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Found 10 papers, 3 papers with code
Understanding Before Recommendation: Semantic Aspect-Aware Review Exploitation via Large Language Models
Recommendation systems harness user-item interactions like clicks and reviews to learn their representations.
Predicting Scores of Various Aesthetic Attribute Sets by Learning from Overall Score Labels
Then, we define an aesthetic attribute contribution to describe the role of aesthetic attributes throughout an image and use it with the attribute scores and the overall scores to train our F2S model.
CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning
In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks.
TBFormer: Two-Branch Transformer for Image Forgery Localization
In this paper, we propose a Transformer-style network with two feature extraction branches for image forgery localization, and it is named as Two-Branch Transformer (TBFormer).
GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework
Our method can classify whether a CT image has been tampered and locate the tampered position.
Fine-Grained Element Identification in Complaint Text of Internet Fraud
Existing system dealing with online complaint provides a final decision without explanations.
Two-Stage Copy-Move Forgery Detection with Self Deep Matching and Proposal SuperGlue
The first stage is a backbone self deep matching network, and the second stage is named as Proposal SuperGlue.
Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution
Constrained image splicing detection and localization (CISDL) is a newly proposed challenging task for image forensics, which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other.
Copy-move Forgery Detection based on Convolutional Kernel Network
Numerous experiments are conducted to demonstrate the effectiveness and robustness of the GPU version of Convolutional Kernel Network, and the state-of-the-art performance of the proposed copy-move forgery detection method based on Convolutional Kernel Network.
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and the segmentation-based multi-scale analysis to locate tampered areas in digital images.
