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Found 22 papers, 10 papers with code
DragText: Rethinking Text Embedding in Point-based Image Editing
A significant aspect that remains unexplored is the interaction between text and image embeddings.
Parameter Efficient Fine Tuning for Multi-scanner PET to PET Reconstruction
Reducing scan time in Positron Emission Tomography (PET) imaging while maintaining high-quality images is crucial for minimizing patient discomfort and radiation exposure.
Slice-Consistent 3D Volumetric Brain CT-to-MRI Translation with 2D Brownian Bridge Diffusion Model
In neuroimaging, generally, brain CT is more cost-effective and accessible imaging option compared to MRI.
FALCON: Frequency Adjoint Link with CONtinuous Density Mask for Fast Single Image Dehazing
This work introduces FALCON (Frequency Adjoint Link with CONtinuous density mask), a single-image dehazing system achieving state-of-the-art performance on both quality and speed.
WoLF: Wide-scope Large Language Model Framework for CXR Understanding
(1) Previous methods solely use CXR reports, which are insufficient for comprehensive Visual Question Answering (VQA), especially when additional health-related data like medication history and prior diagnoses are needed.
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning
Recent advances in text-conditioned image generation diffusion models have begun paving the way for new opportunities in modern medical domain, in particular, generating Chest X-rays (CXRs) from diagnostic reports.
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
Semantic segmentation has innately relied on extensive pixel-level annotated data, leading to the emergence of unsupervised methodologies.
Ranked #1 on
Unsupervised Semantic Segmentation
on Potsdam-3
CoBra: Complementary Branch Fusing Class and Semantic Knowledge for Robust Weakly Supervised Semantic Segmentation
This includes not only the masks generated by our model, but also the segmentation results derived from utilizing these masks as pseudo labels.
Evidence-empowered Transfer Learning for Alzheimer's Disease
To address this, we present evidence-empowered transfer learning for AD diagnosis.
PAC-Bayesian Domain Adaptation Bounds for Multiclass Learners
Multiclass neural networks are a common tool in modern unsupervised domain adaptation, yet an appropriate theoretical description for their non-uniform sample complexity is lacking in the adaptation literature.
Test-time Fourier Style Calibration for Domain Generalization
Thus, it is still possible that those methods can overfit to source domains and perform poorly on target domains.
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation.
Point Cloud Augmentation with Weighted Local Transformations
Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature.
Ranked #11 on
Point Cloud Classification
on PointCloud-C
PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging
Application of deep neural networks to medical imaging tasks has in some sense become commonplace.
Multi-Domain Learning by Meta-Learning: Taking Optimal Steps in Multi-Domain Loss Landscapes by Inner-Loop Learning
We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications.
Robust White Matter Hyperintensity Segmentation on Unseen Domain
That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target).
Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve
Further, this theory has been well-used in practice.
Conditional Recurrent Flow: Conditional Generation of Longitudinal Samples with Applications to Neuroimaging
Such models may work for cross-sectional studies, however, they are not suitable to generate data for longitudinal studies that focus on "progressive" behavior in a sequence of data.
Tensorize, Factorize and Regularize: Robust Visual Relationship Learning
Visual relationships provide higher-level information of objects and their relations in an image â this enables a semantic understanding of the scene and helps downstream applications.
Sampling-free Uncertainty Estimation in Gated Recurrent Units with Exponential Families
There has recently been a concerted effort to derive mechanisms in vision and machine learning systems to offer uncertainty estimates of the predictions they make.
Coupled Harmonic Bases for Longitudinal Characterization of Brain Networks
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
A Projection Free Method for Generalized Eigenvalue Problem With a Nonsmooth Regularizer
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
