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Found 10 papers, 3 papers with code
Depth-aware Test-Time Training for Zero-shot Video Object Segmentation
In this work, we introduce a test-time training (TTT) strategy to address the problem.
Detecting Generated Images by Real Images Only
This paper approaches the generated image detection problem from a new perspective: Start from real images.
GreatSplicing: A Semantically Rich Splicing Dataset
In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces.
DLBD: A Self-Supervised Direct-Learned Binary Descriptor
Since their binarization processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning.
MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation
Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model.
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion
The proposed DTMNet is an end-to-end deep neural network with only one convolutional layer and three fully connected layers.
Reality Transform Adversarial Generators for Image Splicing Forgery Detection and Localization
For detecting the tampered regions, a forgery localization generator GM is proposed based on a multi-decoder-single-task strategy.
Color-related Local Binary Pattern: A Learned Local Descriptor for Color Image Recognition
In this paper, a color-related local binary pattern (cLBP) which learns the dominant patterns from the decoded LBP is proposed for color images recognition.
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection
The core idea of the RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN.
