https://github.com/1hb6s7t/MindSpore-Uncertainty_Calibration_Object_Detection-
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
Exploring Visual Context for Weakly Supervised Person Search
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous Vehicles
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
PointPillars: Fast Encoders for Object Detection from Point Clouds
SRCN3D: Sparse R-CNN 3D for Compact Convolutional Multi-View 3D Object Detection and Tracking
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.