https://github.com/JunaidCS032/MOTBeyondPixels
Accurate Single Stage Detector Using Recurrent Rolling Convolution
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.
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Towards Lightweight Lane Detection by Optimizing Spatial Embedding
From Recognition to Prediction: Analysis of Human Action and Trajectory Prediction in Video
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
PointPillars: Fast Encoders for Object Detection from Point Clouds
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation
RoadMap: A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.