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Found 31 papers, 13 papers with code
Hierarchical Face Aging through Disentangled Latent Characteristics
To explore the age effects on facial images, we propose a Disentangled Adversarial Autoencoder (DAAE) to disentangle the facial images into three independent factors: age, identity and extraneous information.
ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence
To help advance research in political science, we introduce ConfliBERT, a domain-specific pre-trained language model for conflict and political violence.
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis
Directly employing small models may result in a significant drop in performance since it is difficult for a small model to adequately capture local structure and global shape information simultaneously, which are essential clues for point cloud analysis.
Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT
Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.
CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue
This paper explores interactive facial image editing via dialogue and introduces the ChatEdit benchmark dataset for evaluating image editing and conversation abilities in this context.
Controllable Fake Document Infilling for Cyber Deception
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information.
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.
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.
Uncertainty-Aware Reliable Text Classification
Extensive empirical experiments demonstrate that our model based on evidential uncertainty outperforms other counterparts for detecting OOD examples.
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.
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.
Facial Expression Recognition
Facial Expression Recognition (FER)
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.
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.
Multidimensional Uncertainty-Aware Evidential Neural Networks
By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem.
Generative Adversarial Network
Multi-class Classification
+3
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition
As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises.
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search
In this paper, we rethink three freedoms of differentiable NAS, i. e. operation-level, depth-level and width-level, and propose a novel method, named Three-Freedom NAS (TF-NAS), to achieve both good classification accuracy and precise latency constraint.
Dual Variational Generation for Low Shot Heterogeneous Face Recognition
Specifically, we first introduce a dual variational autoencoder to represent a joint distribution of paired heterogeneous images.
UVA: A Universal Variational Framework for Continuous Age Analysis
UVA is the first attempt to achieve facial age analysis tasks, including age translation, age generation and age estimation, in a universal framework.
M2FPA: A Multi-Yaw Multi-Pitch High-Quality Database and Benchmark for Facial Pose Analysis
In this paper, a new large-scale Multi-yaw Multi-pitch high-quality database is proposed for Facial Pose Analysis (M2FPA), including face frontalization, face rotation, facial pose estimation and pose-invariant face recognition.
High Fidelity Face Manipulation with Extreme Poses and Expressions
Furthermore, due to the lack of high-resolution face manipulation databases to verify the effectiveness of our method, we collect a new high-quality Multi-View Face (MVF-HQ) database.
Dual Variational Generation for Low-Shot Heterogeneous Face Recognition
Then, in order to ensure the identity consistency of the generated paired heterogeneous images, we impose a distribution alignment in the latent space and a pairwise identity preserving in the image space.
Ranked #1 on
Face Verification
on CASIA NIR-VIS 2.0
Global and Local Consistent Wavelet-domain Age Synthesis
%Moreover, to achieve accurate age generation under the premise of preserving the identity information, age estimation network and face verification network are employed.
A Coupled Evolutionary Network for Age Estimation
Inspired by the biological evolutionary mechanism, we propose a Coupled Evolutionary Network (CEN) with two concurrent evolutionary processes: evolutionary label distribution learning and evolutionary slack regression.
Geometry-Aware Face Completion and Editing
Extensive experimental results qualitatively and quantitatively demonstrate that our network is able to generate visually pleasing face completion results and edit face attributes as well.
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization
We decompose the prerequisite of warping into dense correspondence field estimation and facial texture map recovering, which are both well addressed by deep networks.
Pose-Guided Photorealistic Face Rotation
Face rotation provides an effective and cheap way for data augmentation and representation learning of face recognition.
Load Balanced GANs for Multi-view Face Image Synthesis
Multi-view face synthesis from a single image is an ill-posed problem and often suffers from serious appearance distortion.
Global and Local Consistent Age Generative Adversarial Networks
To utilize both global and local facial information, we propose a Global and Local Consistent Age Generative Adversarial Network (GLCA-GAN).
Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions
We treat the face completion and corruption as disentangling and fusing processes of clean faces and occlusions, and propose a jointly disentangling and fusing Generative Adversarial Network (DF-GAN).
Attention-Set based Metric Learning for Video Face Recognition
In this paper, we propose a novel Attention-Set based Metric Learning (ASML) method to measure the statistical characteristics of image sets.
