Search Results for author:
Found 6 papers, 2 papers with code
Deep Hashing for Compact Binary Codes Learning
In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for large scale visual search.
Simultaneous Local Binary Feature Learning and Encoding for Face Recognition
In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) method for face recognition.
Cross-Modal Deep Variational Hashing
In this paper, we propose a cross-modal deep variational hashing (CMDVH) method to learn compact binary codes for cross-modality multimedia retrieval.
nuScenes: A multimodal dataset for autonomous driving
Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar.
Ranked #312 on
3D Object Detection
on nuScenes
(using extra training data)
AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation
In this paper, we present an Assertion-based Multi-View Fusion network (AMVNet) for LiDAR semantic segmentation which aggregates the semantic features of individual projection-based networks using late fusion.
Ranked #14 on
LIDAR Semantic Segmentation
on nuScenes
ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
We present ConDA, a concatenation-based domain adaptation framework for LiDAR segmentation that: 1) constructs an intermediate domain consisting of fine-grained interchange signals from both source and target domains without destabilizing the semantic coherency of objects and background around the ego-vehicle; and 2) utilizes the intermediate domain for self-training.
