https://github.com/intelligent-vehicles/bevdriver
공식 코드BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BEVDriver: Leveraging BEV Maps in LLMs for Robust Closed-Loop Driving
TREC iKAT 2023: A Test Collection for Evaluating Conversational and Interactive Knowledge Assistants
PRET: Planning with Directed Fidelity Trajectory for Vision and Language Navigation
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
AlphaMaze: Enhancing Large Language Models' Spatial Intelligence via GRPO
AFANet: Adaptive Frequency-Aware Network for Weakly-Supervised Few-Shot Semantic Segmentation
Locate Anything on Earth: Advancing Open-Vocabulary Object Detection for Remote Sensing Community
Navigating Local Minima in Quantized Spiking Neural Networks
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning
Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information
CLEAR: Improving Vision-Language Navigation with Cross-Lingual, Environment-Agnostic Representations
EnvEdit: Environment Editing for Vision-and-Language Navigation
LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
Do Large Language Models Excel in Complex Logical Reasoning with Formal Language?
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
MedAgent-Pro: Towards Evidence-based Multi-modal Medical Diagnosis via Reasoning Agentic Workflow
Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge
Algorithmic Fairness Generalization under Covariate and Dependence Shifts Simultaneously
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.