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Found 13 papers, 7 papers with code
Artwork Protection Against Neural Style Transfer Using Locally Adaptive Adversarial Color Attack
This process leverages neural networks to merge aesthetic elements of a style image with the structural aspects of a content image into a harmoniously integrated visual result.
A Good Feature Extractor Is All You Need for Weakly Supervised Pathology Slide Classification
We question this belief in the context of weakly supervised whole slide image classification, motivated by the emergence of powerful feature extractors trained using self-supervised learning on diverse pathology datasets.
Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes
To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework.
A White-Box False Positive Adversarial Attack Method on Contrastive Loss Based Offline Handwritten Signature Verification Models
The key contributions of this paper include a novel false positive attack method, two new loss functions, effective style transfer in handwriting styles, and superior performance in white-box false positive attacks compared to other white-box attack methods.
Deep Multiple Instance Learning with Distance-Aware Self-Attention
We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0. 91.
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial Networks
The presence and density of specific types of immune cells are important to understand a patient's immune response to cancer.
MultiPathGAN: Structure Preserving Stain Normalization using Unsupervised Multi-domain Adversarial Network with Perception Loss
Histopathology relies on the analysis of microscopic tissue images to diagnose disease.
Hoechst Is All You Need: Lymphocyte Classification with Deep Learning
Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify several proteins expressed on the surface of cells, enabling cell classification, better understanding of the tumour micro-environment, more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients.
Determining Chess Game State From an Image
Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately.
Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping
Understanding the predictions made by Artificial Intelligence (AI) systems is becoming more and more important as deep learning models are used for increasingly complex and high-stakes tasks.
Deep Learning for Whole Slide Image Analysis: An Overview
The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis.
Contextually learnt detection of unusual motion-based behaviour in crowded public spaces
In this paper we are interested in analyzing behaviour in crowded public places at the level of holistic motion.
Multiple-object tracking in cluttered and crowded public spaces
This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture.
