https://github.com/zyh-uaiaaaa/erasing-attention-consistency
공식 코드Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
An anomaly detection approach for backdoored neural networks: face recognition as a case study
MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
On the use of automatically generated synthetic image datasets for benchmarking face recognition
Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation
SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data
Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
Readability-based Sentence Ranking for Evaluating Text Simplification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
High-Resolution Representations for Labeling Pixels and Regions
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
RetinaFace: Single-stage Dense Face Localisation in the Wild
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.