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Found 34 papers, 15 papers with code
I2Edit: Towards Multi-turn Interactive Image Editing via Dialogue
The dataset is constructed upon the CelebA-HQ dataset with images annotated with a multi-turn dialogue that corresponds to the user editing requirements.
Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation
Such multi-shot scheme brings inference burden, and the predefined scales inevitably have gap from real data.
PetsGAN: Rethinking Priors for Single Image Generation
Moreover, we apply our method to other image manipulation tasks (e. g., style transfer, harmonization), and the results further prove the effectiveness and efficiency of our method.
Dual Spoof Disentanglement Generation for Face Anti-spoofing with Depth Uncertainty Learning
Such noisy samples are hard to predict precise depth values, thus may obstruct the widely-used depth supervised optimization.
FasterPose: A Faster Simple Baseline for Human Pose Estimation
By the experimental analysis, we find that the HR representation leads to a sharp increase of computational cost, while the accuracy improvement remains marginal compared with the low-resolution (LR) representation.
Boosting Semi-Supervised Face Recognition with Noise Robustness
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
Multi-Agent Semi-Siamese Training for Long-tail and Shallow Face Learning
However, unlike the existing public face datasets, in many real-world scenarios of face recognition, the depth of training dataset is shallow, which means only two face images are available for each ID.
Towards NIR-VIS Masked Face Recognition
In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method.
Dive into Ambiguity: Latent Distribution Mining and Pairwise Uncertainty Estimation for Facial Expression Recognition
Due to the subjective annotation and the inherent interclass similarity of facial expressions, one of key challenges in Facial Expression Recognition (FER) is the annotation ambiguity.
CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification
Visible-Infrared person re-identification (VI-ReID) aims to match cross-modality pedestrian images, breaking through the limitation of single-modality person ReID in dark environment.
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing
Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently.
Ranked #3 on
Face Parsing
on CelebAMask-HQ
FaceX-Zoo: A PyTorch Toolbox for Face Recognition
For example, the production of face representation network desires a modular training scheme to consider the proper choice from various candidates of state-of-the-art backbone and training supervision subject to the real-world face recognition demand; for performance analysis and comparison, the standard and automatic evaluation with a bunch of models on multiple benchmarks will be a desired tool as well; besides, a public groundwork is welcomed for deploying the face recognition in the shape of holistic pipeline.
The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances
To start with, we present an overview of the end-to-end deep face recognition.
Edge-aware Graph Representation Learning and Reasoning for Face Parsing
Specifically, we encode a facial image onto a global graph representation where a collection of pixels ("regions") with similar features are projected to each vertex.
Ranked #4 on
Face Parsing
on CelebAMask-HQ
NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition
However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.
Semi-Siamese Training for Shallow Face Learning
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing
The dataset is publicly accessible to the community for boosting the advance of face parsing. 1 Second, a simple yet effective Boundary-Attention Semantic Segmentation (BASS) method is proposed for face parsing, which contains a three-branch network with elaborately developed loss functions to fully exploit the boundary information.
Ranked #8 on
Face Parsing
on LaPa
Mis-classified Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark
Each image is provided with accurate annotation of a 11-category pixel-level label map along with coordinates of 106-point landmarks.
Grand Challenge of 106-Point Facial Landmark Localization
However, previous competitions on facial landmark localization (i. e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components.
Improved Selective Refinement Network for Face Detection
With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.
Support Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Ranked #1 on
Face Identification
on Trillion Pairs Dataset
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities.
ScratchDet: Training Single-Shot Object Detectors from Scratch
Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence.
Large-scale Bisample Learning on ID Versus Spot Face Recognition
In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition.
S3FD: Single Shot Scale-Invariant Face Detector
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
FaceBoxes: A CPU Real-time Face Detector with High Accuracy
The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.
Ranked #3 on
Face Detection
on PASCAL Face
S$^3$FD: Single Shot Scale-invariant Face Detector
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces.
Ranked #2 on
Face Detection
on PASCAL Face
Embedding Deep Metric for Person Re-identication A Study Against Large Variations
From this point of view, selecting suitable positive i. e. intra-class) training samples within a local range is critical for training the CNN embedding, especially when the data has large intra-class variations.
Learning Discriminative Features with Class Encoder
Deep neural networks usually benefit from unsupervised pre-training, e. g. auto-encoders.
Constrained Deep Metric Learning for Person Re-identification
In this paper, we propose a novel CNN-based method to learn a discriminative metric with good robustness to the over-fitting problem in person re-identification.
Face Alignment Across Large Poses: A 3D Solution
Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community.
Ranked #3 on
3D Face Reconstruction
on Florence
