Search Results for author:
Found 12 papers, 6 papers with code
Aligning Diffusion Models by Optimizing Human Utility
We present Diffusion-KTO, a novel approach for aligning text-to-image diffusion models by formulating the alignment objective as the maximization of expected human utility.
Wild2Avatar: Rendering Humans Behind Occlusions
In this work, we present Wild2Avatar, a neural rendering approach catered for occluded in-the-wild monocular videos.
Hierarchical Open-vocabulary Universal Image Segmentation
Open-vocabulary image segmentation aims to partition an image into semantic regions according to arbitrary text descriptions.
Ranked #1 on
Image Segmentation
on Pascal Panoptic Parts
Masking and Mixing Adversarial Training
Adversarial training is a popular and straightforward technique to defend against the threat of adversarial examples.
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes
Data augmentation is an essential technique for improving recognition accuracy in object recognition using deep learning.
Refine and Represent: Region-to-Object Representation Learning
Recent works in self-supervised learning have demonstrated strong performance on scene-level dense prediction tasks by pretraining with object-centric or region-based correspondence objectives.
Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net
They are highly and widely used in tasks such as segmentation.
Invisible-to-Visible: Privacy-Aware Human Instance Segmentation using Airborne Ultrasound via Collaborative Learning Variational Autoencoder
In inference, it is possible to obtain instance segmentation results only from sound images.
Contrastive Neural Processes for Self-Supervised Learning
In this paper, we propose a novel self-supervised learning framework that combines contrastive learning with neural processes.
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
Our approach results in a computationally and memory-efficient model: CFLOW-AD is faster and smaller by a factor of 10x than prior state-of-the-art with the same input setting.
Ranked #13 on
Anomaly Detection
on VisA
(Detection AUROC metric)
Home Action Genome: Cooperative Compositional Action Understanding
However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning.
Ranked #1 on
Video Classification
on Home Action Genome
AutoDO: Robust AutoAugment for Biased Data with Label Noise via Scalable Probabilistic Implicit Differentiation
To overcome these limitations, we reformulate AutoAugment as a generalized automated dataset optimization (AutoDO) task that minimizes the distribution shift between test data and distorted train dataset.
