https://github.com/xinwu98/sc-wls
공식 코드SC-wLS: Towards Interpretable Feed-forward Camera Re-localization
Papers with Code 아카이브
Test-time Adaptation
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xjtu-xgu/arpm
공식 코드Adversarial Reweighting with $α$-Power Maximization for Domain Adaptation
https://github.com/yangsenqiao/vida
공식 코드ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation
https://github.com/ycarobot/ttap
공식 코드From Question to Exploration: Test-Time Adaptation in Semantic Segmentation?
https://github.com/ycyinchao/rdvp-msd
공식 코드https://github.com/yfzhang114/adanpc
공식 코드AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
https://github.com/yhzhu99/tamer
공식 코드TAMER: A Test-Time Adaptive MoE-Driven Framework for EHR Representation Learning
https://github.com/yore0/ttdg-mgm
공식 코드https://github.com/yuanyige/tea
공식 코드https://github.com/yushu-li/hiltta
공식 코드Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection
https://github.com/yushuntang/dct
공식 코드Domain-Conditioned Transformer for Fully Test-time Adaptation
https://github.com/yushuntang/dpal
공식 코드Dual-Path Adversarial Lifting for Domain Shift Correction in Online Test-time Adaptation
https://github.com/yuyongcan/benchmark-tta
공식 코드Benchmarking Test-Time Adaptation against Distribution Shifts in Image Classification
https://github.com/yuyongcan/stamp
공식 코드STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
https://github.com/z1358/obao
공식 코드Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptation
https://github.com/zhangce01/DPE-CLIP
공식 코드Dual Prototype Evolving for Test-Time Generalization of Vision-Language Models
https://github.com/zhangmarvin/memo
공식 코드https://github.com/zhoujiahuan1991/cvpr2025-scap
공식 코드SCAP: Transductive Test-Time Adaptation via Supportive Clique-based Attribute Prompting
https://github.com/zhuoxiao-chen/mos
공식 코드MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection
https://github.com/zrguo/casp
공식 코드Bridging the Gap for Test-Time Multimodal Sentiment Analysis
https://github.com/zrguo/sumi
공식 코드Smoothing the Shift: Towards Stable Test-Time Adaptation under Complex Multimodal Noises
https://github.com/zwenyu/lccs
공식 코드Few-Shot Adaptation of Pre-Trained Networks for Domain Shift
https://github.com/zybeich/DPCore
공식 코드Dynamic Domains, Dynamic Solutions: DPCore for Continual Test-Time Adaptation
https://github.com/zybeich/ot-vp
공식 코드OT-VP: Optimal Transport-guided Visual Prompting for Test-Time Adaptation
https://github.com/zzzx1224/beyond-model-adaptation-at-test-time-papers
공식 코드https://github.com/ChenyangLEI/deep-video-prior
https://github.com/Hongbin98/MonoTTA
Fully Test-Time Adaptation for Monocular 3D Object Detection
https://github.com/JitengMu/A-SDF
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
https://github.com/Lily-Le/EcoTTA
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
https://github.com/RF5/CurveNet
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/baolp/demoireing_with_focused_and_defocused_images_pairs
Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
https://github.com/codelion/adaptive-classifier
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
https://github.com/cufctl/segmentation
https://github.com/dogyoonlee/RSMix
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/dogyoonlee/RSMix-official
Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
https://github.com/donghao51/moosa
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.