparoscopic surgery, represents the vast majority of all o...
research
∙
12/03/2020
Learning Hyperbolic Representations for Unsupervised 3D Segmentation
There exists a need for unsupervised 3D segmentation on complex volumetr...
research
∙
12/02/2020
Holistic 3D Human and Scene Mesh Estimation from Single View Images
The 3D world limits the human body pose and the human body pose conveys ...
research
∙
11/12/2020
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
We study the problem of medical symptoms recognition from patient text, ...
research
∙
02/23/2020
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data
In mobile health (mHealth), reinforcement learning algorithms that adapt...
research
∙
12/20/2019
Adversarial Representation Active Learning
Active learning aims to develop label-efficient algorithms by querying t...
research
∙
11/25/2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Homomorphic encryption enables arbitrary computation over data while it ...
research
∙
02/24/2018
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
Five billion people in the world lack access to quality surgical care. S...
research
∙
08/01/2017
Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance
One in twenty-five patients admitted to a hospital will suffer from a ho...
research
∙
06/09/2017
Learning to Learn from Noisy Web Videos
Understanding the simultaneously very diverse and intricately fine-grain...
research
∙
03/23/2016
Towards Viewpoint Invariant 3D Human Pose Estimation
We propose a viewpoint invariant model for 3D human pose estimation from...
research
∙
11/22/2015
End-to-end Learning of Action Detection from Frame Glimpses in Videos
In this work we introduce a fully end-to-end approach for action detecti...
research
∙
07/21/2015
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Every moment counts in action recognition. A comprehensive understanding...
research
∙
06/23/2014