Search Results for author:
Found 26 papers, 2 papers with code
Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex
Next, we use the contrast loss function to minimize the distance between the image embedding features and the text embedding features to complete the alignment operation of the stimulus image and text information.
A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information
A convolutional network then maps from this multimodal feature space to voxel space, constructing the multimodal visual information encoding network model.
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
The deep neural network has attained significant efficiency in image recognition.
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.
Ranked #1 on
Stance Detection
on MGTAB
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks
Adversarial attacks are considered the intrinsic vulnerability of CNNs.
LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction
The noisy incomplete projection data results in the ill-posedness of the inverse problems.
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory.
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness
A new constraints norm is proposed in model training based on these criteria to isolate adversarial transferability without any prior knowledge of adversarial samples.
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks (DNNs).
RPT++: Customized Feature Representation for Siamese Visual Tracking
Polar pooling plays the role of enriching information collected from the semantic keypoints for stronger classification, while extreme pooling facilitates explicit visual patterns of the object boundary for accurate target state estimation.
Adaptive Multi-layer Contrastive Graph Neural Networks
AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks.
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three.
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
The defense strategies are particularly passive in these processes, and enhancing initiative of such strategies can be an effective way to get out of this arms race.
RPT: Learning Point Set Representation for Siamese Visual Tracking
While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem.
Ranked #13 on
Semi-Supervised Video Object Segmentation
on VOT2020
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies.
BigGAN-based Bayesian reconstruction of natural images from human brain activity
In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.
AdvJND: Generating Adversarial Examples with Just Noticeable Difference
To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference coefficients, in the constraint of a distortion function when generating adversarial examples.
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively.
Neurons and Cognition
A Sinogram Inpainting Method based on Generative Adversarial Network for Limited-angle Computed Tomography
Limited-angle computed tomography (CT) image reconstruction is a challenging reconstruction problem in the fields of CT. With the development of deep learning, the generative adversarial network (GAN) perform well in image restoration by approximating the distribution of training sample data.
Medical Physics
A visual encoding model based on deep neural networks and transfer learning
In this framework, we employ the transfer learning technique to incorporate a pre-trained DNN (i. e., AlexNet) and train a nonlinear mapping from visual features to brain activity.
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Furthermore, we show that voxel-encoding models trained on regular images can successfully generalize to the neural responses to AI images but not AN images.
Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network
Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN).
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner.
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography.
