Search Results for author:
Found 21 papers, 16 papers with code
Momentum-based Weight Interpolation of Strong Zero-Shot Models for Continual Learning
Large pre-trained, zero-shot capable models have shown considerable success both for standard transfer and adaptation tasks, with particular robustness towards distribution shifts.
Disentanglement of Correlated Factors via Hausdorff Factorized Support
We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over $+60\%$ in relative improvement over existing disentanglement methods.
A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning
We model images as directional von Mises-Fisher (vMF) distributions on the hypersphere that can reflect image-intrinsic uncertainties.
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations.
Improving the Fairness of Chest X-ray Classifiers
We also find that methods which achieve group fairness do so by worsening performance for all groups.
Integrating Language Guidance into Vision-based Deep Metric Learning
This causes learned embedding spaces to encode incomplete semantic context and misrepresent the semantic relation between classes, impacting the generalizability of the learned metric space.
Ranked #5 on
Metric Learning
on CARS196
(using extra training data)
Non-isotropy Regularization for Proxy-based Deep Metric Learning
Deep Metric Learning (DML) aims to learn representation spaces on which semantic relations can simply be expressed through predefined distance metrics.
Ranked #8 on
Metric Learning
on CUB-200-2011
(using extra training data)
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML.
Towards Total Recall in Industrial Anomaly Detection
Being able to spot defective parts is a critical component in large-scale industrial manufacturing.
Ranked #2 on
Anomaly Detection
on MVTec AD
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives.
Ranked #7 on
Metric Learning
on CARS196
(using extra training data)
Uniform Priors for Data-Efficient Transfer
Deep Neural Networks have shown great promise on a variety of downstream applications; but their ability to adapt and generalize to new data and tasks remains a challenge.
COVID-19 Image Data Collection: Prospective Predictions Are the Future
This dataset currently contains hundreds of frontal view X-rays and is the largest public resource for COVID-19 image and prognostic data, making it a necessary resource to develop and evaluate tools to aid in the treatment of COVID-19.
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
In this study, we present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images.
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Visual Similarity plays an important role in many computer vision applications.
Ranked #10 on
Metric Learning
on CUB-200-2011
(using extra training data)
Sharing Matters for Generalization in Deep Metric Learning
The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes.
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Learning visual similarity requires to learn relations, typically between triplets of images.
Ranked #14 on
Metric Learning
on CUB-200-2011
(using extra training data)
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year.
MIC: Mining Interclass Characteristics for Improved Metric Learning
In contrast, we propose to explicitly learn the latent characteristics that are shared by and go across object classes.
Ranked #16 on
Metric Learning
on CUB-200-2011
(using extra training data)
Mask Mining for Improved Liver Lesion Segmentation
We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models.
Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
At present, lesion segmentation is still performed manually (or semi-automatically) by medical experts.
Ranked #3 on
Liver Segmentation
on LiTS2017
(Dice metric)
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
