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Found 6 papers, 1 papers with code
KLIF: An optimized spiking neuron unit for tuning surrogate gradient slope and membrane potential
In this paper, we propose a novel k-based leaky Integrate-and-Fire (KLIF) neuron model to improve the learning ability of SNNs.
A noise based novel strategy for faster SNN training
With spike-based BP, training high-precision SNNs typically consumes dozens of times more computational resources and time than their ANN counterparts.
Spiking sampling network for image sparse representation and dynamic vision sensor data compression
Sparse representation has attracted great attention because it can greatly save storage re- sources and find representative features of data in a low-dimensional space.
Adversarial Defense via Neural Oscillation inspired Gradient Masking
Furthermore, we propose a defense method that changes model's gradients by replacing the form of oscillation, which hides the original training gradients and confuses the attacker into using gradients of 'fake' neurons to generate invalid adversarial samples.
FocalClick: Towards Practical Interactive Image Segmentation
To make the model work with preexisting masks, we formulate a sub-task termed Interactive Mask Correction, and propose Progressive Merge as the solution.
Ranked #1 on
Interactive Segmentation
on GrabCut
(using extra training data)
Conditional Diffusion for Interactive Segmentation
In click-based interactive segmentation, the mask extraction process is dictated by positive/negative user clicks; however, most existing methods do not fully exploit the user cues, requiring excessive numbers of clicks for satisfactory results.
