3 accepted papers
Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally kn…
Most of the federated learning techniques are limited to homogeneous model fusion. With the rapid growth of smart applications on resource-constrained edge devices, it becomes a barrier to accommodate their heterogeneous computing power and memory in the real world. Federated Distillation is a promi…
In this paper, we study a new domain adaptation setting on camera networks, namely Multi-View Domain Adaptive Object Detection (MVDA-OD), in which labeled source data is unavailable in the target adaptation process and target data is captured from multiple overlapping cameras. In such a challenging…