EventMG: Efficient Multilevel Mamba-Graph Learning for Spatiotemporal Event Representation
Authors: Sheng Wu
Lin Jin
Hui Feng
Bo Hu
Venue: NeurIPS 2025 poster ()
Keywords: Event Camera
Mamba
Graph Neural Network
Spatiotemporal Representation Learning
Primary area: deep_learning
TL;DR: This paper introduces EventMG, a novel model built on multilevel Mamba-Graph architecture with specialized event scanning (SEST), delivering lightweight, efficient, and high-performance spatiotemporal representation for event camera data.

Abstract: Event cameras offer unique advantages in scenarios involving high speed, low light, and high dynamic range, yet their asynchronous and sparse nature poses significant challenges to efficient spatiotemporal representation learning. Specifically, despite notable progress in the field, effectively modeling the full spatiotemporal context, selectively attending to salient dynamic regions, and robustly adapting to the variable density and dynamic nature of event data remain key challenges. Motivated by these challenges, this paper proposes EventMG, a lightweight, efficient, multilevel Mamba-Graph architecture designed for learning high-quality spatiotemporal event representations. EventMG employs a multilevel approach, jointly modeling information at the micro (single event) and macro (event cluster) levels to comprehensively capture the multi-scale characteristics of event data. At the micro-level, it focuses on spatiotemporal details, employing State Space Model (SSM) based Mamba, to precisely capture long-range dependencies among numerous event nodes. Concurrently, at the macro-level, Component Graphs are introduced to efficiently encode the local semantics and global topology of dense event regions. Furthermore, to better accommodate the dynamic and sparse characteristics of data, we propose the Spatiotemporal-aware Event Scanning Technology (SEST), integrating the Adaptive Perturbation Network (APN) and Multidirectional Scanning Module (MSM), which substantially enhances the model's ability to perceive and focus on key spatiotemporal patterns. By employing this novel collaborative paradigm, EventMG demonstrates the ability to effectively capture multi-level spatiotemporal characteristics of event data while maintaining a low parameter count and linear computational complexity, suggesting a promising direction for event representation learning.

EventMG: Efficient Multilevel Mamba-Graph Learning for Spatiotemporal Event Representation
Sheng Wu' Lin Jin
Hui Fengl.. Bo Hul2
College of Future Information Technology, Fudan University
2 State Key Laboratory of Integrated Chips and Systems, Fudan University
Abstract
Event cameras offer unique advantages in scenarios involving high speed, low light, and high dynamic range, yet their asynchronous and sparse nature poses significant challenges efficient spatiotemporal representation learning. Specifically, despite notable progress in the field, effectively modeling the full spatiotemporal context, selectively attending salient dynamic regions, and robustly adapting the vari- able density and dynamic nature of event data remain key challenges. Motivated by these challenges, this paper proposes EventMG, lightweight, efficient, multi- level Mamba- Grapp architecture designed for learning high-quality spatiotemporal event representations. EventMG employs a multilevel approach, jointly modeling information at the micro (single event) and macro (event cluster) levels to compre- hensively capture the multi-scale characteristics of event data. the micro-level, it focuses on spatiotemporal details, employing State Space Model (SSM) based Mamba, to precisely capture long-range dependencies among numerous event nodes. Concurrently, the macro-level, Component Graphs are introduced to efficiently encode the local semantics and global topology of dense event regions. Furthermore, better accommodate the dynamic and sparse characteristies of Event Scanning Technology (SEST),
data, we propose the Spatiotemporal-avvee integrating the Adaptive Perturbation Network (APN) and Multidirectional Scan- ning Module (MSM), which substantially enhances the model's ability to perceive and focus on key spatiotemporal patterns. By employing this novel collaborative paradigm, EventMG demonstrates the ability to effectively capture multi-level spatiotemporal characteristics of event data while maintaining a low parameter count and linear computational complexity, suggesting a promising direction for event representation learning.
Introduction
Event cameras, a novel class of vision sensors that asynchronously record pixel brightness changes to generate high-speed, sparse data streams [I], have garnered significant attention recent years due to their unique capabilities. Unlike traditional cameras with fixed time exposure, event cameras offer mierosecond-level temporal resolution and dynamic range exceeding 120 dB-far surpassing the 60- 70 dB of conventional cameras 2 2. These attributes provide substantial advantages challenging scenarios such high-speed motion, low-light conditions, and high-contrast environments 4
Despite their distinct advantages, event cameras pose significant challenges for data processing. The asynchronous generation of events in response to changes in pixel brightness results in outputs with irregular spatiotemporal properties, high temporal resolution, and large instantaneous data volumes
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Graph Construction
Mamba-Graph Architecture
Multilevel Fusion
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The overall workflow of EventMG comprises three main modules: 1) Graph Construction, which constructs the micro-level Event Graph and macro-level Component Graph; 2) Mamba-Graph Architecture, which performs spatiotemporal modeling at both levels; and 3) Multilevel Fusion, which integrates outputs across levels to produce the final spatiotemporal representation for downstream tasks.
Figure I:
S These characteristics make it difficult to directly apply traditional algorithms,, which are typically designed for regular and dense data, to event-based modeling and representation learning.
A straightforward strategy to address the above challenges is to convert the event stream into
frame-based dense representations (c.g., event counting or time surfaces [6] 77). This conversion
facilitates the application of established frame-based processing methods, including Transformer- based architectures, which have shown improved performance on certain tasks g 9 However, this event-to- frame conversion undermines the core microsecond-leww spatiotemporal precision of event data, introducing potential motion blur high-speed scenarios. It also increases data density and redundancy in non-event regions, negating the intrinsic sparsity benefits of event data [10], thereby limiting the full exploitation of its spatiotemporal richness.
To retain the spatiotemporal precision and sparsity, recent methods directly process sparse event streams using point-based representations. Techniques based on point clouds [11, Graph Neural Networks (GNNs) [1S18), and Spiking Neural Networks (SNNs) 22 have emerged to avoid dense conversions. To further capture the dynamic context and spatiotemporal dependencies of event streams, Transformer- -aaooeedeerb--ddddd--- architectures have been integrated into sparse processing pipelines Although self atf-ntttenion enables effective modeling of spatiotemporal features of dynamic event streams, its quadratic complexity (O(N2) poses scalability challenges for long sequences, resulting in high computational costs and large models.
23 24.
This paper proposes EventMG, a lightweight and efficient multilevel spatiotemporal representation learning architecture specifically tailored for event data, address the limitations of existing methods, particularly concerning the spatiotemporal precision, representation adequacy, and computational cost. EventMG achieves comprehensive modeling by synergistically capturing both micro-level (individual event spatiotemporal details) and macro-level (event cluster structure and semantics) information, enabling systematic feature extraction and fusion while preserving spatiotemporal precision. At its core, EventMG features an innovative Mamba-Grapy structure: Mamba 225 leveraging State Space Models (SSMs) 26 28), efficiently models long-range spatiotemporal dependencies within large- scale events at linear (O(N) computational cost, capturing fine-grained dynamics. Concurrently, Component Graphs 29 are employed to effectively encode local semantics and global topological structures within dense event regions, thus realizing deep integration of event points and their relational graph context. Furthermore, to specifically adapt Mamba for the unique sparsity and dynamism inherent in event streams, design the Spatiotemporal-awann (SEST). Integrating an Adaptive Perturbation Network (APN) and a Multi-directional Scanning Module (MSM), SEST innovatively enhances the Mamba framework, significantly improving its capacity to perceive and model complex spatiotemporal patterns native to event data.
Event Scanning Technology
Experimental evaluation on representative dynamic tasks indicates that EventMG delivers perfor- mance comparable to state-of-the-art heavyweight models, but at a lower computational and paramet- ric cost. The results underscore the potential of our proposed architectural paradigm, which seeks to strike a deliberate balance between computational efficiency, representational power, and respect for the intrinsic properties of event data.
2

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The main contributions of this study are summarized as follows:
We introduce a novel Mamba-Graph collaborative architecture that combines the strengths of SSM-based and graph-based approaches, efficiently modeling dynamic event data from miero-level spatiotemporal details to macro-level cluster patterns.
We develop the Spatiotemporal-awart Event Scanning Technology (SEST) that specifically adapts SSM-based Mamba to the unique sparse and irregular spatiotemporal characteristics of event streams, significantly enhancing their capacity to model complex dynamic patterns.
We present EventMG, lightweight and efficient spatiotemporal event representation architecture achieving an effective balance between high performance and minimal computational overhead (linear complexity) without requiring pre-training, across diverse dynamic tasks.
2 Related Work
The unique asynchronous sparse data from event cameras poses challenges for efficient representation learning and modeling, driving diverse processing strategies with varying efficiency and performance. This chapter reviews key event data representation approaches and spatiotemporal sequence modeling architectures, analyzing their characteristies and limitations.
2.1 Event Data Representation Strategies
Frame-based Representations: A common strategy to exploit mature frame-based methods (c.g. CNNs, Vision Transformers) is to convert asynchronous, sparse event streams into dense, image-like
representations by accumulating events over fixed counts [30] or time windows [3], effectively projecting and compressing temporal information into 2D or 3D tensors. This enables direct use of existing architectures but compromises mierosecond-leve precision and destroys sparsity, leading to
motion blur, redundant computation, and low efficiency [IO 32 33 Furthermore, processing these
dense representations often requires heavyweight models, increasing latency 23 thus fundamentally limiting the exploitation of event camera advantages.
Sparse Representations:
Directly processing sparse events preserves spatiotemporal fidelity, better aligning with event data characteristics. Point cloud methods [1] treat events as spacetime points, but must contend with unordered sets and neighborhood definitions. Graph-based methods [1H
processed by adapted point networks or specialized kernels [13
7 use GNNs on constructed event graphs to capture structure, offering flexibility but facing challenges in effective graph construction, especially under event noise (34) and high dynamics. Spiking Neural Networks (SNNs) (7))37). bio-inspired asynchronous models well-suited to event data due to their spike-driven mechanisms 3,, offer high energy efficiency potential [2] 39 but present training challenges, representing distinct research path. Although these sparse methods preserve data fidelity, effectively modeling long-range spatiotemporal dependencies in large-scale data remains the key challenge to deeply understanding dynamic scenes and learning insightful spatiotemporal representations.
2.2 Spatiotemporal Sequence Modeling
have been introduced into event processing due its strong representation capabilities and ability model global dependencies through the self-attention mechanism. These methods have been applied to frame-based sequences [ 9 44] and
Transformer-basee Methods: Transformers 40 43
directly sparse event, point cloud, or graph node feature sequences [33 45
23
24
46 However,
the quadratic complexity O(N2) of self-attention limits scalability, making it challenging to handle large-scale, high-resolution event data resource-constrained scenarios.
SSM-based Methods: To address Transformer inefficiency in long-sequence modeling, State
Space Models (SSMs) have emerged as promising alternative 28
47 48). Mamba [25] enables
efficient long-range modeling with linear complexity and hardware- fardwale-friendly properties, rivaling Transformers. However, applying Mamba event streams is challenging due sparsity, asynchrony, and dynamics. EventMG addresses this by introducing APN and MSM modules to improve robustness and spatiotemporal modeling while fully leveraging Mamba's O(N) efficiency.
In summary, EventMG designed to effectively address the challenges of processing event streams. Building upon the Mamba enhancements (APN and MSM), EventMG further innovates by estab-
3

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lishing a Mamba-Graan Graph collaborative architecture. This unique combination efficiently implements multilevel modeling, capturing information from micro macro scales and local global con- texts. As pre-training-free, end-to-end framework, EventMG strikes an excellent balance between performance and lightweight design.
Methodology
This section details our proposed EventMG, a lightweight end-to-end architecture for efficiently learning multi-level spatiotemporal representations from event streams. We begin in Section [3. with a high-level overview of the model's overall architecture. The subsequent subsections then delve into its core modules: micro-level representation learning (Section 3.2) macro-level representation learning (Section 3.3 and the final multi-level fusion strategy (Section 3.4
3.1 Overall Architecture
The overall architecture of EventMG is multi-level information processing pipeline designed to simulate the "event-robject--Se" local event details with global context through three core stages: micro-level modeling, macro-level aggregation, and multi-level feedback and fusion. The entire workflow is illustrated in Figure ||
hierarchy inherent in event data. This pipeline deeply fuses
Specifically, the process begins the micro-level, where input event slices are first constructed into a sparse graph and then processed by the Spatiotemporal-avvee Event Scanning Technology (SEST) and the EventsMamba module extract fine-grained node features encapsulating long-range dependencies. Subsequently, at the macro-level, the model leverages these micro-level features to construct Component Graph and perform information aggregation, distilling the macro-level feature that represents the global scene. Finally, through the serial and mutually-feeding mechanism, the macro-level feature is fed back to enhance each micro-level representation, generating final output that is both detailed and context-aware. This design fully embodies our core philosophy of "starting from the micro, abstracting the macro, and then guiding the micro with the macro."
3.2 Micro-level: Representation based on Events
The processing in EventMG begins at the micro-level, focusing on individual events and their local spatiotemporal context. The core objective at this level is model fine-grained dynamics and high temporal resolution inherent in event data.
3.2.1 Event Graph Construction
At the micro-level, we first process the continuous event stream. Following established methods employ sliding window strategy with fixed number of events, N, to segment the stream into multiple event slices. This approach ensures that each slice contains a stable amount of information and can adapt to the scene's activity intensity, providing standardized inputs for subsequent processing.
100 4
Within each event slice, every individual event is represented which encapsulates its spatial coordinates, timestamp, and polarity information [4]. To capture local relationships between events, basic event graph G = (V, EE) is constructed, where V is the set of event nodes. We employ an efficient sparse graph construction strategy that establishes connections by finding at most & neighbors for each node, rather than constructing a dense adjacency matrix. This method ensures the graph's s storage and computational complexity is ONN), maintaining a linear relationship with the number of events, N. Consequently, the memory footprint is predictable and strictly controllable, which provides a fundamental guarantee for processing large-scale event streams. The edges E of the graph are established based spatiotemporal proximity, defined below:
event node (It, Vo. t,, Ps).
E(u,, 1T)) = {
if Vl 8, l2 + c2 t,) < dat.
otherwise,
where an edge exists if the spatiotemporal distance, calculated as the 1.2 norm of the spatial component ||s; sy and the scaled temporal component clt, tyy, does not exceed the threshold dat.

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Although graph construction defines the event's 's topological structure, the initial node features contain limited information and are insufficient for describing complex local dynamies. To address this, we then introduce a lightweight Event Graph Neural Network (GNN) for local feature enhancement. The module updates and enriches the representation of each node e; by aggregating information from its local spatiotemporal neighborhood (vx) = {ry E(,,, 0;y) = 1), thereby transforming the original, discrete event point cloud into a graph rich with local features. This preliminary feature distillation process provides high-quality input node features for subsequent operations.
3.2.2 Spatiotemporal-wwan Event Scanning Technology (SEST)
To efficiently model long-range spatiotemporal dependencies at the micro level, we adopt the SSM- based Mamba model [25]. However, its standard scanning struggles with the sparsity, asynchrony. and dynamics of event data. To address this, we propose the Spatiotemporal-aware Event Scanning Technology (SEST), which integrates APN and MSM modules to optimize event arrangement and scanning (see Figure 2 enabling Mamba to better adapt to the uniqueness of event streams.
Adaptive Perturbation Network (APN): Although graph construction defines the event's topolog- ical structure, the initial node features contain limited information. To address this, we first employ a lightweight Event Graph Neural Network (GNN) for local feature enhancement. The GNN module updates and enriches the representation of each node iú by aggregating information from its local spatiotemporal neighborhood (vx:)) = {uy E(v; 42) = }}: thereby transforming the original, discrete event point cloud into a graph rich with local features. This preliminary feature distillation process provides high-quality input node features, denoted as h, for subsequent operations.
Subsequently, the APN module enhances the model's 's adaptability to event data and further optimizes the event arrangement through learnable coordinate perturbation mechanism. It consists of two complementary paths, Local and Global, to jointly optimize the features:
h local Conv D(h),
(2a)
h global MLP MMPPioballt)
(2b)
(Ar, Ay. At) = Tanh Manh(MMPnaall (h local + hgglobl))
(2c)
where, the Local path is processed by a convolution (Conyld), which operates on the sequence of node features sorted by timestamp t. Through its sliding convolutional kernel, it efficiently
captures short-term temporal dynamics between an event and its temporally adjacent neighbors. Complementing this, the processing in the Global path targets all nodes, aiming to extract global feature patterns independent of the sequential context. By fusing the outputs of these two paths, the APN module computes the spatiotemporal perturbations (Ar, Ay, At) based on a comprehensive judgment of both local temporal dynamics and global feature patterns. These perturbation values are then applied to the original coordinates to generate the perturbed positions (z/, 2/1 ():
2 = x, + Ar. y/ = y: + Ay. E/ = 4 + At.
The core objective of this adaptive coordinate adjustment is not merely to "correct" the spatiotemporal positions of events, but rather learn a more discriminative Effective Coordinate Space for the subsequent MSM scanning module. This adaptive coordinate adjustment serves two main purposes: (1) It enhances the expression of event dynamics by refining local spatiotemporal node arrangements (e.g.. promoting effective aggregation or dispersion in dense regions), thereby improving the model's s robustness noise and variation. (2) ensures sufficient distinction across the x, y, and dimensions, laying a discriminative foundation for the subsequent multidirectional scanning.
Multidirectional Scanning Module (MSM): To overcome the limitations of unidirectional scan- ning (c.g., the time-based sorting in standard Mamba) in capturing the irregular, multidimensional structure of event data, we introduce the Multidirectional Scanning Module (MSM). The primary function of this module is to construct a set of multidirectional event sequences in preparation for subsequent long-range dependency modeling. It utilizes the effective coordinates optimized by APN to sort the node set Vú along multiple fundamental dimensions dæ, thereby generating a set of sequences S. These scanning directions typically include forward and backward scans along the t, x. and y axes, i.e., the direction set D = {t" ,t .xt, x .V. .V" }.
S = {Sa Scan de) de E D}.
(4)
5

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Apatiotemporal-awarr Event Scanning Technology (SEST)
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Figure 2: The Spatiotemporal-avare Event Scanning Technology (SEST) includes two modules: Adaptive Perturbation Network (APN) and Multidirectional Scanning Module (MSM).
In summary, APN and MSM work in synergy to implement a data- adaptive scanning strategy: APN first learns task-driven effective coordinate space that born for scanning, after which MSM performs multidirectional scans within this optimized space. The final output of this process is an optimized set of events S, containing multiple sequential perspectives, which serves the input for the core modeling module in the next stage.
3.2.3 Micro-level Spatiotemporal Modeling
We introduce EventsMamba as our core micro-level modeling module, leveraging Mamba [25], an efficient state-space model, to process large-scale event streams. This module takes input the set of multidirectional sequences S generated by MSM from APN-optimized coordinates.
By applying the Mamba core processor along these diverse sequential directions, EventsMamma captures long-range spatiotemporal dependencies and dynamic patterns from multiple perspectives. Finally, the processed results from all sequences are fused into the final fine-grained features h, via an aggregation function, as follows:
h Aggr (Mamba(Sx)) S.ES
(5)
Combined with Mamba's inherent ONN) linear complexity, this design efficiently extracts node features h. that capture detailed event-level dynamics and interactions.
3.3 Macro-level: Representation based on Components
Although micro-level modeling captures fine-grained spatiotemporal dynamics, it often lacks aware- ness of global structure and large-scale patterns, resulting in incomplete representations. To address this, we introduce a macro-level representation strategy based on Component Graphs to effectively extract and organize such macro-structural information.
3.3.1 Component Graph Construction
Macro-level modeling begins by identifying and abstracting key structural units in the event stream. Event-dense regions- -uchh as object edges, texture boundaries, areas with strong luminance contrast-not only concentrate events in space and time but also carry important macro-structural and semantic information. Motivated by their structure, these regions-often forming strongly connected subgraphs-are formalized via the graph-theoretic notion of connected components [49]
Ve C V. Ec E.
Ve,, By E Ve, there exists path P.j. Veu E (V Vc), no path connects to Vc,
(6)
IVel R'min-
connected component defined as a maximal subgraph where any two nodes are path-connected and no external node is connected to it. To exclude insignificant regions that may correspond to noise,

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we impose minimum node count threshold Pemin- retaining only components with [Vc] 2 7min- Each valid component C' corresponds subgraph Gc (Vc, Ec).
Next, we construct the Component Graph as a macro-level structural representation from valid con- nected components, each abstracted as a supernode. Given the typically small number of supernodes representing high-level relationships, we adopt a fully connected strategy. The feature of each supern- ode he is obtained by average pooling over its constituent event nodes h, within the component, which effectively captures essential characteristies while reducing processing complexity:
he
Σ h.. VeVe
|Vel
The component graph offers three core benefits: (1) It provides a macro-level, structured view of event stream, simplifying the underlying event graph by modeling scene structure and dynamics at higher level. (2) By selecting valid components, it denoises the data and highlights salient structures, yielding more robust basis for downstream feature learning. (3) Together with micro-level representations, forms the foundation of EventMG's multilevel architecture, enabling cross-level interaction.
3.3.2 Macro-level Spatiotemporal Modeling
After constructing the Component Graph Gc. introduce the ComponentsNet module for macro- level spatiotemporal modeling. As the key processing unit at this level, ComponentsNet captures structural interactions between supernodes and scene-level dynamics. Leveraging Graph Neural Networks (GNNs), the module performs message passing and node updates over the component graph, aggregating information from connected supernodes model their dependencies. This produces high-level representations of the scene's overall state and dynamics, offering essential macro-context for guiding micro-level features in subsequent processing.
3.4 Multilevel Fusion for Comprehensive Representation
To obtain a unified and informative representation, EventMG introduces an adaptive multilevel fusion mechanism based on learnable gating. The macro-level feature he is first mapped via learnable layer fmap and assigned to each micro-level event node v E Ve as macro-context hc = Smap (hc). gating network then adaptively fuses the node's 's micro feature h, with its macro-context h producing the final fused feature h fised.
Go = o (MLP((h,, hc-w)))),
(8a)
fused
9n @ h, + (1-
1 gv) @ ho-
(8b)
where go is the gating weight learned by a multi-layer perceptron (MLP), [++] denotes feature concatenation, is the Sigmoid function, and @ is element-wise multiplication.
This multilevel fusion mechanism integrates micro-level details with macro-level structure, enabling fine-grained features to be contextually guided and macro patterns refined by local specifics. This bidirectional interaction results in spatiotemporal representations that are robust, comprehensive, and discriminative, effectively capturing fast local changes as well as global structural shifts.
4
Experiments
In this section, evaluate EventMG on two representative tasks with high spatiotemporal dynamies: Object Detection and Action Recognition, demonstrating its effectiveness in learning spatiotemporal representations from dynamic event streams.
4.1 Datasets and Evaluation Metrics
Object Detection is evaluated on two traffic scene event camera datasets using three standard metrics.
The Genl Automotive Detection Dataset comprises over 39 hours of 304 x 240 event video, captured across urban, highway, and rural traffic scenarios. It contains over 255,000 manual annotations for pedestrians and cars, annotated at frequency of 1-4 Hz.
Genl Dataset 50
7

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Table Comparison on Genl 1Mpx Datasets. Performance reported terms parameters (Params). mean Average Precision (mAP), and backbone inference time per batch (Time). A star (*) indicates estimated values. Best results are in bold; second- are underlined.
Genl
1Mpx
Method
Backhone
Params (M)
mAP (%)
Time (ms) mAP (%)
Time (ms)
Asynet
32
Sparse CNN
11.4
14.5
RRC-Events
68
CNN
> 100
30.7
21.5
34.3
46.4
YOLOv3 Events
01
CNN
60
31.2
22.3
34.6
49.4
AEGNN 15
GNN
20
16.3
Spiking DenseNet
22
SNN
8.2
18.9
ERGO-12 44
Transformer
59.6
50.4
69.9
40.6
100.0
RED [5]
CNN RNN
24.1
40.0
16.7
39.3
24.1
RVT-B 8
Transformer RNN
18.5
47.2
10.2
47.4
11.9
Nested-T
Transformer RNN
22.2
46.3
25.9
46.0
33.5
GET-T
Transformer + RNN
21.9
47.9
16.8
48.4
18.2
55-VIT-B 666
Transformer SSM
18.2
47.4
8.16
47.2
9.57
EventMG (ours)
GNN SSM
1.96
53.7
8.66
47.4
9.63
Mppx Dataset [5
The Megapixel Automotive Detection Dataset provides over 14 hours of high-resolution event video and 25 million annotations for cars, pedestrians, and two-wheelers, making ideal for developing advanced detection models dynamic traffic scenes.
We evaluate model performance using three primary metrics: 1) Total Parameters, indicating model complexity; 2) Mean Average Precision (mAP@0.5:0.95), computed with COCO [22 toolkit to asses accuracy; 3) Mean inference Time, measuring runtime efficiency.
Evaluation Metries:
Action Recognition assessed on four representative datasets using three evaluation metrics.
THUEACTT -50-HHL Dataset 53 The dataset is very challenging dataset with 50 action classes and 2330 recordings, capturing actions 18 students from various viewpoints in long corridor and hall scenes using DAVIS346 camera (346 x 260), with each clip lasting 2-5 seconds.
DVS Action Dataset [54] The dataset contains 10 actions performed by 15 subjects in an office environment, captured by DAVIS346 event camera at 346 260 resolution.
HMDB51-DVS & UCFI01-DVS [55] Event-based versions of HMDB51 [56] and UCF101 [57]. converted using DAVIS240 camera. HMDB51-DVS includes 6. 6,766 clips across classes, while UCFI01-DVS comprises 13,320 clips from 101 classes. Both resolution of 320 240.
We evaluate model performance based on three key metrics: 1) Parameters (Params), reflecting model size and complexity; 2) GFLOPs, measuring the computational cost; and 3) Accuracy (Acc), assessing predictive precision on the recognition task.
Evaluation Metrics:
Notably, while action recognition datasets such as DVS128 Gesture (58) and Daily DVS 39 are widely used, most advanced methods already achieve near-saturation accuracy close to 100%. To better EventMG, conduct experiments on the above more discriminative datasets.
4.2 Implementation Details
EventMG built upon hierarchical multi-stage architecture designed extract comprehensive spatiotemporal features. The implementation relies on PyTorch [59] for the core framework and PyTorch Geometric (PyG) [60] for graph-based based operations. The number of stages and their feature dimensions are flexibly configured to align with the requirements of different downstream tasks, such object detection and action recognition. To ensure efficient processing of our custom graph operations, parts of PyG's 's underlying code were modified. The training pipeline is managed with PyTorch Lightning on NVIDIA GPUs, and the model optimized using the Adam W optimizer with OneCycle learning rate schedule [22
61

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4.3 Benchmark Comparison
4.3.1 Object Detection
To comprchensively evaluate the effectiveness of EventMG event-based object detection tasks, compare with several representative methods on two widely-used benchmark datasets: Gen1 and 1Mpx. The detailed results are presented in Table
On the Genl dataset, EventMG demonstrates competitive performance, achieving the mAP of 53.7%. Notably, the model stands out for its compact model size; including the task head, it has only 1.96M parameters, making very lean compared to similar methods. For instance, ERGO-12 44 reaches 50.4% mAP with 59.6M parameters, whereas EventMG achieves higher accuracy with only 3.3% of its model size. Furthermore, the efficiency advantage of the model also extends to inference, where runs significantly faster than high-performance models such as ERGO-12 (69.9 ms).
On the more challenging and higher-resolution 1Mpx dataset, EventMG remains competitive, achiev- ing an mAP of 47.4%, which is comparable to the best- best-performing result from GET-T [9] (48 448..%) This performance is achieved while maintaining a significant efficiency advantage, utilizing a small fraction of the parameters in contrast to GET-T's 21.9M.
In summary, EventMG matches outperforms existing methods across datasets while significantly reducing model complexity and inference latency, demonstrating its effectiveness in learning and integrating spatiotemporal patterns from event streams.
4.3.2 Action Recognition
Table 2: Comparison on T-U 55-CHL datasct.
Table 3: Evaluation results on DVS Action dataset.
Method
Params (M) GFLOPs Acc
Method
Params (M) GFLOPs Acc
HMAX SNN 67
0.327
STCA 68
15.13
0.792
Motion-based based SNN
39
0.473
Notion-bbsed SNN 39
0.781
EV-ACT 53
21.3
29
0.585
ST-EVNet 69
1.6
0.887
EventMamba
10
0.905
0.953
0.594
TTPOINT [4]
0.334
0.587
0.927
EventMamha 10
0.905
0,476
0.891
EventMG (Ours)
0.773
0.691
0.588
Two-stream SNN
70
9.17
0.917
EventMG (Ours)
0.773
0.531
0.933
We evaluate EventMG on event-based action recognition across four popular datasets, comparing with leading methods. Results are shown in Tables and with some baselines referenced from 100 and supplemented by original sources.
On the challenging THUB-ACT, -50-CHL dataset (Table 2) EventMG delivers strong performance. -TL---CC--O-CHL
Compared to EV-ACT [53], it achieves higher accuracy with only 3.6% of the parameters and 2.3% of the GFLOPs. Although slightly behind EventMamba [T]] in accuracy (0.588 vs. 0.594), EventMG requires fewer parameters (0. 77.7 0.905M) and less computation (0.691 vs. 0.953 GFLOPs). These results confirm the efficiency of our proposed multilevel spatiotemporal modeling strategy.
On the DVS Action dataset (Table 3 EventMG achieves the highest accuracy among all methods 93.3%, while maintaining a relatively low parameter count (0.773M) and the second-lowest computational cost (0.531 GFLOPs). These results highlight its balance between accuracy and efficiency. Although prior work [10) noted the risk of overfitting on small-scale datasets, EventMG mitigates this through the APN module, which enhances generalization to salient spatiotemporal patterns, ensuring robust and efficient performance.
For the larger HMDB51-DVS and UCF101-DVS datasets converted from conventional videos, EventMG demonstrates strong overall performance (Table(4). achieves the lowest GFLOPs among all methods and keeps the parameter count below 1M slightyhlly above TTPOINT [14] but still far smaller than most advanced models. terms of accuracy, while marginally below the best-reported results in [10, EventMG outperforms most existing approaches, including TTPOINT. This favorable trade-off achieved without increasing internal feature dimensions, validating the effectiveness of the proposed multilevel spatiotemporal architecture in modeling complex dynamic scenes efficiently.

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Table 4: Evaluation results on HMDB51-DVS and UCFI01-DVS.
HMDB51-DVS
UCFI01-DVS
Method
Params (M)
GFLOPs
Acc
Params (M)
GFLOPs
Acc
C3D
78.41
39.69
0.417
78.41
39.69
0,472
I3D
12.37
30.11
0.466
12.37
30.11
0.635
ResNet-34 73
63.70
11.64
0.438
ResNext-50 74
26,05
6,46
0.394
26,05
6,46
0,602
RG-CNN 555
3,86
12.39
0.515
6.95
12,46
0.632
EVTC+I3D 7/
0.704
0.791
EventMix 16
12.63
1.99
0.703
Event-LSTM 77
21.4
0.776
ITPOINT [14
0.345
0.587
0.569
0.357
0,587
0.725
EventMamba 10
0.916
0.953
0.864
3.28
3.722
0.979
EventMG (Ours)
0.773
0.531
0.712
0.773
0.531
0.796
4.4 Ablation Studies
This section presents ablation studies to assess the impact of key EventMG components. Starting from the full model, cach module is removed individually and evaluated across two tasks (Table 5)
Table 5: Ablation results on Genl and T-UE-ACT550- -50-CHL. Genl corresponds to object detection, and THU .50CHLL action recognition. Note: Parameter counts Genl exclude the task head 1.2M...
Ablation Modules
Genl (Detection)
THU ACT PATT -50-CHL (Action)
Component Graph APN MSM
Params (M)
mAP
Time (ms)
Params (M)
GFLOPs
Acc
X
0.367
0.213
3.20
0.334
0,397
0.322
0.639
0.447
6,95
0.632
0.608
0.491
0.555
0.410
6.81
0.561
0.589
0.502
X
0.630
0.424
5.26
0.685
0.585
0.486
0.729
0.537
8.66
0.773
0.691
0.588
Component Graph models structural relationships among event clusters to provide macro-level semanties. Removing this module results significant accuracy drop in both tasks, highlighting its essential role in global structure modeling and cross-level feature fusion.
SEST-APN (Adaptive Perturbation Network) applies learnable coordinate perturbations to enhance robustness and focus on salient spatiotemporal patterns. Its removal reduces complexity but causes a sharp accuracy drop, confirming value for dynamic adaptation and feature learning.
SEST-MSM (Mutti-directional Scanning Module) captures spatiotemporal dependencies through multidirectional scanning. Excluding this module degrades performance in both tasks, demonstrating its importance in modeling diverse dynamic trajectories.
Impact of Scanning Directions. We conduct an ablation study on the scanning directions to validate our multidirectional design. For this study, we employ configuration of forward scans along the t, and y axes, which represents effective balance of performance and efficiency. On the Genl dataset, this three-direction model achieves an mAP of 53,7%, showing a significant synergistic gain compared to unidirectional scans (l-axis only: 42.4%, r-axis only: 44.0%, and y-axis only: 43.8%).
Conclusion and Discussion
We propose EventMG, a Mamba-Graph collaborative architecture that systematically addresses the intrinsic challenges of event data. It leverages GNNs for local topology and an innovative SEST module to intelligently serialize graph information, enabling Mamba to model long-range dependencies with linear complexity while achieving multi-level understanding of the data. The core contribution EventMG lies a novel design philosophy that seeks a delicate balance among representational power, computational efficiency, and respect for the data's 's intrinsic properties.
The limitations and future directions of EventMG warrant further exploration, including its generaliza- tion across diverse tasks, robustness under extreme conditions, and the challenges of hyperparameter tuning and interpretability inherent to multi-module design. Future research should focus on ex- ploring more efficient adaptive and interpretable methods to further refine this architectural paradigm.
10

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Acknowledgments and Disclosure of Funding
The authors declare no conflict of interest. This work was supported by the National Key Research and Development Program (No. 2024YFE0200703) The computations in this research were performed using the CFFF platform of Fudan University.
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A Preliminaries
A.1 State Space Models
State Space Models (SSMs) 78 provide classical framework for modeling dynamic systems by capturing the relationships between inputs and outputs through hidden states. These models describe the evolution of a system in terms of state variables that are not directly observed. Formally, SSM is expressed as:
(t) = Ah(t) + Bx(t), v(() Ch((),
where httt) RN represents the hidden state, sx() is the input signal, and y(t) is the output signal. The matrices A, B, and C' are system parameters that govern the dynamics of the system.
For timerete-time inputs, the Zero-Order Hold (ZOH) method [79 is commonly used to discretize the continuous-time model. The discretization of the system is given by:
À cxp( AA),
(10)
B = (AA) (exp( (A)) 1) AB,
where denotes the time step and exp( represents the matrix exponential. The discretized system is then deseribed by the following equations:
h4 Ah-11 Brg.
(11)
y/1 Cly
Although this formulation allows for modeling of discrete-time inputs, the assumption of linear (LTT) restricts its flexibility. This limitation makes SSMs less adaptable to dynamic or non-uniform data patterns, where the system dynamics may change over time.
A.2 Mamba: Linear-Time Sequence Modeling with Selective State Spaces
extends the classical State Space Model framework by introducing dynamic, input- dependent parameters, allowing the system to adapt in real time to varying input sequences. The key difference lies in the system's ability modify its parameters based on the current input, enabling more flexible representation of dynamic systems. The formulation of Mamba is as follows:
Mamba
25
h = A(x1)h-11 + B(xz)
(12)
y/ C(
where A(zz), Bxxz), and C(æx) are dynamic, learnable parameters that depend the input sequence. This flexibility allows Mamba to capture both long-range dependencies and local features, making it more adaptable complex data patterns.
distinctive feature of Mamba is its selective mechanism, which enables the model to decide which information should be propagated or discarded based the current input token. This selective information flow improves computational efficiency and enhances the ability of the model to capture crucial dependencies in the data. By adjusting the influence of different parts of the input, Mamba is able to focus on the most relevant information at each time step, leading to more efficient learning and better generalization.
A.3
Graph Neural Networks
Graph Neural Networks (GNNs) 80 are deep learning models tailored for graph-structured data, designed to extract high-order features from non- -un-Euclidean domains by modeling interactions between nodes. Unlike traditional deep learning models such convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which primarily target Euclidean data with regular structures (e.g., images and text), GNNs are specifically built for scenarios where data is inherently represented as graphs. Examples include social networks, molecular structures, and recommendation systems, where connections between entities are complex and dynamically evolving.
The core mechanism of GNNs message passing, which iteratively aggregates information from neighboring nodes to update node representations. This approach enables GNNs to capture global

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structural patterns and solve tasks such as node classification, link prediction, and graph classification, establishing their significance in graph representation learning.
Given a graph G = (V, F), where = {ui, u2,..., vN) is the set of nodes and E C V the of edges, each node E V is associated with an initial feature vector (0) RR,, and
edges Can E E may carry feature vectors h E R The neighborhood of a node u is denoted as (() {u vu, E). The update process each layer in GNN can be expressed as:
h 11+1)
Pupdate
(h.)) Paggregate (nh E(()))
(13)
where h the representation of node u layer Vaggregate aggregates information from neighbor- ing nodes (e.g., sum, mean, max), and @updaae updates the node representation using this aggregated information.
Early implementations of GNNs were introduced by Scarselli et al. [80]. forming the foundation for subsequent advancements. Over time, specialized variants have been developed to enhance the expressiveness and efficiency of GNNs in different applications.
Graph Convolutional Network (GCN): The GCN model [8] simplifies the pressag-ppassing pro- cess by using spectral graph convolutions. enhances local feature propagation through normalized adjacency matrices, making it efficient for smoothing features across neighbors. Its layer-wise update defined as:
((+ o (D - & HOW
(14)
where Á = + is the adjacency matrix with self-loops, is the degree matrix, and W(!) Rd () is learnable weight matrix.
E
Graph Attention Network (GAT) GAT 82 introduces attention mechanism, allowing nodes to dynamically weigh the importance of their neighbors during aggregation. This enables GAT to capture heterogeneous relationships within the graph effectively. The update rule is:
h h++1)
h
(15)
(EN(()-(()
where ( attention coefficients computed by attention mechanism (typically shared linear transformation followed by a LeakyReLU activation and normalized via Softmax across u), and w'!) is learnable weight matrix for transforming node features. Note the inclusion of v in its own neighborhood for self-attention.
is designed to achieve maximum discriminative power, theoretically matching the expressiveness of the Seisfeiler-eehman (WL) graph isomorphism test. It optimizes aggregation and update functions, often using Multi-Layer Perceptron (MLP). A common update rule is:
Graph Isomorphism Network (GIN) GIN 83
(+1) = MLP
(1+c
h
Σ
h
(16)
(EN(e)
where MLP(() is layer-specific MLP, and ( is either a learnable parameter fixed scalar that adjusts the weight of the central node's 's feature.
GraphSAGE GraphSAGE (Graph SAmple and aggreGatE) 84 focuses on inductive representation
learning, enabling GNNs to generalize to unseen nodes or entirely new graphs. Instead aggregating features from all neighbors, it typically samples fixed-size neighborhood for each node and applies an aggregation function. A general update step can be written as:
h ( (b) AGGREGATEE" (nh | u Naampled (v))
(17)
hg = (Wr). - CONCATON h0 (o,))) bb)).
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where sampleed()) (v) is the sampled neighborhood of AGGREGATE can be functions like mean, LSTM, pooling, CONCAT denotes concatenation, and W and b are learnable parameters. GraphSAGE is particularly effective for inductive tasks.
Each of these models enhances GNN capabilities through distinct technical innovations, achieving great performance in tasks like node and graph classification.
B Discussion on Theoretical Limitations
This section provides an objective discussion on the inherent theoretical limitations of the foundational modules our model relies on (GNNs and SSMs), as well as the analysis of the interpretability of our proposed APN module. Understanding these limitations is key to clarifying the trade-offs and motivations behind our EventMG hybrid architecture.
B.1 Limitations of Graph Neural Networks
GNNs based on message passing are powerful for processing structured data, but they also face several inherent theoretical challenges:
posssage-passingg GNNs, the nodes' features are formed by aggregating informa- tion from their neighborhoods. As the network deepens, the receptive field of each node expands and begins to significantly overlap with others. This causes the representations of different nodes to become homogenous, ultimately losing their distinctiveness and discriminative power. Consequently, modeling long-range dependencies by simply stacking GNN layers is extremely difficult, as the deepening of the network is the very cause of this feature smoothing.
Over-smoothing:
Scalability Limitations:
When processing large-scale graphs or those with high-degree nodes, the computational and memory costs of GNNs can grow exponentially with depth, a phenomenon known "neighbor explosion." " This poses significant challenges model scalability.
These limitations, particularly the over- weer-soothhng problem, highlight the inadequacy of solely stacking GNN layers for long-range dependency modeling and motivate the integration with other paradigms, such as SSMs.
B.2 Limitations of State Space Models
SSMs, as represented by Mamba, demonstrate excellent capabilities in capturing long-range depen- dencies with linear complexity. However, their nature sequence models also introduces inherent limitations:
aapology-agnostie Nature: The fundamental design of an SSM is to process one-dimensional, ordered sequences, where its core function to understand "before-and-after" relationships. However, the structure of high-dimensional data such as graphs and images is topological and lacks single, natural sequential order. Therefore, before applying SSM, the data must be "flattened" into a ID sequence through scanning process. This step performance bottleneck: poor serialization strategy can destroy the original local topology, preventing the SSM from learning meaningful patterns. Consequently,
SSM's effectiveness is highly dependent the design of "smart serializer" that can effectively encode high-dimensional structural information into a ID sequence.
Inherent Unidirectionality: Standard SSMs, including Mamba, causal models that only process unidirectional information flow. This is a limitation for many perception tasks that require global context, unlike autoregressive generation tasks. Although this can be compensated for by using bidirectional scanning. typically requires additional computational and design complexity.
These limitations, especially the topology aggnostic nature, highlight the necessity of designing "smart serialization" front-end for SSMs that is aware of multidimensional structures.
B.3 Limitations in the Interpretability of APN
We had planned to provide a more intuitive validation for the APN module's Effective Coordinate Space hypothesis by visualizing the coordinate perturbation values (Az, Ay, At). Our initial assump-
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tion was that these perturbations might exhibit an easily interpretable pattern, such as larger spatial perturbations occurring object edges achieve a separation between foreground and background.
However, after conducting rigorous and comprehensive experiments on multiple samples, we found that these perturbation values exhibit a high degree of non-linearity and sample dependency. Although the expected trends were observed in a minority of samples, in most cases, the distribution of perturbations presented complex patterns that are difficult to interpret intuitively. We attribute this primarily to the following reasons:
The Non-linear Nature of Optimization: The perturbations learned by APN not meant to align with human intuition. Instead, they are driven by the final task loss to create optimal 1D sequences for the subsequent MSM and Mamba modules. This optimization process is highly non-linear, and its final solution may be complex yet effective mapping that is not directly interpretable by humans.
The effectiveness of APN likely stems from the collective effect of all node perturbations, rather than relying on a few individual nodes producing large, intuitive offsets. While the perturbation of single node may have limited significance, the aggregate effect of all perturbations collectively forms a coordinate space that is more advantageous for scanning.
Collective vs. Individual Effect:
We believe that presenting only a few bell-behhvedd samples carries the risk of cherry- picking evidence and may not objectively reflect the general behavior of APN in all cases. This essentially touches upon the challenge we have already noted in the main Discussion section: the interpretability of complex models' internal mechanisms. Based on this consideration, we have decided to omit this visual analysis from the current version and plan to explore this more challenging topic as a dedicated direction for mechanistic research in future work.
C Downstream Task
C.1 Graph-to- Craph-to-Vision Pyramid
The Graph-to- Vision Visiion Pyramid (GVP) module specifically designed for downstream vision tasks, with the core objective of converting the sparse, unstructured graph node features from upstream modules into the dense, multi-scale pyramidal feature maps favored by vision applications.
The core of this transformation is an efficient, index-based Sparse-to-Dere Mapping strategy. Specif- ically, for each graph node feature h, that fuses micro- and macro-level information, we place directly onto 2D feature grid at its corresponding original pixel coordinate (æi, y:). This process initially forms a sparse feature map. Subsequently, we process this sparse grid with standard convolu- tional layers to densify and convert it into a dense base feature map. From this base map, multi-layer projection operations are used generate feature maps at different resolutions, which collectively form the visual pyramid.
It is worth noting that while this process does not explicitly preserve the graph's 's edge structure, the key semantic information effectively embedded into the final multi-scale visual features because the input node features h, have already been deeply encoded with rich spatiotemporal and structural relationships by the upstream modules. This mapping strategy, being based entirely on indexing operations, highly parallelizable and therefore extremely efficient for GPU execution.
For non-visual tasks or those that only require a global feature representation, the GVP module can be replaced with other more suitable pooling or processing techniques. This modular design ensures the flexibility and adaptability of our overall framework to a diverse range of tasks.
C.2 Detection Head: RT-DETR
Our EventMG employs RT-DETR 85 as its detection head, with a parameter size of approximately 1.20M. RT-DETR (Real-Time Detection Transformer) is the first end-to-end real-time detection model, designed to accelerate the DETR (Detection Transformer) framework while enhancing query selection for improved detection accuracy. By addressing computational bottlenecks in traditional Transformer- based object detection, RT-DETR significantly enhances both efficiency and accuracy, making it practical choice for real-time vision applications.
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1. Architecture and Key Enhancements RT-DETR is composed of three principal components. First, the backbone extracts multi-scale features from the ipput image, serving as the foundation for subsequent processing. Second, the Efficient Hybrid Encoder improves computational efficiency by decoupling intra-scale feature interactions from cross-scale feature fusion, effectively reducing redundancy while maintaining rich contextual information. Third, the Transformer Decoder employs iterative refinement to optimize object queries using auxiliary prediction heads.
A key innovation in RT-DETR is the Uncertainty-Mininn refines target queries by reducing ambiguity, leading to greater robustness and a lower false positive rate. This mechanism ensures that the most relevant object representations are selected during detection, improving precision in complex scenes.
Query Selection mechanism, which
2. Loss Function RT-DETR follows and-to-end training approach similar to DETR, incorporat- ing a dynamic matching strategy for target assignment. The loss function is formulated as:
Xet + Xbox Lrox + >gion
(18)
where Less represents the classification loss, implemented using Focal Loss to improve the handling of imbalanced class distributions. The term Lbox is the LI loss applied to bounding box regression, ensuring precise localization of detected objects. Additionally, Lgoou employs Generalized IoU (GIoU) loss. which enhances bounding box alignment and optimizes spatial consistency between predicted and ground -truh boxes.
3. Performance and Efficiency RT-DETR achieves state-of-the the-art results on the COCO dataset, demonstrating a strong balance between detection accuracy and inference speed. RT-DETR-RST achieves 53.1% 1% mAP while rumning at 108 FPS
NVIDIA T4 GPU, while RT-DETR-RO R101 reaches 54.3% mAP at 74 FPS. These results outperform many existing YOLO-based models, positioning RT-DETR as highly effective choice for real-time detection scenarios.
Compared to conventional Transformer-baat detectors, RT-DETR significantly reduces computa- tional overhead while maintaining end-to-end training benefits. Its capability to efficiently process complex object scenes makes it particularly well-suited for event-based vision applications within the EventMG framework.
C.3 Detection Head: YOLOX
YOLOX [86 is included in our study as an optional alternative detection head. Although detection results based on YOLOX are not reported in this work, it remains one of the widely adopted detection heads in the field. As an advanced variant of the YOLO series, YOLOX enhances detection accuracy while maintaining computational efficiency. It improves upon YOLOv3 and YOLOv5 by introducing an anchor-free paradigm, a decoupled detection head, and the SimOTA target assignment strategy, effectively addressing the limitations of anchor dependency and suboptimal target assignment in earlier YOLO models.
Architecture and Key Enhancements The YOLOX architecture comprises four main com- ponents. The backbone, typically based on DarkNet53 or CSPNet, extracts hierarchical feature representations from the input image. The Feature Pyramid Network (FPN) and Path Aggregation Network (PAN) refine multi-scale feature fusion, significantly improving the detection of small objects. The decoupled detection head independently processes classification and regression tasks, reducing task confficts and improving both convergence speed and accuracy. Lastly, the SimOTA target assignment strategy dynamically assigns positive samples using optimal transport theory, leading to superior target matching and robustness.
Three major improvements distinguish YOLOX from its predecessors. First, the anchor-fres detection approach eliminates reliance on predefined anchor boxes, simplifying training and reducing the need for hyperparameter tuning. Second, the decoupled detection head enables separate processing for classification and localization, leading to higher precision in object detection. Third, the SimOTA tar- get assignment mechanism enhances label assignment flexibility, particularly benefiting the detection of occluded and small objects.
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2. Loss Function
The loss function of YOLOX is formulated as follows:
L Let + Are + XiauCiiu
(19)
where: Lats represents the classification loss (Cross Entropy Loss). Lreg denotes the bounding box regression loss (L1 loss). Lion is the loU-based loss, optimizing bounding box localization accuracy.
3. Performance and Efficiency YOLOX achieves state-of-the-an performance the COCO dataset. Specifically, YOLOX-L attains 50.09 AP on 640x640 input resolution, outperforming YOLOv5-L (48.2% AP). Additionally, its lightweight variants, YOLOX-Tiny and YOLOX-Namd achieve 32.8% and 25.3% AP, respectively, making them highly suitable real-time and resource- constrained applications.
Compared RT-DETR, YOLOX's anchor-free framework facilitates faster inference and lower computational overhead. However, it may be less suited for fully end-to-end detection tasks due to its separate processing pipeline. Within the EventMG framework, YOLOX is employed as an alternative detection head, offering flexibility for different event-based vision applications, particularly those requiring real-time performance.
D Hyperparameter Sensitivity Analysis
This section presents sensitivity analysis of several key hyperparameters in EventMG to validate the robustness of our model design and the rationale for our parameter choices. All experiments are conducted on the Genl dataset, with mAP used as the performance metric.
Table Hyperparameter sensitivity analysis. All experiments are conducted on the Gen dataset, with mAP the metric. Bold values indicate the default settings used in our paper.
N
70min
6a
Value
mAP
Value
mAP
Value
mAP
10,000
0,471
50
0.529
0.01
0,498
20,000
0.537
100
0.537
0.02
0.531
30,000
0.549
150
0.534
0.05
0,537
200
0.522
0.10
0.533
0.15
0.534
D.1 Impact of Event Count
In our method, we employ strategy of using fixed number of events, N (rather than fixed time window), to segment the event stream. This design effectively constitutes Activity-driven Dynamic Frame Rate: in scenes with dense events, the time required to acquire N events is short, equivalent to 'iigh frame rate": conversely, sparse scenes, time taken is longer, equivalent to a 'low frame rate'. This mechanism allows the model to avoid processing information-less 'empty windows,' thereby enabling efficient use computational resources.
To investigate the impact of the value of N on performance, we conducted sensitivity analysis, with the results shown in Table 6 The analysis clearly shows that when N is halved from 20,000 10,000, the model's 's performance drops significantly (-12.3%) due to insufficient input information and an inability capture the complete spatiotemporal structure. Conversely, increasing by 50% to 30,000 yields only marginal gain in performance (+2.2%). Therefore, N=20,000 is selected as an effective balance point between accuracy and efficiency for the model.
D.2 Impact of Minimum Component Size
The parameter 7min is used to filter out excessively small event clusters when constructing the macro-level component graph. In our design, Rmin acts merely a coarse-grained noise filter rather than a precise object segmenter. Its effectiveness based on key observation: event clusters generated by the motion of real objects are typically much larger than those formed by random noise.

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More importantly. our subsequent macro-micro fusion design provides degree of fault tolerance. The final refined predictions still rely on micro-level features, with macro-level information serving only contextual guidance. This means that even if the filtering by 1lmin is imperfect (e-g., one component containing multiple objects, or multiple components corresponding to single object), the model can still distinguish them based on micro-level features, which greatly reduces the system's s sensitivity to miin-
The quantitative analysis results, as shown in Table 0 confirm the robustness of our design. Adjusting Amin within a four-fold range (50 200) results largely stable model performance, proving that the overall architecture is not sensitive to this hyperparameter.
D.3 Impact of Spatiotemporal Distance Threshold
The threshold 5st is used define the neighborhood range of nodes when constructing the initial event graph. Our sensitivity analysis on this parameter (see Table 6 reveals a key phenomenon of asymmetric sensitivity.
one hand, when 6et is too small (e.g., 0.01), model performance drops sharply. This is because event data is not uniformly distributed in spacetime but rather clusters along contours and edges generated by object motion. An excessively small threshold fails to connect these physically close- related events, thereby disrupting the graph's fundamental topology.
On the other hand, once 6at reaches a reasonable value capable of capturing the local neighborhood (e.0.,20002), (e.g., 20.02), the model's performance enters a broad and stable operating range. This robustness to larger threshold values due to two safeguard mechanisms in our design: 1) The hard constraint of the maximum number of neighbors, k, prevents nodes from connecting to too many neighbors even with a large threshold; 2) The micro-macro fusion feedback architecture has strong fault tolerance, which can mitigate the noise effects of redundant connections.
This analysis indicates that our model does not rely on a fragile optimal value for 8g.. Although adaptive threshold schemes, such as [87), represent more advanced direction in the field, they are often accompanied by higher computational costs. How to strike a balance between the flexibility of adaptive methods and the simple efficiency of our current strategy is topic worthy of future exploration.
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Reviews:
[{"review_id": "dGGVbdJRNA", "reviewer": "Reviewer_rxhc", "summary": "This paper proposes EventMG, a Mamba-based lightweight architecture for event processing. The main technical innovation is its multi-level design that incorporates spatiotemporal details and global contexts. At the micro-level, the method enables event-wise information propagation using Mamba. Meanwhile, at the macro-level, it performs global reasoning on event clusters created by the Component Graph. Another technical contribution is two specialized modules, APS and MSM, that refine event coordinates and execute multi-directional scans to enhance Mamba. EventMG has been demonstrated on two event-based vision tasks: object detection and action recognition, where it shows significant efficiency in terms of parameter size, FLOPs, and latency, while maintaining competitive accuracy to the advanced models.", "questions": "1. Please clarify the points stated in the weaknesses. Due to the lack of clarity, I might be overlooking some strengths (or possibly weaknesses) of the method.\n\n2. The proposed method processes each event frame independently. How can this method be extended to incorporate temporal context between frames?\n\n3. The proposed method operates on a frame-by-frame basis, which may limit its application in real-time contexts, as the frame rate is constrained by the temporal size of a single frame. Conducting a sensitivity analysis on the temporal window size could be beneficial to evaluate the trade-off between accuracy and frame rate.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 1, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n1. The proposed method efficiently processes sparse events without the need for frame representation.  \n2. The method has been thoroughly evaluated across various tasks and benchmarks, achieving competitive accuracy while significantly reducing computational requirements.  \n3. The effectiveness of the proposed components is clearly demonstrated through ablation experiments.\n\n**Weaknesses**\n\nWhile the strong performance of the proposed method is impressive, some sections of the paper lack clarity, which hinders a complete understanding of the method. In particular, the following points remain unclear:\n\n1. There is no explanation provided for how event-wise features are input into the head. I wonder if it requires constructing a dense representation from the sparse event-wise features to adapt to the standard vision head.\n\n2. It is unclear how the constructed event graph is utilized in SEST. It appears that Mamba does not reference the graph structure, rather it scans in the order sorted according to each space-time axis.\n\n3. The DWConv in Eq. 2a is applied to the node features of the graph, which raises questions about the operation being performed because DWConv requires dense features as inputs.\n\n4. SEST is described as containing Mamba (as seen in Eq. 4), but in Figure 1, the Mamba Block is positioned after SEST. While this may not be a critical issue, it may confuse the reader.\n\n5. Furthermore, it is unclear whether the Macro-level is calculated after the Micro-level or if they are processed in parallel. Figure 1 shows a left-facing arrow from the Micro-level to the Component Graph, but lacks an explanation, leaving the details ambiguous.", "was_revised": true, "final_justification": "The authors have clarified my concerns regarding the technical details and outlined their direction for revision. I believe the paper meets the acceptance criteria, and I will maintain my rating as borderline acceptance.", "rebuttal": "**W1:** Thank you for this important question. You are correct: our method converts sparse event-wise features into a dense representation in the final stage to interface with standard vision heads. This design is motivated by two key considerations:\n\n- **Compatibility with Established Paradigms:** Leading detection heads like DETR and YOLO are tailored for dense prediction. As end-to-end sparse event detection remains an open challenge, the hybrid architecture—sparse backbone with a dense head—offers a practical and widely adopted solution in event-based vision, consistent with prior works [1–4].\n\n- **Preservation of Spatiotemporal Context:** Our backbone encodes rich spatiotemporal features per event, which are then mapped to corresponding pixel locations via an efficient sparse-to-dense aggregation. This process spatially arranges high-level features—not raw events—thereby preserving temporal semantics and minimizing information degradation.\n\nWe acknowledge that sparse-to-dense conversion is a necessary design trade-off to leverage modern high-performance dense detectors, involving information aggregation rather than simple loss. To mitigate its impact, our method encodes rich spatiotemporal context into each event feature prior to conversion. We agree that end-to-end sparse detection is promising and are exploring it.\n\n[1] Event-based asynchronous sparse convolutional networks. ECCV2020  \n[2] Aegnn: Asynchronous event-based graph neural networks. CVPR2022  \n[3] From chaos comes order: Ordering event representations for object recognition and detection. ICCV2023  \n[4] State space models for event cameras. CVPR2024  \n\n\n**W2:** Thank you for your insightful question and keen observation. You're absolutely right—Mamba, as a sequence model, does not directly process graph topology. Instead, the micro-level graph serves as a vital **preprocessing and local feature enrichment** mechanism in our architecture, structured as two sequential stages:\n\n1. **Graph-based Local Feature Enhancement:**  \n   We first construct a spatio-temporal event graph, then apply a lightweight GNN to update each node’s feature. This step enriches each node from “who I am” to “who I am relative to my neighbors,” embedding meaningful local context without global propagation.\n\n2. **Mamba-based Global Context Modeling:**  \n   The SEST module arranges these locally-enhanced features into multiple 1D sequences along various spatio-temporal directions. Mamba then processes each sequence independently, leveraging its strong capacity for modeling long-range dependencies with linear efficiency.\n\nThis pipeline mirrors the design of Vision Transformers (ViT) [5]: CNNs produce locally informed “image tokens,” which are then globally contextualized via Transformers. Similarly, our GNN produces “event tokens” enriched with local structure, and Mamba builds global dependencies among them.\n\nWe will clarify this two-stage design and update the diagram to reflect the GNN’s role.\n\n[5] An image is worth 16x16 words: Transformers for image recognition at scale.  \n\n\n**W3:** Thanks for your meticulous review. We sincerely apologize for a major oversight in the original manuscript: the mention of \"DWConv\" was incorrect—our actual implementation uses a standard Conv1d. We regret the confusion this may have caused.\n\nTo clarify the design rationale: our APN module enhances features via complementary Local and Global paths. The Local path applies Conv1d to timestamp-sorted nodes, capturing short-term temporal dynamics among neighboring events. The Global path uses a point-wise Linear layer to extract broader, context-independent patterns. Fusing both outputs allows APN to integrate fine-grained temporal cues with global feature semantics.\n\nWe have fully corrected this inconsistency in the revised manuscript, including updates to Figure 2, Section 3.2, and all related equations to reflect the actual Conv1d implementation. We truly appreciate your help in identifying this issue.\n\n**W4:** Thanks for your detailed review. We sincerely apologize for the confusion caused by the unclear process description in the original manuscript, particularly regarding the previous Equation 4. In the revised version, we've distinctly separated this process into two independent stages: 1) MSM constructs the sequence set, and 2) EventsMamba processes this sequence set. This aligns with the flow shown in Figure 1.\n\nSpecifically, the MSM module's function is now strictly limited to constructing a multi-directional set of event sequences. It sorts the node set $V_i$ according to different directions $d_k$, generating a sequence set $\\mathcal{S}$:\n$$\\mathcal{S} = \\left( S_k = \\mathrm{Scan}(V_i, d_k) \\vert d_k \\in D \\right)$$\nwhere the set of directions is $D = \\{t^+, t^-, x^+, x^-, y^+, y^-\\}$. We will replace the original formula in Section 3.1.2 with this description and adjust the related text accordingly.\n\nSecond, the EventsMamba module receives this set $\\mathcal{S}$ as input, independently applies the core Mamba processor to each sequence $S_k$, and then aggregates all processed sequence results into the final node features $h_v$:\n$$h_v = \\underset{S_k \\in \\mathcal{S}}{\\text{Aggr}} \\left( \\text{Mamba}(S_k) \\right).$$\nWe've updated these descriptions and equations in the relevant methodology sections, ensuring logical consistency throughout the paper.\n\n\n**W5:** Thank you for identifying this important ambiguity in our pipeline. The interaction between our micro- and macro-level processing is serial and mutually-feeding, not parallel. Our workflow is described below:\n\nFirst, The model first processes events at the micro-level, generating initial, fine-grained features for each event node.\n\nSecond, These micro-level features are then used to construct the Component Graph, from which macro-level features are extracted to represent global context. The leftward arrow noted in Figure 1 depicts this micro-to-macro dependency.\n\nFinally, the extracted macro-level features are fed back to refine each micro-level node’s representation. This allows each event node to become context-aware, achieving deep interaction between micro and macro scales.\n\nWe completely agree that presenting this workflow clearly is paramount. To address this, we’ll revise the manuscript by: (1) adding a process overview, (2) refining Figure 1 with sequential labels, and (3) expanding its caption.\n\n\n**Q1:** Thank you for your summary and the chance to clarify. In the preceding responses, we have provided detailed explanations for each technical point (W1–W5) you raised and have revised the manuscript accordingly.\n\nWe sincerely hope that this series of clarifications clearly illuminates the underlying logic and strengths of our method's design, facilitating a more comprehensive evaluation on your part. Should any ambiguities remain, we would be pleased to provide further explanation.\n\n\n**Q2:** Thanks for this important question about temporal context. We sincerely apologize for the insufficient description of our data preprocessing in the original manuscript, which led to your misunderstanding that our method processes independent \"event frames.\" In reality, our approach ensures the effective integration of temporal context through a dual mechanism at both the data and model levels, rather than handling isolated event slices.\n- **Data Level:**  \n  We follow established practices from [1, 2], employing a fixed event count sliding window strategy to process continuous event streams (rather than fixed time intervals). A fixed count ensures each processing slice contains a stable amount of information and adapts to scene activity intensity. Meanwhile, the sliding window creates a smooth physical connection between slices, effectively mitigating boundary effects at the input end.\n- **Model Level:**  \n  Furthermore, we leverage Mamba's inherent advantage as a State Space Model (SSM) to implement cross-slice state-passing: after processing one slice, its hidden state is used as the initial state for the next. This allows contextual information to flow continuously across slice boundaries within the model, enabling more long-range and coherent spatio-temporal dependency modeling.\n\nWe apologize again for the previous oversight. In the revised manuscript, we'll add a dedicated subsection on data preprocessing, detailing this strategy and its rationale to fully clarify your concerns.\n\n[6] Rethinking efficient and effective point-based networks for event camera classification and regression. TPAMI2025  \n[7] Event-based vision: A survey. TPAMI2020  \n\n\n**Q3:** Regarding your concern about the “frame rate limitation,” as clarified in our response to Q2, our method does not rely on fixed time windows but instead adopts a *Fixed Event Count* strategy. This inherently realizes an *activity-driven dynamic frame rate*: in high-motion, event-dense scenarios, the target event count is reached quickly—effectively yielding a high frame rate; in static, sparse settings, accumulation takes longer—resulting in a lower frame rate. This design avoids processing uninformative intervals and significantly improves computational efficiency by leveraging event sparsity.\n\nWe analyzed the impact of event count N on detection performance:\n\n| **Event Count (N)** | **mAP (%)** | **Inference Time (ms)** |\n|-|-|-|\n| 10,000 | 47.1 (−12.3%) | 5.79 (−7.1%) |\n| 20,000 | 53.7 | 6.23 |\n| 30,000 | 54.9 (+2.2%) | 8.21 (+31.8%) |\n\nAs shown, N = 20,000 offers a favorable trade-off between accuracy and efficiency. Reducing N to 10,000 yields only a 7.1% speedup but causes a substantial 12.3% drop in accuracy due to insufficient spatiotemporal context. Conversely, increasing N to 30,000 brings only marginal accuracy gains (+2.2%) while significantly increasing inference time (+31.8%). We have included this analysis and discussion in the revised manuscript."}, {"review_id": "LSNazG2CAR", "reviewer": "Reviewer_1mAU", "summary": "This paper, EventMG, introduces a lightweight, multilevel architecture designed for learning efficient spatiotemporal representations from asynchronous, sparse event camera data. As claimed, the proposed framework combines:\n- Micro-level modelling via mamba-based event graphs, capturing long-range, fine-grained temporal dynamics.\n- Macro-level modelling via component graphs, which represent clusters of events to encode global structure.\n\nFurther, a spatiotemporal-aware event scanning technology method that enhances Mamba’s adaptability using: (1) an attention-based perturbation network, and (2) a multidirectional scanning module is introduced. Experimental results on object detection and action recognition benchmarks show that EventMG achieves comparable performance with SOTA methods with fewer parameters and computation.", "questions": "See the \"weaknesses\" section.", "limitations": "Even though the authors have mentioned about the generalization to other event-based tasks and robustness against distribution shifts, the following key limitations should also be addressed.\n- Applicability for online learning and/or streaming\n- Robustness to noise, given that hardware jitter can present and contribute in the micro-level graph due to fixed radius thresholding", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n- The proposed architecture seems novel with marrying the state-space models and GNNs while also attending to both local and global (spatial) information. SEST seems to be useful as an adaptation of the SSM-based Mamba for sparse event data.\n- The claimed linear complexity is promising towards event-based tasks. \n- The experiments cover a wider range of datasets (both smaller and more challenging ones), two prominent tasks, and ablations which shows EventMG typically balances the accuracy and efficiency.  \n\nWeaknesses\n- As authors pointed out in the section 5, the task diversity is limited which should have been improved by expanding the experiment space (as an example, testing on classification datasets, high resolution gesture/activity recognition datasets) since the method is presented as a generic candidate for event spatio-temporal learning. \n- Even though GNNs are known to be parameter-efficient, the computations to this end can be costly. Therefore, I believe that apart from number of parameters and inference time, memory footprint (especially given that each event is considered to be a node in micro-level and the complexity of adjacency matrix is $O(N^2)$) and total wall-time should also be reported to further validate the efficiency of the proposed method. \n- In coupling with the above point, the authors do not explicitly mention about any event accumulation technique used (such as fixed time interval or fixed event volume) for event batching/accumulation which can be critical for evaluating memory efficiency of the method. As an example, if fixed event volume-based accumulation is used, it guarantees an upper bound for the complexity of the adjancency matrix. If not, did the authors use all the events within an event recording/instance? If yes, I doubt the rationale behind that.  \n- Does inference time include initial preprocessing including graph construction as well?\n- When constructing the micro-level graph, this work used a fixed thresholding approach which can be highly inefficient in instances leading to non-uniform event density such as highly sparse or highly dense spatio-temporal regions at which the assumption of event neighbourhood consistency does not hold. Additionally, the overall performance can be highly sensitive to the set threshold as well. In this direction, an adaptive thresholding for constructing the edges would work beneficial [1].\n- In component graph thresholding, the paper briefly mentions a threshold ($n_{min}$) for selecting valid clusters, but it does not analyze sensitivity to this hyperparameter or its effect on generalization. If the target is to build a meaningful global scene extraction from a highly varying scene with multiple objects through a connected component graph, it seems to be difficult to assign a hard (global) threshold for the minimum number of objects (to be ideally within the scene).  \n- APN attempts to \"optimize\" the event coordinates in the spatiotemporal space targeting more distinctive features for MSM to retrieve. Given that the final activation is tanh, how do the authors analyse the bounds and effectiveness of this module, particularly in a qualitative and empirical fashion? As an example, can the authors visually present a post-hoc analysis on what happens to event graph after this module? I feel like finding the \"right\" edges is more critical than refining the positions in this context. \n- In multiple places (line 62, 188 etc.), the authors claim that the their micro-level (local) spatiotemporal graph modelling is beneficial for learning \"long-range\" spatio-temporal context, particularly due to Mamba architecture. Is this claim because of (1) the micro-level graph is built using all the events in the recording and (2) SEST scans through all the directions throughout the event span? If not, I feel like this is a over-stretched claim. \n- Even though the proposed method performs competitively on object detection (Table 1) and some action recognition datasets (Table 2 and 3), its performance on HMDB51-DVS and UCF101-DVS datasets are significantly lower than the SOTA. Do the authors have any explicit explanation for this behavior?\n- I feel like more ablations, both module-wise and within modules, are needed to evaluate the necessity and validity of the each proposed module: as an example, to see how (1) MSM directionality affect results across different motion patterns and (2) varying micro and macro graph sizes affect results across different tasks/scenes.\n\n[1] Bandara, Nuwan, et al. \"EyeGraph: modularity-aware spatio temporal graph clustering for continuous event-based eye tracking.\" Advances in Neural Information Processing Systems 37 (2024): 120366-120380.", "was_revised": true, "final_justification": "As almost all of my initial and follow-up concerns are adequately addressed, I lean towards accepting the paper, contingent upon the inclusion of new ablation results and clarifications in the paper and/or appendix.", "rebuttal": ""}, {"review_id": "rQP5QT1fYk", "reviewer": "Reviewer_NxX4", "summary": "This paper introduces EventMG, a lightweight, multilevel Mamba-Graph architecture designed for efficient spatiotemporal representation of event camera data. It combines a micro-level Mamba-based State Space Model, enhanced by a Spatiotemporal-aware Event Scanning Technology (SEST), with a macro-level graph-based representation of event clusters. Through a fusion of long-range event dependencies and local-global structural cues, EventMG effectively models the asynchronous, sparse nature of event data and achieves strong performance on object detection and action recognition tasks.", "questions": "1. Can the Multidirectional Scanning Module (MSM) be considered a form of data augmentation that effectively increases the diversity of training data? The MSM appears to contribute significantly to performance improvements, as evidenced in Table 5. I’m also curious whether similar scanning strategies are commonly adopted in related methods. If my understanding is incorrect, I would appreciate any clarification.", "limitations": "See weaknesses. My primary concern lies in the necessity of each specifically designed component within the overall complex network architecture. Given that the reported experimental improvements are relatively modest, a more in-depth analysis or justification of the full design would be valuable. I'm willing to re-evaluate this paper based on the author's feedback.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper clearly articulates its motivation to tackle key issues in prior approaches, such as spatiotemporal precision, representational adequacy, and computational overhead. These concerns are well-founded and convincingly presented.\n2. The manuscript is well-organized and written in a clear, accessible manner, making it easy to follow.\n\nWeaknesses:\n1. The overall architecture appears somewhat complex. Although the rationale behind each network component is explained in the respective sections, the paper would benefit from additional experimental validation, such as ablation studies that replace specific modules with alternative designs or visualizations that highlight the contributions of tailored components. This would provide more intuitive insights beyond the quantitative results reported in Table 5.\n2. While Table 5 shows a significant reduction in model parameters, this does not seem to translate into substantial practical speedup, for example, when compared to EGSST-B without the SSM module. This might be due to the sophisticated architecture being less amenable to GPU acceleration. Additionally, reporting FLOPs alongside inference time would offer a more comprehensive assessment of the model’s computational efficiency.", "was_revised": true, "final_justification": "Most of my concerns have been addressed through the discussion. I’m leaning towards raising the score accordingly.", "rebuttal": ""}, {"review_id": "mOiNASpZ2z", "reviewer": "Reviewer_Xb3d", "summary": "The paper introduces a hybrid network building on state space models and GNNs to process events. They evaluate the method on object detection and action recognition.", "questions": "- Did you perform any subsampling of events before building the graph (eq. 1)?\n- How did you find specific parameters like the scaling coefficient and threshold delta_st?", "limitations": "The authors mention some limitations in the discussion section, especially a discussion of theoretical limitations, e.g. of GNNs or SSMs, could strengthen the paper.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strength**\n- Addresses a challenge in event vision to build neural networks that use the sparse nature of the event data.\n- The empirical results for object detection and action recognition in combination with a low parameter count are promising.\n\n**Weaknesses**\n- The presentation of the network is a bit confusing. I think the overall workflow and how SEST is embedded in it, could be explained better.\n- There are limited ablations. Tab. 5 shows ablations for the main modules, but there seem to be a lot of hyperparameters (e.g., component size threshold).", "was_revised": true, "final_justification": "I thank the authors for their extensive reply to my concerns. I updated my rating to borderline accept.", "rebuttal": ""}]

Author rebuttal:

Meta-review: All reviewers are aligned that the paper be accepted with some swapping to accept after substantial discussion. The AC thanks the reviewers and authors for the detailed discussion and agrees with the reviews that the paper should be accepted to NeurIPs.
Decision: Accept (poster)