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Found 8 papers, 2 papers with code
FAST3D: Flow-Aware Self-Training for 3D Object Detectors
In the field of autonomous driving, self-training is widely applied to mitigate distribution shifts in LiDAR-based 3D object detectors.
Robustness of Object Detectors in Degrading Weather Conditions
State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions.
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data
We introduce a simple yet effective fusion method of LiDAR and RGB data to segment LiDAR point clouds.
MURAUER: Mapping Unlabeled Real Data for Label AUstERity
In this work, we remove this requirement by learning to map from the features of real data to the features of synthetic data mainly using a large amount of synthetic and unlabeled real data.
Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems
Furthermore due to the design of the network, at test time only the 2D camera images are required for classification which enables the usage in portable computer vision systems.
Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly
To this end, we divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem.
Ranked #13 on
Image Retrieval
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BIER - Boosting Independent Embeddings Robustly
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings.
Grid Loss: Detecting Occluded Faces
Detection of partially occluded objects is a challenging computer vision problem.
