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Found 5 papers, 2 papers with code
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow
This is mainly because the correlation volume, the basis of pixel matching, is computed as the dot product of the convolutional features of the two images.
Ranked #9 on
Optical Flow Estimation
on KITTI 2015 (train)
DTNN: Energy-efficient Inference with Dendrite Tree Inspired Neural Networks for Edge Vision Applications
DTNN achieved significant energy saving (19. 4X and 64. 9X improvement on ResNet-18 and VGG-11 with ImageNet, respectively) with negligible loss of accuracy.
Medical Image Segmentation Using Squeeze-and-Expansion Transformers
Medical image segmentation is important for computer-aided diagnosis.
Ranked #1 on
Brain Tumor Segmentation
on BRATS 2019
Efficient Spiking Neural Networks with Radix Encoding
Traditionally, a spike train needs around one thousand time steps to approach similar accuracy as its ANN counterpart.
RCT: Resource Constrained Training for Edge AI
For example, quantisation-aware training (QAT) method involves two copies of model parameters, which is usually beyond the capacity of on-chip memory in edge devices.
