https://github.com/graph-com/llm_bp
공식 코드Model Generalization on Text Attribute Graphs: Principles with Large Language Models
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/guobaoxiao/comprompter
공식 코드https://github.com/gurayozgur/madation
공식 코드MADation: Face Morphing Attack Detection with Foundation Models
https://github.com/gyhandy/hierarchy-clip
공식 코드Improving Zero-shot Generalization and Robustness of Multi-modal Models
https://github.com/haidiazaman/rl-concept-learning-project
공식 코드Compositional Learning of Visually-Grounded Concepts Using Reinforcement
https://github.com/haochenheheda/lvvis
공식 코드https://github.com/haoosz/BiGR
공식 코드https://github.com/haoyizhu/pointcloudmatters
공식 코드Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
https://github.com/haoyizhu/realrobot
공식 코드Point Cloud Matters: Rethinking the Impact of Different Observation Spaces on Robot Learning
https://github.com/hhyhrhy/owmm-agent
공식 코드OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
https://github.com/hkuds/opencity
공식 코드OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
https://github.com/hkuds/recgpt
공식 코드https://github.com/hp-l33/ARPG
공식 코드Autoregressive Image Generation with Randomized Parallel Decoding
https://github.com/huage001/clear
공식 코드CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers Up
https://github.com/hujiecpp/pe3r
공식 코드https://github.com/hyf1530588058/HeteroMorpheus
공식 코드HeteroMorpheus: Universal Control Based on Morphological Heterogeneity Modeling
https://github.com/iliaschalkidis/lmtc-eurlex57k
공식 코드An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels
https://github.com/infzhou/sam-pd
공식 코드SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising
https://github.com/ink-usc/crosstaskmoe
공식 코드Eliciting and Understanding Cross-Task Skills with Task-Level Mixture-of-Experts
https://github.com/insta360-research-team/defom-stereo
공식 코드https://github.com/intelligent-reliable-autonomous-systems/gcrs-expert-abstractions
공식 코드Learning with Expert Abstractions for Efficient Multi-Task Continuous Control
https://github.com/isee-laboratory/diffuir
공식 코드Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model
https://github.com/isl-org/ZoeDepth
공식 코드ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth
https://github.com/jaggbow/magnet
공식 코드https://github.com/jahuerta92/authorship-embeddings
공식 코드https://github.com/jameszhou-gl/gpt-4v-distribution-shift
공식 코드How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
https://github.com/jarintotiondin/ziragroundingdino
공식 코드Zero-shot Generalizable Incremental Learning for Vision-Language Object Detection
https://github.com/jaromiru/sr-drl
공식 코드https://github.com/jasonppy/syllable-discovery
공식 코드https://github.com/jerry-boss/drvd-bench
공식 코드DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?
https://github.com/jiaming-zhou/X-ICM
공식 코드Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization
https://github.com/jiwanchung/esper
공식 코드https://github.com/joonkeekim/Instructive-Decoding
공식 코드https://github.com/jspenmar/slowtv_monodepth
공식 코드Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
https://github.com/junda24/monster
공식 코드https://github.com/kaist-lklab/gap
공식 코드Gradient Ascent Post-training Enhances Language Model Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.