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Depth And Camera Motion
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
Deep Anomaly Detection for Generalized Face Anti-Spoofing
Generalized Presentation Attack Detection: a face anti-spoofing evaluation proposal
Deep Tree Learning for Zero-shot Face Anti-Spoofing
Sparse Representations for Object and Ego-motion Estimation in Dynamic Scenes
Aurora Guard: Real-Time Face Anti-Spoofing via Light Reflection
Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and Practices
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach
Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing
Deep Transfer Across Domains for Face Anti-spoofing
Enhance the Motion Cues for Face Anti-Spoofing using CNN-LSTM Architecture
Improving Face Anti-Spoofing by 3D Virtual Synthesis
Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing
Exploring Hypergraph Representation on Face Anti-spoofing Beyond 2D Attacks
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Exploiting temporal and depth information for multi-frame face anti-spoofing
Self-Supervised Learning of Depth and Camera Motion from 360° Videos
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
Face De-Spoofing: Anti-Spoofing via Noise Modeling
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding
BA-Net: Dense Bundle Adjustment Network
A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Learning Depth from Monocular Videos using Direct Methods
Unsupervised Learning of Depth and Ego-Motion from Video
DeMoN: Depth and Motion Network for Learning Monocular Stereo
Deep Residual Learning for Image Recognition
face anti-spoofing based on color texture analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.