Title: EventMG: Efficient Multilevel Mamba-Graph Learning for Spatiotemporal Event Representation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Event Camera

Mamba

Graph Neural Network

Spatiotemporal Representation Learning
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
**Strength**
- Addresses a challenge in event vision to build neural networks that use the sparse nature of the event data.
- The empirical results for object detection and action recognition in combination with a low parameter count are promising.

**Weaknesses**
- 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.
- 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).

QUESTIONS:
- Did you perform any subsampling of events before building the graph (eq. 1)?
- 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.
TITLE: Official Review
CLARITY: 1
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nSee 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\nSee the \"weaknesses\" section.\n\nLIMITATIONS:\nEven 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", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}