Search Results for author:
Found 26 papers, 9 papers with code
Faceptor: A Generalist Model for Face Perception
This design enhances the unification of model structure while improving application efficiency in terms of storage overhead.
Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis
2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose.
Marginal Debiased Network for Fair Visual Recognition
Deep neural networks (DNNs) are often prone to learn the spurious correlations between target classes and bias attributes, like gender and race, inherent in a major portion of training data (bias-aligned samples), thus showing unfair behavior and arising controversy in the modern pluralistic and egalitarian society.
Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition
With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention.
Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence
In this paper, we show that using high-level contextualized features as prediction targets can achieve superior performance.
CLIP in Medical Imaging: A Comprehensive Survey
With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications.
Oracle Character Recognition using Unsupervised Discriminative Consistency Network
Ancient history relies on the study of ancient characters.
Survey on Deep Face Restoration: From Non-blind to Blind and Beyond
We also discuss notable benchmarks commonly utilized in the field.
SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation
To address the conflicts among multiple tasks and meet the different demands of tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis subnet, which can adaptively select the features from optimal levels and channels to perform the desired tasks.
Adaptive Face Recognition Using Adversarial Information Network
Supervision on pseudo-labeled samples attracts them towards their prototypes and would cause an intra-domain gap between pseudo-labeled samples and the remaining unlabeled samples within target domain, which results in the lack of discrimination in face recognition.
Gradient Attention Balance Network: Mitigating Face Recognition Racial Bias via Gradient Attention
Specifically, we use the gradient attention map (GAM) of the face recognition network to track the sensitive facial regions and make the GAMs of different races tend to be consistent through adversarial learning.
Deep face recognition with clustering based domain adaptation
Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images gathered from Internet are different from test images because of different lighting condition, pose and image quality.
Cycle Label-Consistent Networks for Unsupervised Domain Adaptation
The cycle label-consistent loss reinforces the consistency between ground-truth labels and pseudo-labels of source samples leading to statistically similar latent representations between source and target domains.
Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms
We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale images of 30, 222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion.
Unsupervised Structure-Texture Separation Network for Oracle Character Recognition
Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters.
Meta Balanced Network for Fair Face Recognition
Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones.
Augmented Geometric Distillation for Data-Free Incremental Person ReID
On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it.
Predicting Disease Progress with Imprecise Lab Test Results
In this method, each sample in imprecision range space has a certain probability to be the real value, participating in the loss calculation.
Impact of Medical Data Imprecision on Learning Results
In this paper, we initiate a study on the impact of imprecision on prediction results in a healthcare application where a pre-trained model is used to predict future state of hyperthyroidism for patients.
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning
Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly.
Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning
To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses.
Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition.
Time-weighted Attentional Session-Aware Recommender System
Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information.
Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition.
Deep Face Recognition: A Survey
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.
Deep Visual Domain Adaptation: A Survey
Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data.
