Search Results for author:
Found 7 papers, 2 papers with code
GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR
Multimodal synthetic data generation is crucial in domains such as autonomous driving, robotics, augmented/virtual reality, and retail.
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?
Current state-of-the-art (SOTA) 3D object detection methods often require a large amount of 3D bounding box annotations for training.
Learning Semantic Correspondence with Sparse Annotations
In this paper, we aim to address the challenge of label sparsity in semantic correspondence by enriching supervision signals from sparse keypoint annotations.
Burn After Reading: Online Adaptation for Cross-domain Streaming Data
In the context of online privacy, many methods propose complex privacy and security preserving measures to protect sensitive data.
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
To integrate the strengths of the two classifiers, we apply the well-established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS).
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities
As a result, using partial data channels to build a full representation of multi-modalities is clearly desired.
