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Found 13 papers, 6 papers with code
Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations
We show that adding a consistency loss, ensuring that the predictions of the network are consistent over consecutive training epochs, is a simple yet effective method to train multi-label classifiers in a weakly supervised setting.
Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index
We verify these results empirically in an extensive validation on six medical segmentation tasks and can confirm that metric-sensitive losses are superior to cross-entropy based loss functions in case of evaluation with Dice Score or Jaccard Index.
AOWS: Adaptive and optimal network width search with latency constraints
We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware.
Discriminative training of conditional random fields with probably submodular constraints
In order to constrain such a model to remain tractable, previous approaches have enforced the weight vector to be positive for pairwise potentials in which the labels differ, and set pairwise potentials to zero in the case that the label remains the same.
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard.
Adaptive Compression-based Lifelong Learning
The latter approach falls under the denomination of lifelong learning, where the model is updated in a way that it performs well on both old and new tasks, without having access to the old task's training samples anymore.
Generating superpixels using deep image representations
Superpixel algorithms are a common pre-processing step for computer vision algorithms such as segmentation, object tracking and localization.
MultiGrain: a unified image embedding for classes and instances
When fed to a linear classifier, the learned embeddings provide state-of-the-art classification accuracy.
Ranked #1 on
Image Retrieval
on INRIA Holidays
Yes, IoU loss is submodular - as a function of the mispredictions
This note is a response to [7] in which it is claimed that [13, Proposition 11] is false.
Efficient semantic image segmentation with superpixel pooling
In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation.
The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
Function Norms and Regularization in Deep Networks
Deep neural networks (DNNs) have become increasingly important due to their excellent empirical performance on a wide range of problems.
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
Ranked #34 on
Semantic Segmentation
on PASCAL VOC 2012 test
