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Found 11 papers, 2 papers with code
Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023 OmniObject3D Challenge
To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation.
MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving
This set-level latent code is an expression of the optimal 3D shape in the implicit space, and can be subsequently decoded to a continuous SDF of the vehicle.
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer
In this work, we propose the first attempt to explore multi-domain learning and generalization for LiDAR-based 3D object detection.
Exploring Semantic Attributes from A Foundation Model for Federated Learning of Disjoint Label Spaces
To resolve this problem, federated learning has been introduced to transfer knowledge across multiple sources (clients) with non-shared data while optimising a globally generalised central model (server).
Self-Supervised Consistent Quantization for Fully Unsupervised Image Retrieval
In global consistent quantization, we employ contrastive learning for both embedding and quantized representations and fuses these representations for consistent contrastive regularization between instances.
Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
However, due to the significant imbalance between the amount of annotated data in the source and target domains, usually only the target distribution is aligned to the source domain, leading to adapting unnecessary source specific knowledge to the target domain, i. e., biased domain adaptation.
Decentralised Person Re-Identification with Selective Knowledge Aggregation
This helps to preserve model personalisation knowledge on each local client domain and learn instance-specific information.
Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation
Our base model consists of a domain-invariant feature extractor and an ensemble of domain-specific classifiers.
Domain Generalization
Multi-Source Unsupervised Domain Adaptation
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Striking a Balance Between Stability and Plasticity for Class-Incremental Learning
With the reformulated baseline, we present two new approaches to CIL by learning class-independent knowledge and multi-perspective knowledge, respectively.
Decentralised Learning from Independent Multi-Domain Labels for Person Re-Identification
Specifically, each local client receives global model updates from the server and trains a local model using its local data independent from all the other clients.
Peer Collaborative Learning for Online Knowledge Distillation
Meanwhile, we employ the temporal mean model of each peer as the peer mean teacher to collaboratively transfer knowledge among peers, which helps each peer to learn richer knowledge and facilitates to optimise a more stable model with better generalisation.
