https://github.com/mirkozaff/aa-sgan
공식 코드AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.
Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.
<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mistlab/swarm-slam
공식 코드https://github.com/mistlab/vpr-calibration-and-uncertainty
공식 코드Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
https://github.com/mit-han-lab/bevfusion
공식 코드BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
https://github.com/mit-han-lab/efficientvit
공식 코드EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/mit-han-lab/llm-awq
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/mit-han-lab/spvnas
공식 코드Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/mit-han-lab/torchsparse
공식 코드https://github.com/mit-spark/perceptionmonitoring
공식 코드https://github.com/mit-wu-lab/mapf-autonomous-driving
공식 코드https://github.com/mitkina/EnvironmentPrediction
공식 코드Dynamic Environment Prediction in Urban Scenes using Recurrent Representation Learning
https://github.com/mjseong0414/CRT-Fusion
공식 코드CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection
https://github.com/mlatcl/fbp-vs-soa
공식 코드Towards better data discovery and collection with flow-based programming
https://github.com/mlmlab/mscn
공식 코드https://github.com/mlnjeongpark/vita-par
공식 코드https://github.com/mlvccn/pla_sm_videopred
공식 코드Pair-wise Layer Attention with Spatial Masking for Video Prediction
https://github.com/mmasana/OoD_Mining
공식 코드https://github.com/mmocking/riders
공식 코드https://github.com/modanesh/leader
공식 코드LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty
https://github.com/modestyachts/certainty_equiv_perception_control
공식 코드https://github.com/mohamedac29/P2AT
공식 코드P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation
https://github.com/mohamednagymostafa/dfr-fastmot
공식 코드https://github.com/mohamednagymostafa/kitti-mot.bench-evals
공식 코드https://github.com/mohamedyd/AutoCure
공식 코드AutoCure: Automated Tabular Data Curation Technique for ML Pipelines
https://github.com/momo1986/robust_sam_iv
공식 코드Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
https://github.com/mona4399/featuremixing
공식 코드Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
https://github.com/monjurulkarim/Crash_road_function_dataset
공식 코드https://github.com/monjurulkarim/DSTA
공식 코드A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents
https://github.com/moonlabiiserb/lidar-intensity-predictor
공식 코드https://github.com/moonseokha/streamocc
공식 코드https://github.com/morancyc/usnet
공식 코드Fast Road Segmentation via Uncertainty-aware Symmetric Network
https://github.com/mostafaelaraby/bacs-continual-semantic-segmentation
공식 코드https://github.com/mozhgan91/leo
공식 코드LEO: Boosting Mixture of Vision Encoders for Multimodal Large Language Models
https://github.com/mr-neko/jm3d
공식 코드JM3D & JM3D-LLM: Elevating 3D Understanding with Joint Multi-modal Cues
https://github.com/mrifkikurniawan/sslad
공식 코드https://github.com/mrnabati/CenterFusion
공식 코드CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.