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Xiang Cao

7 accepted papers

2023

Frequency and Scale Perspectives of Feature Extraction

ICASSP 2023accepted

Convolutional neural networks (CNNs) have achieved superior performance but still lack clarity about the nature and properties of feature extraction. In this paper, by analyzing the sensitivity of neural networks to frequencies and scales, we find that neural networks not only have low- and mediumfr…

Cited by 0SourceScholar
2023

Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragments

ICASSP 2023accepted

Neuromorphic vision sensors (event cameras) are inherently suitable for spiking neural networks (SNNs) and provide novel neuromorphic vision data for this biomimetic model. Due to the spatiotemporal characteristics, novel data augmentations are required to process the unconventional visual signals o…

Cited by 0SourceScholar
2023

Training Robust Spiking Neural Networks with Viewpoint Transform and Spatiotemporal Stretching

ICASSP 2023accepted

Neuromorphic vision sensors (event cameras) simulate biological visual perception systems and have the advantages of high temporal resolution, less data redundancy, low power consumption, and large dynamic range. Since both events and spikes are modeled from neural signals, event cameras are inheren…

Cited by 0SourceScholar
2023

Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons

ICASSP 2023accepted

As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term…

Cited by 0SourceScholar
2022

Dynamic Multi-Scale Loss Balance for Object Detection

ICASSP 2022accepted

It is a common paradigm in object detection frameworks to perform multi-scale detection. However, each scale is treated equally during training. In this paper, we carefully study the objective imbalance of multi-scale detector training. We argue that the loss in each scale is neither equally importa…

Cited by 0SourceScholar
2022

Kernel Estimation Network for Blind Super-Resolution

ICASSP 2022accepted

Existing super-resolution (SR) methods commonly assume that the degradation kernels are fixed and known (e.g., bicubic downsampling or single Gaussian blurring kernel). However, these methods suffer a severe performance drop when the real degradations deviate from this assumption. To address this is…

Cited by 0SourceScholar
2022

Multi-Scale Reinforcement Learning Strategy for Object Detection

ICASSP 2022accepted

Feature Pyramid Network (FPN) has become a common detection paradigm by improving multi-scale features with strong semantics. However, most FPN-based methods typically treat each feature map equally and sum the loss without distinction, which might lead to suboptimal overall performance. In this pap…

Cited by 0SourceScholar