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Found 16 papers, 5 papers with code
Training Class-Imbalanced Diffusion Model Via Overlap Optimization
To address the observed appearance overlap between synthesized images of rare classes and tail classes, we propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
Latent Space Editing in Transformer-Based Flow Matching
Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training.
ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation
We propose ScribbleGen, a generative data augmentation method that leverages a ControlNet diffusion model conditioned on semantic scribbles to produce high-quality training data.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.
Deep-learning-based coupled flow-geomechanics surrogate model for CO$_2$ sequestration
The surrogate model is trained to predict the 3D CO2 saturation and pressure fields in the storage aquifer, and 2D displacement maps at the Earth's surface.
FroDO: From Detections to 3D Objects
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner.
Multiphase flow prediction with deep neural networks
This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency.
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
High-fidelity numerical simulation results for the posterior geomodels (generated by the surrogate-based data assimilation procedure) are shown to be in essential agreement with the recurrent R-U-Net predictions.
Beyond Gradient Descent for Regularized Segmentation Losses
Both loss functions and architectures are often explicitly tuned to be amenable to this basic local optimization.
Constrained-CNN losses for weakly supervised segmentation
To the best of our knowledge, the method of [Pathak et al., 2015] is the only prior work that addresses deep CNNs with linear constraints in weakly supervised segmentation.
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods.
On Regularized Losses for Weakly-supervised CNN Segmentation
This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training.
Kernel clustering: density biases and solutions
We call it Breiman's bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity.
Secrets of GrabCut and Kernel K-Means
Our bound formulation for kernel K-means allows to combine general pair-wise feature clustering methods with image grid regularization using graph cuts, similarly to standard color model fitting techniques for segmentation.
Kernel Cuts: MRF meets Kernel & Spectral Clustering
We propose a new segmentation model combining common regularization energies, e. g. Markov Random Field (MRF) potentials, and standard pairwise clustering criteria like Normalized Cut (NC), average association (AA), etc.
