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Found 13 papers, 13 papers with code
Proximal Splitting Adversarial Attack for Semantic Segmentation
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
Class Adaptive Network Calibration
Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method.
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images
Our method is composed of two networks: a localizer that yields segmentation mask, followed by a classifier.
Mutual-Information Based Few-Shot Classification
We motivate our transductive loss by deriving a formal relation between the classification accuracy and mutual-information maximization.
Information Maximization for Few-Shot Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot learning.
Augmented Lagrangian Adversarial Attacks
Our attack enjoys the generality of penalty methods and the computational efficiency of distance-customized algorithms, and can be readily used for a wide set of distances.
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions, while discouraging unbalanced segmentations.
Transductive Information Maximization For Few-Shot Learning
We introduce Transductive Infomation Maximization (TIM) for few-shot learning.
A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Second, we show that, more generally, minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses.
Ranked #12 on
Metric Learning
on CARS196
(using extra training data)
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection.
Min-max Entropy for Weakly Supervised Pointwise Localization
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems.
