https://github.com/julian-risch/PatentMatch
공식 코드PatentMatch: A Dataset for Matching Patent Claims & Prior Art
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
PatentMatch: A Dataset for Matching Patent Claims & Prior Art
Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence
Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation
Match me if you can: Semi-Supervised Semantic Correspondence Learning with Unpaired Images
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On
Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentation
XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention
Lexical Sememe Prediction using Dictionary Definitions by Capturing Local Semantic Correspondence
SPColor: Semantic Prior Guided Exemplar-based Image Colorization
TransforMatcher: Match-to-Match Attention for Semantic Correspondence
Multi-Level Correlation Network For Few-Shot Image Classification
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings
DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
Dynamic Context Correspondence Network for Semantic Alignment
CATs: Cost Aggregation Transformers for Visual Correspondence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.