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Found 10 papers, 3 papers with code
Sparse Coding Driven Deep Decision Tree Ensembles for Nuclear Segmentation in Digital Pathology Images
In this way, rich image appearance models together with more contextual information are integrated by learning a series of decision tree ensembles.
Block shuffling learning for Deepfake Detection
Finally, we restore the spatial layout of the blocks to capture the semantic associations among them.
A Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting
In this paper, a novel Unified Multi-Task Learning Framework of Real-Time Drone Supervision for Crowd Counting (MFCC) is proposed, which utilizes an image fusion network architecture to fuse images from the visible and thermal infrared image, and a crowd counting network architecture to estimate the density map.
Functional Connectivity Based Classification of ADHD Using Different Atlases
To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose.
Understanding CNNs from excitations
To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure.
Real-centric Consistency Learning for Deepfake Detection
Essentially, the target of deepfake detection problem is to represent natural faces and fake faces at the representation space discriminatively, and it reminds us whether we could optimize the feature extraction procedure at the representation space through constraining intra-class consistence and inter-class inconsistence to bring the intra-class representations close and push the inter-class representations apart?
An Effective Fusion Method to Enhance the Robustness of CNN
We use a dot product-based approach to add the denoising module to ResNet18 and the attention mechanism to further improve the robustness of the model.
Classification of ADHD Patients Using Kernel Hierarchical Extreme Learning Machine
However, in practice, exploring interactions in brain functional connectivity based on operational magnetic resonance imaging data is critical for studying mental illness.
Boost Adversarial Transferability by Uniform Scale and Mix Mask Method
To address these challenges, we propose a framework called Uniform Scale and Mix Mask Method (US-MM) for adversarial example generation.
Adversarial Purification of Information Masking
Notably, the residual perturbations on the purified image primarily stem from the same-position patch and similar patches of the adversarial sample.
