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De Ma

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13 papers

2 author rows

AAAI Conference 2026 Conference Paper

SNN-Driven Event-Based Flow and Rotation Estimation with SO(3) Refinement

Ruimin Sun
Haoran Xu
De Ma

Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient event-based vision by leveraging sparse, temporally precise spikes. We propose a directly trained, fully spiking model for optical flow estimation, featuring a novel Spike GRU and membrane potential carryover for improved temporal modeling. On the DSEC-Flow benchmark, our model achieves competitive accuracy while reducing energy consumption by 42.88× over EV-FlowNet and 38× over TIDNet. Building on the predicted motion field, we infer camera rotation and, to the best of our knowledge, are the first to construct panoramic event images from SNN-based flow. We further introduce an optional unsupervised SO(3) refinement step that improves rotation accuracy by maximizing panorama consistency—without IMU or pose supervision. Our results achieve comparable visual quality to CMax-SLAM, showing that SNNs can enable fast and high-level spatial perception using only event-based input.

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AAAI Conference 2026 Conference Paper

S³: Spiking Neurons as an Isolating Segmenter for Brain Signal Decoding

Qian Zheng
Ming Chen
Sha Zhao
Shi Gu
Peng Lin
De Ma
Huajin Tang
Gang Pan

Recent brain decoding studies have primarily emphasized the development of brain decoders, while largely neglecting the segmentation step. Existing methods typically adopt fixed-length segmentation, which might overlook subject- or task-level variability and disrupt temporal patterns within brain signals. To address this gap, we propose S3, which leverages spiking neurons as an isolating segmenter for brain signal decoding. S3 segments brain signals adaptively, considering subject- and task-level variability while preserving intrinsic temporal patterns of brain signals. It exploits the unique reset mechanism of spiking neurons to isolate previous irrelevant temporal patterns during the generation of each segmentation point. To optimize S3 for enhancing task performance in the absence of segmentation labels, we develop an optimization method where segmentation pseudo-labels are created with a stochastic-greedy algorithm to optimize them, while circumventing gradient blockade between S3 and task performance. Experiments on 10 downstream tasks across 13 public datasets demonstrate that S3 consistently outperforms existing methods, validating its effectiveness, generalizability and interpretability.

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AAMAS Conference 2025 Conference Paper

Bidirectional Distillation: A Mixed-Play Framework for Multi-Agent Generalizable Behaviors

Lang Feng
Jiahao Lin
Dong Xing
Li Zhang
De Ma
Gang Pan

Population-population generalization is a challenging problem in multi-agent reinforcement learning (MARL), particularly when agents encounter unseen co-players. However, existing self-playbased methods are constrained by the limitation of inside-space generalization. In this study, we propose Bidirectional Distillation (BiDist), a novel mixed-play framework, to overcome this limitation in MARL. BiDist leverages knowledge distillation in two alternating directions: forward distillation, which emulates the historical policies’ space and creates an implicit self-play, and reverse distillation, which systematically drives agents towards novel distributions outside the known policy space in a non-self-play manner. Our results highlight its remarkable generalization ability across a variety of cooperative, competitive, and social dilemma tasks, and reveal that BiDist significantly diversifies the policy distribution space.

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IJCAI Conference 2025 Conference Paper

EDyGS: Event Enhanced Dynamic 3D Radiance Fields from Blurry Monocular Video

Mengxu Lu
Zehao Chen
Yan Liu
De Ma
Huajin Tang
Qian Zheng
Gang Pan

The task of generating novel views in dynamic scenes plays a critical role in the 3D vision domain. Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) have shown great promise in this domain but struggle with motion blur, which often arises in real-world scenarios due to camera or object motion. Existing methods address camera motion blur but fall short in dynamic scenes, where the coupling of camera and object motion complicates multi-view consistency and temporal coherence. In this work, we propose EDyGS, a model designed to reconstruct sharp novel views from event streams and monocular videos of dynamic scenes with motion blur. Our approach introduces a motion-mask 3D Gaussian model that assigns each Gaussian an additional attribute to distinguish between static and dynamic regions. By leveraging this motion mask field, we separate and optimize the static and dynamic regions independently. A progressive learning strategy is adopted, where static regions are reconstructed by jointly optimizing camera poses and learnable 3D Gaussians, while dynamic regions are modeled using an implicit deformation field alongside learnable 3D Gaussians. We conduct both quantitative and qualitative experiments on synthetic and real-world data. Experimental results demonstrate that EDyGS effectively handles blurry inputs in dynamic scenes.

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AAAI Conference 2025 Conference Paper

EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

Zehao Chen
Zhan Lu
De Ma
Huajin Tang
Xudong Jiang
Qian Zheng
Gang Pan

High Dynamic Range (HDR) video reconstruction seeks to accurately restore the extensive dynamic range present in real-world scenes and is widely employed in downstream applications. Existing methods typically operate on one or a small number of consecutive frames, which often leads to inconsistent brightness across the video due to their limited perspective on the video sequence. Moreover, supervised learning-based approaches are susceptible to data bias, resulting in reduced effectiveness when confronted with test inputs exhibiting a domain gap relative to the training data. To address these limitations, we present an event-guided HDR video reconstruction method through building 3D Gaussian Splatting (3DGS), to ensure consistent brightness imposed by 3D consistency. We introduce HDR 3D Gaussians capable of simultaneously representing HDR and low-dynamic-range (LDR) colors. Furthermore, we incorporate a learnable HDR-to-LDR transformation optimized by input event streams and LDR frames to eliminate the data bias. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method achieves state-of-the-art performance.

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AAAI Conference 2025 Conference Paper

EvHDR-NeRF: Building High Dynamic Range Radiance Fields with Single Exposure Images and Events

Zehao Chen
Zhanfeng Liao
De Ma
Huajin Tang
Qian Zheng
Gang Pan

We present EvHDR-NeRF to recover a High Dynamic Range (HDR) radiance field from event streams and a set of Low Dynamic Range (LDR) views with single exposures. Using the EvHDR-NeRF, we can generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the new relationship between events streams and LDR images, which considers both the Camera Response Function (CRF) and exposure time. Based on this relationship, we categorize events into inter-frame events and intra-exposure. The former is utilized for building HDR radiance field and the latter is used to deblur potentially blurred images. Compared to existing methods, this method can effectively reconstruct the HDR radiance field even when the input images are degraded. Experimental results demonstrate that our method achieves state-of-the-art HDR reconstruction, providing a more adaptable and accurate solution for complex imaging applications.

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AAAI Conference 2025 Conference Paper

EvSTVSR: Event Guided Space-Time Video Super-Resolution

Haojie Yan
Zhan Lu
Zehao Chen
De Ma
Huajin Tang
Qian Zheng
Gang Pan

In the domain of space-time video super-resolution, it is typically challenging to handle complex motions (including large and nonlinear motions) and varying illumination scenes due to the lack of inter-frame information. Leveraging the dense temporal information provided by event signals offers a promising solution. Traditional event-based methods typically rely on multiple images, using motion estimation and compensation, which can introduce errors. Accumulated errors from multiple frames often lead to artifacts and blurriness in the output. To mitigate these issues, we propose EvSTVSR, a method that uses fewer adjacent frames and integrates dense temporal information from events to guide alignment. Additionally, we introduce a coordinate-based feature fusion upsampling module to achieve spatial super-resolution. Experimental results demonstrate that our method not only outperforms existing RGB-based approaches but also excels in handling large motion scenarios.

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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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ICML Conference 2025 Conference Paper

Training High Performance Spiking Neural Network by Temporal Model Calibration

Jiaqi Yan
Changping Wang
De Ma
Huajin Tang
Qian Zheng
Gang Pan 0001

Spiking Neural Networks (SNNs) are considered promising energy-efficient models due to their dynamic capability to process spatial-temporal spike information. Existing work has demonstrated that SNNs exhibit temporal heterogeneity, which leads to diverse outputs of SNNs at different time steps and has the potential to enhance their performance. Although SNNs obtained by direct training methods achieve state-of-the-art performance, current methods introduce limited temporal heterogeneity through the dynamics of spiking neurons or network structures. They lack the improvement of temporal heterogeneity through the lens of the gradient. In this paper, we first conclude that the diversity of the temporal logit gradients in current methods is limited. This leads to insufficient temporal heterogeneity and results in temporally miscalibrated SNNs with degraded performance. Based on the above analysis, we propose a Temporal Model Calibration (TMC) method, which can be seen as a logit gradient rescaling mechanism across time steps. Experimental results show that our method can improve the temporal logit gradient diversity and generate temporally calibrated SNNs with enhanced performance. In particular, our method achieves state-of-the-art accuracy on ImageNet, DVSCIFAR10, and N-Caltech101. Codes are available at https: //github. com/zju-bmi-lab/TMC.

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NeurIPS Conference 2024 Conference Paper

FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor

Mengting Xu
De Ma
Huajin Tang
Qian Zheng
Gang Pan

Currently, researchers think that the inherent robustness of spiking neural networks (SNNs) stems from their biologically plausible spiking neurons, and are dedicated to developing more bio-inspired models to defend attacks. However, most work relies solely on experimental analysis and lacks theoretical support, and the direct-encoding method and fixed membrane potential leak factor they used in spiking neurons are simplified simulations of those in the biological nervous system, which makes it difficult to ensure generalizability across all datasets and networks. Contrarily, the biological nervous system can stay reliable even in a highly complex noise environment, one of the reasons is selective visual attention and non-fixed membrane potential leaks in biological neurons. This biological finding has inspired us to design a highly robust SNN model that closely mimics the biological nervous system. In our study, we first present a unified theoretical framework for SNN robustness constraint, which suggests that improving the encoding method and evolution of the membrane potential leak factor in spiking neurons can improve SNN robustness. Subsequently, we propose a robust SNN (FEEL-SNN) with Frequency Encoding (FE) and Evolutionary Leak factor (EL) to defend against different noises, mimicking the selective visual attention mechanism and non-fixed leak observed in biological systems. Experimental results confirm the efficacy of both our FE, EL, and FEEL methods, either in isolation or in conjunction with established robust enhancement algorithms, for enhancing the robustness of SNNs.

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AAAI Conference 2024 Conference Paper

Learning to Manipulate Artistic Images

Wei Guo
Yuqi Zhang
De Ma
Qian Zheng

Recent advancement in computer vision has significantly lowered the barriers to artistic creation. Exemplar-based image translation methods have attracted much attention due to flexibility and controllability. However, these methods hold assumptions regarding semantics or require semantic information as the input, while accurate semantics is not easy to obtain in artistic images. Besides, these methods suffer from cross-domain artifacts due to training data prior and generate imprecise structure due to feature compression in the spatial domain. In this paper, we propose an arbitrary Style Image Manipulation Network (SIM-Net), which leverages semantic-free information as guidance and a region transportation strategy in a self-supervised manner for image generation. Our method balances computational efficiency and high resolution to a certain extent. Moreover, our method facilitates zero-shot style image manipulation. Both qualitative and quantitative experiments demonstrate the superiority of our method over state-of-the-art methods.Code is available at https://github.com/SnailForce/SIM-Net.

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IJCAI Conference 2022 Conference Paper

Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks

Lang Feng
Qianhui Liu
Huajin Tang
De Ma
Gang Pan

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low energy consumption. Recent research is devoted to utilizing spatio-temporal information to directly train SNNs by backpropagation. However, the binary and non-differentiable properties of spike activities force directly trained SNNs to suffer from serious gradient vanishing and network degradation, which greatly limits the performance of directly trained SNNs and prevents them from going deeper. In this paper, we propose a multi-level firing (MLF) method based on the existing spatio-temporal back propagation (STBP) method, and spiking dormant-suppressed residual network (spiking DS-ResNet). MLF enables more efficient gradient propagation and the incremental expression ability of the neurons. Spiking DS-ResNet can efficiently perform identity mapping of discrete spikes, as well as provide a more suitable connection for gradient propagation in deep SNNs. With the proposed method, our model achieves superior performances on a non-neuromorphic dataset and two neuromorphic datasets with much fewer trainable parameters and demonstrates the great ability to combat the gradient vanishing and degradation problem in deep SNNs.

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IJCAI Conference 2021 Conference Paper

Event-based Action Recognition Using Motion Information and Spiking Neural Networks

Qianhui Liu
Dong Xing
Huajin Tang
De Ma
Gang Pan

Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.

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