Search Results for author: Zhengyu Ma

Found 16 papers, 8 papers with code

SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition

no code implementations21 Jun 2024 Liutao Yu, Liwei Huang, Chenlin Zhou, Han Zhang, Zhengyu Ma, Huihui Zhou, Yonghong Tian

To address this challenge, some researchers have turned to brain-inspired spiking neural networks (SNNs), such as recurrent SNNs and ANN-converted SNNs, leveraging their inherent temporal dynamics and energy efficiency.

Action Recognition Temporal Action Localization

Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection

2 code implementations16 May 2024 Tianhe Ren, Qing Jiang, Shilong Liu, Zhaoyang Zeng, Wenlong Liu, Han Gao, Hongjie Huang, Zhengyu Ma, Xiaoke Jiang, Yihao Chen, Yuda Xiong, Hao Zhang, Feng Li, Peijun Tang, Kent Yu, Lei Zhang

Empirical results demonstrate the effectiveness of Grounding DINO 1. 5, with the Grounding DINO 1. 5 Pro model attaining a 54. 3 AP on the COCO detection benchmark and a 55. 7 AP on the LVIS-minival zero-shot transfer benchmark, setting new records for open-set object detection.

Edge-computing Few-Shot Object Detection +2

Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods

1 code implementation6 May 2024 Chenlin Zhou, Han Zhang, Liutao Yu, Yumin Ye, Zhaokun Zhou, Liwei Huang, Zhengyu Ma, Xiaopeng Fan, Huihui Zhou, Yonghong Tian

In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.

QKFormer: Hierarchical Spiking Transformer using Q-K Attention

2 code implementations25 Mar 2024 Chenlin Zhou, Han Zhang, Zhaokun Zhou, Liutao Yu, Liwei Huang, Xiaopeng Fan, Li Yuan, Zhengyu Ma, Huihui Zhou, Yonghong Tian

ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation.

Enhancing EEG-to-Text Decoding through Transferable Representations from Pre-trained Contrastive EEG-Text Masked Autoencoder

no code implementations27 Feb 2024 Jiaqi Wang, Zhenxi Song, Zhengyu Ma, Xipeng Qiu, Min Zhang, Zhiguo Zhang

Reconstructing natural language from non-invasive electroencephalography (EEG) holds great promise as a language decoding technology for brain-computer interfaces (BCIs).

Brain Decoding EEG +2

Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli

no code implementations2 Jun 2023 Liwei Huang, Zhengyu Ma, Huihui Zhou, Yonghong Tian

Taken together, our work is the first to apply deep recurrent SNNs to model the mouse visual cortex under movie stimuli and we establish that these networks are competent to capture both static and dynamic representations and make contributions to understanding the movie information processing mechanisms of the visual cortex.

Action Recognition Image Classification +2

Auto-Spikformer: Spikformer Architecture Search

no code implementations1 Jun 2023 Kaiwei Che, Zhaokun Zhou, Zhengyu Ma, Wei Fang, Yanqi Chen, Shuaijie Shen, Li Yuan, Yonghong Tian

The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties.

Temporal Contrastive Learning for Spiking Neural Networks

no code implementations23 May 2023 Haonan Qiu, Zeyin Song, Yanqi Chen, Munan Ning, Wei Fang, Tao Sun, Zhengyu Ma, Li Yuan, Yonghong Tian

However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.

Contrastive Learning

Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies

1 code implementation NeurIPS 2023 Wei Fang, Zhaofei Yu, Zhaokun Zhou, Ding Chen, Yanqi Chen, Zhengyu Ma, Timothée Masquelier, Yonghong Tian

Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.

Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy

no code implementations25 Apr 2023 Yang Li, Wei Wang, Ming Wang, Chunmeng Dou, Zhengyu Ma, Huihui Zhou, Peng Zhang, Nicola Lepri, Xumeng Zhang, Qing Luo, Xiaoxin Xu, Guanhua Yang, Feng Zhang, Ling Li, Daniele Ielmini, Ming Liu

We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.

Binarization

Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network

1 code implementation24 Apr 2023 Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Zhengyu Ma, Han Zhang, Huihui Zhou, Yonghong Tian

Based on this residual design, we develop Spikingformer, a pure transformer-based spiking neural network.

Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse

1 code implementation9 Mar 2023 Liwei Huang, Zhengyu Ma, Liutao Yu, Huihui Zhou, Yonghong Tian

However, they highly simplify the computational properties of neurons compared to their biological counterparts.

The style transformer with common knowledge optimization for image-text retrieval

no code implementations1 Mar 2023 Wenrui Li, Zhengyu Ma, Jinqiao Shi, Xiaopeng Fan

The main module is the common knowledge adaptor (CKA) with both the style embedding extractor (SEE) and the common knowledge optimization (CKO) modules.

Image-text Retrieval Text Retrieval

A Unified Framework for Soft Threshold Pruning

1 code implementation25 Feb 2023 Yanqi Chen, Zhengyu Ma, Wei Fang, Xiawu Zheng, Zhaofei Yu, Yonghong Tian

In this work, we reformulate soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), a classic method from the fields of sparse recovery and compressed sensing.

Scheduling

Mining Data from the Congressional Record

no code implementations3 Jun 2019 Zhengyu Ma, Tianjiao Qi, James Route, Amir Ziai

We propose a data storage and analysis method for using the US Congressional record as a policy analysis tool.

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