Search Results for author:
Found 26 papers, 20 papers with code
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.
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)
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.
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 #19 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.
PADS: Policy-Adapted Sampling for Visual Similarity Learning
Learning visual similarity requires to learn relations, typically between triplets of images.
Ranked #17 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.
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Visual Similarity plays an important role in many computer vision applications.
Ranked #13 on
Metric Learning
on CUB-200-2011
(using extra training data)
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.
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.
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.
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 #10 on
Metric Learning
on CARS196
(using extra training data)
Towards Total Recall in Industrial Anomaly Detection
Being able to spot defective parts is a critical component in large-scale industrial manufacturing.
Ranked #3 on
Anomaly Detection
on AeBAD-V
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.
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 #8 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 #11 on
Metric Learning
on CUB-200-2011
(using extra training data)
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.
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.
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.
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.
If at First You Don't Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
Despite their impressive capabilities, diffusion-based text-to-image (T2I) models can lack faithfulness to the text prompt, where generated images may not contain all the mentioned objects, attributes or relations.
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts
The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3.
Vision-by-Language for Training-Free Compositional Image Retrieval
Finally, we show that CIReVL makes CIR human-understandable by composing image and text in a modular fashion in the language domain, thereby making it intervenable, allowing to post-hoc re-align failure cases.
Ranked #1 on
Zero-Shot Composed Image Retrieval (ZS-CIR)
on CIRCO
Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model
Training deep networks requires various design decisions regarding for instance their architecture, data augmentation, or optimization.
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios.
