https://github.com/momo1986/robust_sam_iv
공식 코드Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving
Papers with Code 아카이브
Autonomous Driving
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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Autonomous driving is the task of driving a vehicle 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: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mona4399/featuremixing
공식 코드Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
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/RRPN
공식 코드RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles
https://github.com/mrottmann/MetaSeg
공식 코드Detection of False Positive and False Negative Samples in Semantic Segmentation
https://github.com/msscao/acdepth
공식 코드Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather
https://github.com/mystorm16/bsh-det3d
공식 코드BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap
https://github.com/nailperry-zd/paddleseg-trianglenet
공식 코드TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency
https://github.com/naomiex/mdha
공식 코드MDHA: Multi-Scale Deformable Transformer with Hybrid Anchors for Multi-View 3D Object Detection
https://github.com/naufalso/adversarial-manhole
공식 코드https://github.com/naver/kapture-localization
공식 코드https://github.com/nazat28/xai-based-feature-ensemble-for-enhanced-anomaly-detection-in-autonomous-driving-systems
공식 코드XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems
https://github.com/neeharperi/futuredet
공식 코드https://github.com/neu-real/streammos
공식 코드StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memory
https://github.com/neurai-lab/cude-monodepthcl
공식 코드Continual Learning of Unsupervised Monocular Depth from Videos
https://github.com/neurai-lab/mimdepth
공식 코드Image Masking for Robust Self-Supervised Monocular Depth Estimation
https://github.com/neurai-lab/mmc
공식 코드https://github.com/nexer8/se3d
공식 코드SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
https://github.com/nicholasli1995/pcp-mv
공식 코드https://github.com/nickstheisen/hyperseg
공식 코드https://github.com/nicolamarinello/offsetocc
공식 코드Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes
https://github.com/nightmare-n/pvt-ssd
공식 코드PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer
https://github.com/nilakshdas/skelevision
공식 코드SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
https://github.com/nish03/FuseVis
공식 코드FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization
https://github.com/nish03/ffs
공식 코드Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
https://github.com/niveditarufus/CMSVG
공식 코드Cosine meets Softmax: A tough-to-beat baseline for visual grounding
https://github.com/nkondapa/visualknowledgetracing
공식 코드https://github.com/nnanhuang/s3gaussian
공식 코드$\textit{S}^3$Gaussian: Self-Supervised Street Gaussians for Autonomous Driving
https://github.com/noboevbo/ehpi_action_recognition
공식 코드Simple yet efficient real-time pose-based action recognition
https://github.com/nonmy/fdct
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.