https://bitbucket.org/aswathyve/unifieddeceptioncorpus
공식 코드UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection
Cross-Domain Identification for Thermal-to-Visible Face Recognition
Affect Expression Behaviour Analysis in the Wild using Consensual Collaborative Training
Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition
Pre-training strategies and datasets for facial representation learning
Open-Set Face Identification on Few-Shot Gallery by Fine-Tuning
GCF: Graph Convolutional Networks for Facial Expression Recognition
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Progressive-Scale Boundary Blackbox Attack via Projective Gradient Estimation
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
MakeupAttack: Feature Space Black-box Backdoor Attack on Face Recognition via Makeup Transfer
Colors Matter: AI-Driven Exploration of Human Feature Colors
Specular- and Diffuse-reflection-based Face Spoofing Detection for Mobile Devices
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial Expression Recognition
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
Variational Leakage: The Role of Information Complexity in Privacy Leakage
SensitiveNets: Learning Agnostic Representations with Application to Face Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.