https://github.com/RaghavvGoel/marl_transfer_new
Learning Transferable Cooperative Behavior in Multi-Agent Teams
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/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
MADation: Face Morphing Attack Detection with Foundation Models
https://github.com/ShuoZhangXJTU/PEDP
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
https://github.com/SonuDixit/gComm
Zero-Shot Generalization using Intrinsically Motivated Compositional Emergent Protocols
https://github.com/THUDM/P-tuning
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
https://github.com/XavierJiezou/KTDA
Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation
https://github.com/XiaochenWang-PSU/FedKIM
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
https://github.com/abril4416/kgen_vqa
From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models
https://github.com/ai-agi/hicl
https://github.com/ai21labs/factor
https://github.com/aleksispi/airloc
https://github.com/allenai/beacon
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
https://github.com/allenai/objaverse-xl
https://github.com/aman313/SCAN
https://github.com/anair13/bullet-manipulation-affordances
What Can I Do Here? Learning New Skills by Imagining Visual Affordances
https://github.com/anguyen8/TAB
Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence
https://github.com/arcadelab/fastsam3d_slicer
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
https://github.com/arkilpatel/compositional-generalization-seq2seq
https://github.com/bentherien/mu_learned_optimization
$μ$LO: Compute-Efficient Meta-Generalization of Learned Optimizers
https://github.com/buoyancy99/sap
Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
https://github.com/bytedance/gr-1
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation
https://github.com/cake-lab/Mobile-AR-Depth-Estimation
ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
https://github.com/caoyunkang/groundedsam-zero-shot-anomaly-detection
Segment Any Anomaly without Training via Hybrid Prompt Regularization
https://github.com/chaitjo/graph-convnet-tsp
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
https://github.com/chaitjo/learning-paradigms-for-tsp
Learning the Travelling Salesperson Problem Requires Rethinking Generalization
https://github.com/cheoljun95/sdhubert
https://github.com/craftjarvis/mc-planner
https://github.com/crockwell/cap3d
https://github.com/damaggu/tadp
Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
https://github.com/dvlab-research/VisionReasoner
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/eachsheep/shortcutsbench
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
https://github.com/envision-research/lotus
Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
https://github.com/facebookresearch/metaicl
Multitask Prompted Training Enables Zero-Shot Task Generalization
https://github.com/google-research/language
Consistency by Agreement in Zero-shot Neural Machine Translation
https://github.com/hisfog/SfMNeXt-Impl
SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimation
https://github.com/hiyouga/easyr1
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.