Papers with Code 아카이브

Semi Supervised Learning for Image Captioning

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하위 태스크: 1
평가표: 3
보존된 평가 기록: 3

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/eai-lab/transpl

공식 코드

TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation

https://github.com/eliottthomas99/data_quest

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QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents

https://github.com/embar111/pgpc

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Pseudolabel guided pixels contrast for domain adaptive semantic segmentation

https://github.com/epfl-imos/simple-sfod

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Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights

https://github.com/facebookresearch/suncet

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Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples

https://github.com/fjchange/MIST_VAD

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MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection

https://github.com/fnozarian/ReliableStudent

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Reliable Student: Addressing Noise in Semi-Supervised 3D Object Detection

https://github.com/fqhank/FuSTAL

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Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization

https://github.com/franklinlingfeng/code-for-salcr

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Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID

https://github.com/franklinlingfeng/code_for_mult

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Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID

https://github.com/fy-vision/diga

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DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation

https://github.com/gallenszl/ucfnet

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Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching

https://github.com/gfmei/softclu

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Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding

https://github.com/ghostish/open3dsot

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An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds

https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-tta

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Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation

https://github.com/google-deepmind/semppl

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SemPPL: Predicting pseudo-labels for better contrastive representations

https://github.com/google-research/fixmatch

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FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

https://github.com/gorilla-lab-scut/ttac

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Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering

https://github.com/guanghaozhu663/astmatch

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AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image Segmentation

https://github.com/gudovskiy/al-fk-self-supervision

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Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision

https://github.com/hailanyi/cpd

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Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

https://github.com/hailanyi/virconv

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Virtual Sparse Convolution for Multimodal 3D Object Detection

https://github.com/hamperdredes/sood

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SOOD: Towards Semi-Supervised Oriented Object Detection

https://github.com/haoxuanli-thu/dbf-unet

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DBF-UNet: A Two-Stage Framework for Carotid Artery Segmentation with Pseudo-Label Generation

https://github.com/haru-zt/ddpls

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Density-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection

https://github.com/hazel-heejeong-nam/self-supervised-causal-disentanglement

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SCADI: Self-supervised Causal Disentanglement in Latent Variable Models

https://github.com/hengcai-nju/3d2dct

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3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching between 3D and 2D Networks

https://github.com/hf618/dtbs

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DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

https://github.com/hfut-cv/lmt-gp

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LMT-GP: Combined Latent Mean-Teacher and Gaussian Process for Semi-supervised Low-light Image Enhancement

https://github.com/hilab-git/fpl-plus

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FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation

https://github.com/hilab-git/srpl-sfda

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SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

https://github.com/hilab-git/upl-sfda

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UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation

https://github.com/hitsz-hlt/st-w-scorer-absa

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Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction

https://github.com/hku-medai/wsi-hgnn

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Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning

https://github.com/hlz0606/sspcm

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Semi-Supervised 2D Human Pose Estimation Driven by Position Inconsistency Pseudo Label Correction Module

https://github.com/hmllmh/rstc

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Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.