Search Results for author:
Found 7 papers, 6 papers with code
Multi-conditioned Graph Diffusion for Neural Architecture Search
To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures.
Ranked #1 on
Neural Architecture Search
on NAS-Bench-301
SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation
Few-shot domain adaptation mitigates this issue by adapting deep neural networks pre-trained on the source domain to the target domain using a randomly selected and annotated support set from the target domain.
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
In this paper, we argue that the random selection of unlabelled training target images to be annotated and included in the support set may not enable an effective fine-tuning process, so we propose a new approach to optimise this image selection process.
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consuming process.
A Spatio-Temporal Multilayer Perceptron for Gesture Recognition
Gesture recognition is essential for the interaction of autonomous vehicles with humans.
Ranked #1 on
Skeleton Based Action Recognition
on Drive&Act
Lightweight Monocular Depth Estimation through Guided Decoding
Similarly, on the KITTI dataset, inference is possible with up to 23. 7 fps on the Jetson Nano and 102. 9 fps on the Xavier NX.
Few-Shot Microscopy Image Cell Segmentation
Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem.
