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Found 12 papers, 6 papers with code
Message-passing selection: Towards interpretable GNNs for graph classification
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
Multi-Prompt with Depth Partitioned Cross-Modal Learning
In recent years, soft prompt learning methods have been proposed to fine-tune large-scale vision-language pre-trained models for various downstream tasks.
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e. g., clear edges, smooth color shading, abstract fine structures, etc.).
Multi-view Feature Augmentation with Adaptive Class Activation Mapping
We propose an end-to-end-trainable feature augmentation module built for image classification that extracts and exploits multi-view local features to boost model performance.
Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection
For the second issue, we reconsider how to improve detection efficiency with excellent performance, and then propose our lightweight encoder-decoder architecture termed CarNet.
Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function
Numerous detection problems in computer vision, including road crack detection, suffer from exceedingly foreground-background imbalance.
Two-stage Training for Learning from Label Proportions
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.
Attention Transfer Network for Nature Image Matting
Then, we introduce a scale transfer block to magnify the feature maps without adding extra information.
Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification
RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations.
Learning from Label Proportions with Generative Adversarial Networks
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning
To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.
Multi-view metric learning for multi-instance image classification
To improve the performance, the idea of multi-view learning is implemented and three kinds of features are provided, each one corresponds to a single view.
