Author name cluster

Shibo Zhou

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6 papers

2 author rows

AAAI Conference 2025 Conference Paper

Efficient 3D Recognition with Event-driven Spike Sparse Convolution

Xuerui Qiu
Man Yao
Jieyuan Zhang
Yuhong Chou
Ning Qiao
Shibo Zhou
Bo Xu
Guoqi Li

Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be well-suited for processing them. However, when applying SNNs to point clouds, they often exhibit limited performance and fewer application scenarios. We attribute this to inappropriate preprocessing and feature extraction methods. To address this issue, we first introduce the Spike Voxel Coding (SVC) scheme, which encodes the 3D point clouds into a sparse spike train space, reducing the storage requirements and saving time on point cloud preprocessing. Then, we propose a Spike Sparse Convolution (SSC) model for efficiently extracting 3D sparse point cloud features. Combining SVC and SSC, we design an efficient 3D SNN backbone (E-3DSNN), which is friendly with neuromorphic hardware. For instance, SSC can be implemented on neuromorphic chips with only minor modifications to the addressing function of vanilla spike convolution. Experiments on ModelNet40, KITTI, and Semantic KITTI datasets demonstrate that E-3DSNN achieves state-of-the-art (SOTA) results with remarkable efficiency. Notably, our E-3DSNN (1.87M) obtained 91.7% top-1 accuracy on ModelNet40, surpassing the current best SNN baselines (14.3M) by 3.0%. To our best knowledge, it is the first direct training 3D SNN backbone that can simultaneously handle various 3D computer vision tasks (e.g., classification, detection, and segmentation) with an event-driven nature.

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ICRA Conference 2025 Conference Paper

HSRL: A Hierarchical Control System Based on Spiking Deep Reinforcement Learning for Robot Navigation

Bo Yang
Shibo Zhou
Chaohui Lin
Qingao Chai
Rui Yan 0005
De Ma
Gang Pan 0001
Huajin Tang

Reinforcement Learning (RL) has shown promise in robotic navigation tasks, yet applying it to real-world environments remains challenging due to dynamic complexities and the need for dynamically feasible actions. We propose a hierarchical control framework based on Spiking Deep Reinforcement Learning (SDRL) for robust robot navigation in real environments. Our approach utilizes a two-layer architecture: a high-level decision layer powered by a Spiking GRU network for handling partially observable environments, and a low-level executive layer employing Continuous Attractor Neural Networks (CANNs) to ensure precise and continuous actions. This hierarchical structure allows real-time decisionmaking that respects the physical constraints of the robot. Experimental results show that our method adapts effectively to new environments without fine-tuning and surpasses existing methods in performance. We also explore the implementation on the Darwin3 chip, paving the way for biologically inspired motion control in future robotic applications.

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NeurIPS Conference 2025 Conference Paper

Spiking Neural Networks Need High-Frequency Information

Yuetong Fang
Deming Zhou
Ziqing Wang
Hongwei Ren
zeng zecui
Lusong Li
Shibo Zhou
Renjing Xu

Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind that of artificial neural networks, often assumed to result from information loss caused by sparse and binary activations. In this work, we challenge this long-standing assumption and reveal a previously overlooked frequency bias: spiking neurons inherently suppress high-frequency components and preferentially propagate low-frequency information. This frequency-domain imbalance, we argue, is the root cause of degraded feature representation in SNNs. Empirically, on Spiking Transformers, adopting Avg-Pooling (low-pass) for token mixing lowers performance to 76. 73% on Cifar-100, whereas replacing it with Max-Pool (high-pass) pushes the top-1 accuracy to 79. 12%. Accordingly, we introduce Max-Former that restores high-frequency signals through two frequency-enhancing operators: (1) extra Max-Pool in patch embedding, and (2) Depth-Wise Convolution in place of self-attention. Notably, Max-Former attains 82. 39% top-1 accuracy on ImageNet using only 63. 99M parameters, surpassing Spikformer (74. 81%, 66. 34M) by +7. 58%. Extending our insight beyond transformers, our Max-ResNet-18 achieves state-of-the-art performance on convolution-based benchmarks: 97. 17% on CIFAR-10 and 83. 06% on CIFAR-100. We hope this simple yet effective solution inspires future research to explore the distinctive nature of spiking neural networks. Code is available: https: //github. com/bic-L/MaxFormer.

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NeurIPS Conference 2025 Conference Paper

Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models

Jun Ling
Yao Qi
Tao Huang
Shibo Zhou
Yanqin Huang
Jiang Yang
Ziqi Song
Ying Zhou

In this work, we address the task of table image to LaTeX code generation, with the goal of automating the reconstruction of high-quality, publication-ready tables from visual inputs. A central challenge of this task lies in accurately handling complex tables—those with large sizes, deeply nested structures, and semantically rich or irregular cell content—where existing methods often fail. We begin with a comprehensive analysis, identifying key challenges and highlighting the limitations of current evaluation protocols. To overcome these issues, we propose a reinforced multimodal large language model (MLLM) framework, where a pre-trained MLLM is fine-tuned on a large-scale table-to-LaTeX dataset. To further improve generation quality, we introduce a dual-reward reinforcement learning strategy based on Group Relative Policy Optimization (GRPO). Unlike standard approaches that optimize purely over text outputs, our method incorporates both a structure-level reward on LaTeX code and a visual fidelity reward computed from rendered outputs, enabling direct optimization of the visual output quality. We adopt a hybrid evaluation protocol combining TEDS-Structure and CW-SSIM, and show that our method achieves state-of-the-art performance, particularly on structurally complex tables, demonstrating the effectiveness and robustness of our approach.

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NeurIPS Conference 2024 Conference Paper

Spiking Neural Network as Adaptive Event Stream Slicer

Jiahang Cao
Mingyuan Sun
Ziqing Wang
Hao Cheng
Qiang Zhang
Shibo Zhou
Renjing Xu

Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based algorithms rely on splitting the events into fixed groups, resulting in the omission of crucial temporal information, particularly when dealing with diverse motion scenarios (e. g. , high/low speed). In this work, we propose SpikeSlicer, a novel-designed event processing framework capable of splitting events stream adaptively. SpikeSlicer utilizes a low-energy spiking neural network (SNN) to trigger event slicing. To guide the SNN to fire spikes at optimal time steps, we propose the Spiking Position-aware Loss (SPA-Loss) to modulate the neuron's state. Additionally, we develop a Feedback-Update training strategy that refines the slicing decisions using feedback from the downstream artificial neural network (ANN). Extensive experiments demonstrate that our method yields significant performance improvements in event-based object tracking and recognition. Notably, SpikeSlicer provides a brand-new SNN-ANN cooperation paradigm, where the SNN acts as an efficient, low-energy data processor to assist the ANN in improving downstream performance, injecting new perspectives and potential avenues of exploration.

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AAAI Conference 2021 Conference Paper

Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance

Shibo Zhou
Xiaohua Li
Ying Chen
Sanjeev T. Chandrasekaran
Arindam Sanyal

Spiking neural network (SNN) is promising but the development has fallen far behind conventional deep neural networks (DNNs) because of difficult training. To resolve the training problem, we analyze the closed-form input-output response of spiking neurons and use the response expression to build abstract SNN models for training. This avoids calculating membrane potential during training and makes the direct training of SNN as efficient as DNN. We show that the nonleaky integrate-and-fire neuron with single-spike temporalcoding is the best choice for direct-train deep SNNs. We develop an energy-efficient phase-domain signal processing circuit for the neuron and propose a direct-train deep SNN framework. Thanks to easy training, we train deep SNNs under weight quantizations to study their robustness over low-cost neuromorphic hardware. Experiments show that our direct-train deep SNNs have the highest CIFAR-10 classification accuracy among SNNs, achieve ImageNet classification accuracy within 1% of the DNN of equivalent architecture, and are robust to weight quantization and noise perturbation.

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