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Found 17 papers, 4 papers with code
LoCo: Low-Contrast-Enhanced Contrastive Learning for Semi-Supervised Endoscopic Image Segmentation
The segmentation of endoscopic images plays a vital role in computer-aided diagnosis and treatment.
Causality-inspired Latent Feature Augmentation for Single Domain Generalization
Single domain generalization (Single-DG) intends to develop a generalizable model with only one single training domain to perform well on other unknown target domains.
Mitigating Label Noise on Graph via Topological Sample Selection
Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled.
DTA: Distribution Transform-based Attack for Query-Limited Scenario
In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack.
Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation
Radiology Report Generation (RRG) draws attention as a vision-and-language interaction of biomedical fields.
Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method
In the existing FGVC datasets used in computer vision, it is generally assumed that each collected instance has fixed characteristics and the distribution of different categories is relatively balanced.
Improving robustness of softmax corss-entropy loss via inference information
Several state-of-the-arts start from improving the inter-class separability of training samples by modifying loss functions, where we argue that the adversarial samples are ignored and thus limited robustness to adversarial attacks is resulted.
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention.
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task.
Boundary-aware Context Neural Network for Medical Image Segmentation
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.
Perturb More, Trap More: Understanding Behaviors of Graph Neural Networks
While graph neural networks (GNNs) have shown a great potential in various tasks on graph, the lack of transparency has hindered understanding how GNNs arrived at its predictions.
Cascaded Context Enhancement Network for Automatic Skin Lesion Segmentation
In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation.
HopGAT: Hop-aware Supervision Graph Attention Networks for Sparsely Labeled Graphs
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention.
Digital image splicing detection based on Markov features in QDCT and QWT domain
In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain.
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques.
Fingerprint Classification Based on Depth Neural Network
Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS).
