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Found 15 papers, 3 papers with code
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.
Enhancing Generalization of Invisible Facial Privacy Cloak via Gradient Accumulation
The blooming of social media and face recognition (FR) systems has increased people's concern about privacy and security.
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.
Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline
Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces.
Model and Data Agreement for Learning with Noisy Labels
We introduce mean point ensemble to utilize a more robust loss function and more information from unselected samples to reduce error accumulation from the model perspective.
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
Deep face recognition has achieved great success due to large-scale training databases and rapidly developing loss functions.
Ranked #2 on
Face Verification
on CALFW
CASSOD-Net: Cascaded and Separable Structures of Dilated Convolution for Embedded Vision Systems and Applications
In this paper, we propose a network module, Cascaded and Separable Structure of Dilated (CASSOD) Convolution, and a special hardware system to handle the CASSOD networks efficiently.
Ranked #1 on
Face Detection
on ADE20K
Hardware Architecture of Embedded Inference Accelerator and Analysis of Algorithms for Depthwise and Large-Kernel Convolutions
In order to handle modern convolutional neural networks (CNNs) efficiently, a hardware architecture of CNN inference accelerator is proposed to handle depthwise convolutions and regular convolutions, which are both essential building blocks for embedded-computer-vision algorithms.
Ranked #1 on
Face Detection
on WIDER FACE
Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps
"Lightweight convolutional neural networks" is an important research topic in the field of embedded vision.
Ranked #1 on
Face Detection
on FDDB
(Accuracy metric)
Adaptive Label Noise Cleaning With Meta-Supervision for Deep Face Recognition
The training of a deep face recognition system usually faces the interference of label noise in the training data.
BAMSProd: A Step towards Generalizing the Adaptive Optimization Methods to Deep Binary Model
In this paper, we provide an explicit convex optimization example where training the BNNs with the traditionally adaptive optimization methods still faces the risk of non-convergence, and identify that constraining the range of gradients is critical for optimizing the deep binary model to avoid highly suboptimal solutions.
QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework
Despite the achievements of recent binarization methods on reducing the performance degradation of Binary Neural Networks (BNNs), gradient mismatching caused by the Straight-Through-Estimator (STE) still dominates quantized networks.
Ranked #927 on
Image Classification
on ImageNet
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision
Furthermore, based on YOLOv2, we design IFQ-Tinier-YOLO face detector which is a fixed-point network with 256x reduction in model size (246k Bytes) than Tiny-YOLO.
Ranked #10 on
Face Detection
on FDDB
DupNet: Towards Very Tiny Quantized CNN with Improved Accuracy for Face Detection
Firstly, we employ weights with duplicated channels for the weight-intensive layers to reduce the model size.
Ranked #1 on
Face Detection
on WIDER Face
(GFLOPs metric)
Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability).
Ranked #182 on
Image Classification
on CIFAR-10
(using extra training data)
