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Sketch Recognition
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
ViSketch-GPT: Collaborative Multi-Scale Feature Extraction for Sketch Recognition and Generation
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
Hybrid Classical-Quantum architecture for vectorised image classification of hand-written sketches
SketchGPT: Autoregressive Modeling for Sketch Generation and Recognition
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
SEVA: Leveraging sketches to evaluate alignment between human and machine visual abstraction
SketchXAI: A First Look at Explainability for Human Sketches
Improved Test-Time Adaptation for Domain Generalization
Progressive Random Convolutions for Single Domain Generalization
Cross-Domain Ensemble Distillation for Domain Generalization
Abstracting Sketches through Simple Primitives
Edge Augmentation for Large-Scale Sketch Recognition without Sketches
A hierarchical residual network with compact triplet-center loss for sketch recognition
Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network
SelfReg: Self-supervised Contrastive Regularization for Domain Generalization
On Training Sketch Recognizers for New Domains
Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
Multi-Graph Transformer for Free-Hand Sketch Recognition
A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques
Reducing Domain Gap by Reducing Style Bias
Sketch-Specific Data Augmentation for Freehand Sketch Recognition
Active Scene Learning
Deep Embedding using Bayesian Risk Minimization with Application to Sketch Recognition
Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
Learning Deep Sketch Abstraction
Distribution-Aware Binarization of Neural Networks for Sketch Recognition
SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition
Training-Free Synthesized Face Sketch Recognition Using Image Quality Assessment Metrics
Learning from Synthetic Data Using a Stacked Multichannel Autoencoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.