Search Results for author:
Found 18 papers, 7 papers with code
DreamTeacher: Pretraining Image Backbones with Deep Generative Models
In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones.
EPIC-KITCHENS VISOR Benchmark: VIdeo Segmentations and Object Relations
VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets.
Causal Scene BERT: Improving object detection by searching for challenging groups of data
Our main contribution is a pseudo-automatic method to discover such groups in foresight by performing causal interventions on simulated scenes.
ATISS: Autoregressive Transformers for Indoor Scene Synthesis
The ability to synthesize realistic and diverse indoor furniture layouts automatically or based on partial input, unlocks many applications, from better interactive 3D tools to data synthesis for training and simulation.
Ranked #3 on
Indoor Scene Synthesis
on PRO-teXt
2D Semantic Segmentation task 1 (8 classes)
3D Semantic Scene Completion
+1
Causal Scene BERT: Improving object detection by searching for challenging groups
We verify that the prioritized groups found via intervention are challenging for the object detector and show that retraining with data collected from these groups helps inordinately compared to adding more IID data.
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets
This is expensive, and guaranteeing the quality of the labels is a major challenge.
Fed-Sim: Federated Simulation for Medical Imaging
Since the model of geometry and material is disentangled from the imaging sensor, it can effectively be trained across multiple medical centers.
Interactive Annotation of 3D Object Geometry using 2D Scribbles
We implement our framework as a web service and conduct a user study, where we show that user annotated data using our method effectively facilitates real-world learning tasks.
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation
In Meta-Sim2, we aim to learn the scene structure in addition to parameters, which is a challenging problem due to its discrete nature.
Learning to Evaluate Perception Models Using Planner-Centric Metrics
The downside of these metrics is that, at worst, they penalize all incorrect detections equally without conditioning on the task or scene, and at best, heuristics need to be chosen to ensure that different mistakes count differently.
Neural Turtle Graphics for Modeling City Road Layouts
We propose Neural Turtle Graphics (NTG), a novel generative model for spatial graphs, and demonstrate its applications in modeling city road layouts.
Meta-Sim: Learning to Generate Synthetic Datasets
Training models to high-end performance requires availability of large labeled datasets, which are expensive to get.
Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations
We further reason about true object boundaries during training using a level set formulation, which allows the network to learn from misaligned labels in an end-to-end fashion.
Fast Interactive Object Annotation with Curve-GCN
Our model runs at 29. 3ms in automatic, and 2. 6ms in interactive mode, making it 10x and 100x faster than Polygon-RNN++.
Learning to Caption Images through a Lifetime by Asking Questions
In order to bring artificial agents into our lives, we will need to go beyond supervised learning on closed datasets to having the ability to continuously expand knowledge.
Color Sails: Discrete-Continuous Palettes for Deep Color Exploration
Then, the user can adjust color sail parameters to change the base colors, their blending behavior and the number of colors, exploring a wide range of options for the original design.
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
Manually labeling datasets with object masks is extremely time consuming.
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos
We propose a novel method for temporally pooling frames in a video for the task of human action recognition.
