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Found 18 papers, 10 papers with code
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Finally, SAM is prompted by the retrieved ROI to segment a specific organ.
ConvLoRA and AdaBN based Domain Adaptation via Self-Training
To further boost adaptation, we utilize Adaptive Batch Normalization (AdaBN) which computes target-specific running statistics and use it along with ConvLoRA.
Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data
To address both challenges, we leverage synthetic data and propose an end-to-end model for polyp segmentation that integrates real and synthetic data to artificially increase the size of the datasets and aid the training when unlabeled samples are available.
Joint one-sided synthetic unpaired image translation and segmentation for colorectal cancer prevention
We take advantage of recent one-sided translation models because they use significantly less memory, allowing us to add a segmentation model in the training loop.
Unifying Synergies between Self-supervised Learning and Dynamic Computation
Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings.
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets.
Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts
Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues.
Embedding contrastive unsupervised features to cluster in- and out-of-distribution noise in corrupted image datasets
These noisy samples have been evidenced by previous works to be a mixture of in-distribution (ID) samples, assigned to the incorrect category but presenting similar visual semantics to other classes in the dataset, and out-of-distribution (OOD) images, which share no semantic correlation with any category from the dataset.
Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video
Cow lameness is a severe condition that affects the life cycle and life quality of dairy cows and results in considerable economic losses.
How Important is Importance Sampling for Deep Budgeted Training?
We suggest that, given a specific budget, the best course of action is to disregard the importance and introduce adequate data augmentation; e. g. when reducing the budget to a 30% in CIFAR-10/100, RICAP data augmentation maintains accuracy, while importance sampling does not.
Addressing out-of-distribution label noise in webly-labelled data
We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1. 0 and Clothing1M.
The Importance of Importance Sampling for Deep Budgeted Training
For example, training in CIFAR-10/100 with 30% of the full training budget, a uniform sampling strategy with certain data augmentation surpasses the performance of 100% budget models trained with standard data augmentation.
Multi-Objective Interpolation Training for Robustness to Label Noise
We further propose a novel label noise detection method that exploits the robust feature representations learned via contrastive learning to estimate per-sample soft-labels whose disagreements with the original labels accurately identify noisy samples.
Ranked #21 on
Image Classification
on mini WebVision 1.0
Reliable Label Bootstrapping for Semi-Supervised Learning
We propose Reliable Label Bootstrapping (ReLaB), an unsupervised preprossessing algorithm which improves the performance of semi-supervised algorithms in extremely low supervision settings.
Towards Robust Learning with Different Label Noise Distributions
However, we show that different noise distributions make the application of this trick less straightforward and propose to continuously relabel all images to reveal a discriminative loss against multiple distributions.
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning
In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples.
Unsupervised Label Noise Modeling and Loss Correction
Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss).
Ranked #44 on
Image Classification
on Clothing1M
On guiding video object segmentation
This guiding process relies on foreground masks from independent algorithms (i. e. state-of-the-art algorithms) to implement an attention mechanism that incorporates the spatial location of foreground and background to compute their separated representations.
