https://github.com/william-wang618/m2pt
공식 코드M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Papers with Code 아카이브
Zero-shot Generalization
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/william-wang618/mmpt-emnlp2024
공식 코드M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
https://github.com/wujunde/promptunet
공식 코드https://github.com/wuzijian1997/sis-pt-sam
공식 코드https://github.com/xuetianci/pacit
공식 코드PACIT: Unlocking the Power of Examples for Better In-Context Instruction Tuning
https://github.com/yiqings/rvt
공식 코드https://github.com/yk7333/taskallocation
공식 코드A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation
https://github.com/ylkuo/ltl-zero-shot
공식 코드Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
https://github.com/ylong4/dfzsl
공식 코드https://github.com/yooceii/Rotation-Translation-and-Cropping-for-Zero-Shot-Generalization
공식 코드Rotation, Translation, and Cropping for Zero-Shot Generalization
https://github.com/younggyoseo/trajectory_mcl
공식 코드Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
https://github.com/yousia/lgr_ad
공식 코드https://github.com/yuliangguo/depth_any_camera
공식 코드Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera
https://github.com/yvanyin/metric3d
공식 코드Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
https://github.com/zhangyiqun018/genome
공식 코드Nature-Inspired Population-Based Evolution of Large Language Models
https://github.com/zhaohengz/llamp
공식 코드Large Language Models are Good Prompt Learners for Low-Shot Image Classification
https://github.com/zhenyuw16/unidetector
공식 코드Detecting Everything in the Open World: Towards Universal Object Detection
https://github.com/zhoujiahuan1991/cvpr2025-stop
공식 코드STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
https://github.com/zjunlp/iepile
공식 코드IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
https://github.com/zmhh-h/mote
공식 코드MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
https://github.com/zyh-uaiaaaa/generalizable-fer
공식 코드https://github.com/AMAP-ML/RealQA
https://github.com/DevSinghSachan/art
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/EkaterinaXie/LiteVAR
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
https://github.com/ForoughA/neuralMath
https://github.com/FoundationVision/Infinity
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
https://github.com/GMC-DRL/ConfigX
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
https://github.com/GR1-Manipulation/GR-1
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
https://github.com/GuidoManni/BodySLAM
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
https://github.com/H-Freax/ThinkGrasp
Efficient Alignment of Unconditioned Action Prior for Language-conditioned Pick and Place in Clutter
https://github.com/JanAthmer/Compositional-generalization-capabillity-of-Transformer
https://github.com/Lavreniuk/Delineate-Anything
Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery
https://github.com/Leeroo-AI/mergoo
Learning to Route Among Specialized Experts for Zero-Shot Generalization
https://github.com/Magicboomliu/Accelerator-Simple-Template
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/MindSpore-scientific/code-14/tree/main/The_RefinedWeb_Dataset_for_Falcon
https://github.com/PKU-AI-Edge/DGN/
Learning Transferable Cooperative Behavior in Multi-Agent Teams
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.