5 papers
Distributed Semantic Segmentation With Improved Rate-Distortion Trade-Off
Danish Nazir, Timo Bartels, Thorsten Bagdonat +1
Distributed deep neural networks (DNNs) for dense perception tasks such as semantic segmentation execute an encoder DNN on edge devices, and a decoder DNN typically on a large-scal…
StaticSegFormer: An Efficient High-Performance Semantic Segmentation Based on Static Structured Pruning
Timo Bartels, Danish Nazir, Jan Piewek +2
Structured pruning enhances the efficiency of deep neural networks (DNNs) by eliminating groups of parameters during inference. Previous methods mostly reduce computational complex…
Efficient Multi-View 3D Object Detection by Dynamic Token Selection and Fine-Tuning
Danish Nazir, Antoine Hanna-Asaad, Lucas Görnhardt +3
Existing multi-view three-dimensional (3D) object detection approaches widely adopt large-scale pre-trained vision transformer (ViT)-based foundation models as backbones, being com…
An Efficient Semantic Segmentation Decoder for In-Car or Distributed Applications
Danish Nazir, Gowtham Sai Inti, Timo Bartels +3
Modern automotive systems leverage deep neural networks (DNNs) for semantic segmentation and operate in two key application areas: (1) In-car, where the DNN solely operates in the…
Distributed Semantic Segmentation with Efficient Joint Source and Task Decoding
Danish Nazir, Timo Bartels, Jan Piewek +2
Distributed computing in the context of deep neural networks (DNNs) implies the execution of one part of the network on edge devices and the other part typically on a large-scale c…