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Zero-shot Generalization
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kfu02/jaxmarl
공식 코드Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination
https://github.com/kidswithtokens/one-prompt
공식 코드https://github.com/kimsekeun/medivista-sam
공식 코드MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
https://github.com/kit-mrt/future-motion
공식 코드Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers
https://github.com/koorye/dept
공식 코드https://github.com/kowshikthopalli/DREAME
공식 코드Automated Domain Discovery from Multiple Sources to Improve Zero-Shot Generalization
https://github.com/leggedrobotics/learning_quadrupedal_locomotion_over_challenging_terrain_supplementary
공식 코드https://github.com/lezhang7/retrieval_mugi
공식 코드Exploring the Best Practices of Query Expansion with Large Language Models
https://github.com/liliu-avril/Awesome-Segment-Anything
공식 코드https://github.com/liufanfanlff/robouniview
공식 코드RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation
https://github.com/liuxueyi/reasonplan
공식 코드ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving
https://github.com/llmmove/llmmove
공식 코드Where to Move Next: Zero-shot Generalization of LLMs for Next POI Recommendation
https://github.com/lnairgt/clip-distillation
공식 코드CLIP-Embed-KD: Computationally Efficient Knowledge Distillation Using Embeddings as Teachers
https://github.com/luomingshuang/m3gpt
공식 코드M$^3$GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation
https://github.com/lwq20020127/q-insight
공식 코드Q-Insight: Understanding Image Quality via Visual Reinforcement Learning
https://github.com/mahesh-ak/SktQA
공식 코드A Case Study of Cross-Lingual Zero-Shot Generalization for Classical Languages in LLMs
https://github.com/marekstrong/Zero-NatVer
공식 코드Zero-Shot Fact Verification via Natural Logic and Large Language Models
https://github.com/masterxiong/universal-morphology-control
공식 코드Distilling Morphology-Conditioned Hypernetworks for Efficient Universal Morphology Control
https://github.com/maxzanella/mta
공식 코드https://github.com/meize0729/rsbuilding
공식 코드https://github.com/miccunifi/kdpl
공식 코드Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
https://github.com/microsoft/VideoX/tree/master/X-CLIP
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/microsoft/mpnode.jl
공식 코드https://github.com/microsoft/unilm
공식 코드https://github.com/microsoft/videox
공식 코드Expanding Language-Image Pretrained Models for General Video Recognition
https://github.com/mikewangwzhl/zemi
공식 코드Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
https://github.com/mila-iqia/skipper
공식 코드https://github.com/miosipof/lie-dasr
공식 코드Dysarthria Normalization via Local Lie Group Transformations for Robust ASR
https://github.com/ml-research/semantic-image-editing
공식 코드https://github.com/mrflogs/SHIP
공식 코드Improving Zero-Shot Generalization for CLIP with Synthesized Prompts
https://github.com/mtangemann/motion_energy_segmentation
공식 코드https://github.com/multimodallearning/samcarriestheburden
공식 코드SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation
https://github.com/mvrl/geosynth
공식 코드GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis
https://github.com/mvrl/gomaa-geo
공식 코드https://github.com/mzhaoshuai/rlcf
공식 코드Test-Time Adaptation with CLIP Reward for Zero-Shot Generalization in Vision-Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.