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Found 6 papers, 3 papers with code
ProxyBNN: Learning Binarized Neural Networks via Proxy Matrices
The matrix composed of basis vectors is referred to as the proxy matrix, and auxiliary variables serve as the coefficients of this linear combination.
SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World
In this paper, we propose SpikingNeRF, which aligns the radiance ray with the temporal dimension of SNN, to naturally accommodate the SNN to the reconstruction of Radiance Fields.
$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value.
GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks
In this paper, we propose GLIF, a unified spiking neuron, to fuse different bio-features in different neuronal behaviors, enlarging the representation space of spiking neurons.
Location-aware Upsampling for Semantic Segmentation
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks.
Ranked #35 on
Semantic Segmentation
on PASCAL Context
A System-Level Solution for Low-Power Object Detection
As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6. 9W (with display) with an mAP of 66. 4 verified on the PASCAL VOC 2012 dataset.
