Search Results for author:
Found 11 papers, 5 papers with code
Deep Colorization
This paper investigates into the colorization problem which converts a grayscale image to a colorful version.
A Bayesian Perspective on the Deep Image Prior
The deep image prior was recently introduced as a prior for natural images.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years.
On Equivariant and Invariant Learning of Object Landmark Representations
Given a collection of images, humans are able to discover landmarks by modeling the shared geometric structure across instances.
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification
We evaluate the effectiveness of semi-supervised learning (SSL) on a realistic benchmark where data exhibits considerable class imbalance and contains images from novel classes.
GANORCON: Are Generative Models Useful for Few-shot Segmentation?
Motivated by this we present an alternative approach based on contrastive learning and compare their performance on standard few-shot part segmentation benchmarks.
Improving Few-Shot Part Segmentation using Coarse Supervision
A significant bottleneck in training deep networks for part segmentation is the cost of obtaining detailed annotations.
Cross-Modal 3D Shape Generation and Manipulation
In this paper, we propose a generic multi-modal generative model that couples the 2D modalities and implicit 3D representations through shared latent spaces.
Accidental Turntables: Learning 3D Pose by Watching Objects Turn
We propose a technique for learning single-view 3D object pose estimation models by utilizing a new source of data -- in-the-wild videos where objects turn.
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to off-the-shelf SfM pipelines which have well-understood failure modes.
Machine Unlearning of Pre-trained Large Language Models
This study investigates the concept of the `right to be forgotten' within the context of large language models (LLMs).
