https://github.com/ActiveVisionLab/ANCNet
공식 코드Correspondence Networks with Adaptive Neighbourhood Consensus
Papers with Code 아카이브
Semantic correspondence
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CompVis/cleandift
공식 코드https://github.com/EvelynFan/FAU
공식 코드https://github.com/GuiyuZhao/SGOR
공식 코드https://github.com/JiYuanFeng/MCTrans
공식 코드Multi-Compound Transformer for Accurate Biomedical Image Segmentation
https://github.com/Junyi42/geoaware-sc
공식 코드Telling Left from Right: Identifying Geometry-Aware Semantic Correspondence
https://github.com/Junyi42/sd-dino
공식 코드A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence
https://github.com/KU-CVLAB/CATs-PlusPlus
공식 코드CATs++: Boosting Cost Aggregation with Convolutions and Transformers
https://github.com/KU-CVLAB/NeMF
공식 코드Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence
https://github.com/Mathux/FROT
공식 코드https://github.com/QianWangX/VidSeg_diffusion
공식 코드Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models
https://github.com/RobertLuo1/HDC
공식 코드https://github.com/Seokju-Cho/Volumetric-Aggregation-Transformer
공식 코드https://github.com/Shaoli-Huang/SnapMix
공식 코드SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data
https://github.com/TimoBolkart/TEMPEH
공식 코드Instant Multi-View Head Capture through Learnable Registration
https://github.com/Visual-AI/Semantic-Correspondence
공식 코드Semantic Correspondence: Unified Benchmarking and a Strong Baseline
https://github.com/aiart-hdu/matebit
공식 코드Masked and Adaptive Transformer for Exemplar Based Image Translation
https://github.com/aim-uofa/diffews
공식 코드Unleashing the Potential of the Diffusion Model in Few-shot Semantic Segmentation
https://github.com/alxead/sfc
공식 코드https://github.com/bofang98/uatvr
공식 코드https://github.com/bupt-ai-cz/tcvc
공식 코드Temporal Consistent Automatic Video Colorization via Semantic Correspondence
https://github.com/clferrari/SLC-3DMM
공식 코드https://github.com/danaesavi/socialmedia-textimage-classification-auxlosses
공식 코드Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Tasks
https://github.com/dongdongdong666/CPGAN
공식 코드CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis
https://github.com/dvlab-research/ref-npr
공식 코드Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
https://github.com/dzh19990407/ppmn
공식 코드PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding
https://github.com/facebookresearch/vlpart
공식 코드https://github.com/gecgomes/Multilingual_IC_Eval
공식 코드Evaluation of Multilingual Image Captioning: How far can we get with CLIP models?
https://github.com/icannistraci/bootstrapping-ao
공식 코드https://github.com/jameslahm/scpnet
공식 코드Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels
https://github.com/jcy132/PatchGCL
공식 코드https://github.com/jiahao000/orl
공식 코드Unsupervised Object-Level Representation Learning from Scene Images
https://github.com/jingxiaolun/tc-mgc
공식 코드TC-MGC: Text-Conditioned Multi-Grained Contrastive Learning for Text-Video Retrieval
https://github.com/juhongm999/chm
공식 코드https://github.com/juhongm999/dhpf
공식 코드https://github.com/juhongm999/hpf
공식 코드Hyperpixel Flow: Semantic Correspondence with Multi-layer Neural Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.