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REVIEW 4 major objections 6 minor 57 references

Scalable Event Cloud Network for Event-based Classification

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read FECNet claims state-of-the-art action recognition from raw event clouds at 0.1 G MACs.

desk verdict A real efficiency-first architecture, but the SOTA claim is hinged on one unseeded UCF101-DVS split and should not be trusted until the authors provide the split and a leakage check. read the letter →

arxiv 2412.20803 v2 pith:HRU3NNMA submitted 2024-12-30 cs.CV

Hongwei Ren , Fei Ma , Xiaopeng Lin , Yuetong Fang , Hongxiang Huang , Yue Zhou , Yulong Huang , Haotian Fu
show 4 more authors
Ziyi Yang Youxin Jiang Xiangqian Wu Bojun Cheng
This is my paper · ORCID
classification cs.CV
keywords eventcamerascloudrepresentationfrequencydomainlearningactionrecognitionpointnetworksefficientneuralneuromorphicvision
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that raw event-camera outputs, treated as an ordered 'event cloud' of spatial, temporal, and polarity coordinates, can support state-of-the-art action recognition while using roughly one-thousandth of the multiply-accumulate operations of frame-based models. It proposes FECNet, a network that learns local groupings from event features rather than from raw coordinates alone, and that extracts spatial and temporal features with Fourier-domain filters instead of convolutions and recurrent or attention modules. If the claim holds, event-cloud processing becomes a practical backbone for low-power, high-speed event-based perception across classification, action recognition, and human pose estimation.

What carries the argument

The central object is the Event Cloud: an ordered, downsampled sequence of raw events with four coordinates (x, y, t, polarity), treated as a one-dimensional temporal signal rather than as a permutation-invariant point set. The key mechanism is the Fourier transform: the Spatial-FA module replaces a convolution over the feature dimension with a Hadamard product in the frequency domain, reducing per-point complexity from O($d^{2}$) to O(d log d), and the Temporal-FA module applies a learnable global filter to the spectrum along the event-time axis to model long-range temporal relationships. Around this mechanism sit the Differentiation Farthest Point Sampling, Event Feature-based K-Nearest Neighbour, and Coordinates Evolution Strategy, which construct local groups that respect the different meanings of the four event coordinates. Together they keep the input close to the raw sensor output while keeping the model cheap enough to handle tens of thousands of events.

What would settle it

Run FECNet on the event-number sweep of Table 8 with the Temporal-FA module replaced by a one-dimensional temporal convolution or a GRU of equal parameter count: if accuracy on N-Caltech101 or UCF101-DVS stays within noise, or if the learned global filters are near-uniform and removing them leaves results unchanged, the paper's attribution of long-sequence gains to the frequency-domain filter is settled false.

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Extended reading notes

Core claim

FECNet's central claim is that the finest-grained event representation, an ordered Event Cloud with coordinates (x, y, t, polarity), can be scaled to long event sequences without a computational explosion by moving feature extraction into the frequency domain. The paper reports state-of-the-art results on all three action recognition datasets it tests (DVS128 Gesture 98.9%, Daily DVS 99.3%, UCF101-DVS 91.6%), exceeding the prior best on UCF101-DVS by 16 percentage points, while keeping computational cost at 0.109 G MACs and throughput at about 230 FPS on DVS128 Gesture. On object classification and human pose estimation it reports accuracy comparable to or better than existing point-based and voxel-based methods, with the smallest model size among pose estimation baselines. The claimed source of these gains is the combination of an Event Cloud that preserves polarity and fine temporal order, a redesigned grouping-and-sampling module that treats each coordinate according to its role, and Fourier-domain filters that capture long-range dependencies globally at low cost.

Load-bearing premise

The load-bearing premise is that a global learnable filter applied to the Fourier spectrum along the event-time axis can capture long-range temporal dependencies better than attention or LSTM, at matched cost, without losing localization.

Editorial extensions

If this is right

  • Event Cloud can be processed at near-raw fidelity, so temporal information lost in frame or voxel conversion need not be sacrificed for efficiency.
  • Frequency-domain filtering along the time axis can replace attention and LSTM components for long event sequences at a fraction of the computational cost.
  • Polarity, integrated structurally during grouping rather than as an extra input channel, improves accuracy across classification, action recognition, and pose estimation.
  • The reported trade-off of 0.109 G MACs, roughly 230 FPS, and 98.9% accuracy on DVS128 Gesture makes event-cloud networks feasible for real-time, low-power vision systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural test the paper leaves implicit is to inspect the learned Temporal-FA filters on trained models: if the filters are nearly uniform or the accuracy gain vanishes when they are removed, the frequency-domain mechanism is not the true source of the reported long-sequence improvements.
  • Because the method relies on temporal order rather than permutation invariance, the same frequency-aware grouping could transfer to other ordered spatiotemporal point streams, such as timestamped LiDAR returns, where polarity is absent but time ordering still matters.
  • Since FECNet's accuracy keeps rising as the event count grows while a point-cloud baseline plateaus, a practical extension would be a runtime scheduler that adapts the number of events fed to the network based on scene activity, trading accuracy against latency.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes a point-cloud-style Event Cloud representation for event cameras, with an event-based Grouping and Sampling module (D-FPS, EF-KNN, CES), Spatial and Temporal Frequency-aware modules based on the Fourier transform, and a residual block. The authors evaluate the resulting FECNet on object classification, action recognition, and human pose estimation, reporting strong accuracy with very low MACs (e.g., 0.109 G on DVS128 Gesture) and near-SOTA or SOTA results across nine datasets. The claimed headline is that FECNet achieves SOTA on all three action-recognition datasets, with a particularly large gain on UCF101-DVS (0.916 vs 0.787). The manuscript also includes an event-number scalability study and an ablation of the main modules. However, the paper has internal inconsistencies (SECNet vs FECNet; ten vs nine datasets), and the largest empirical claim rests on a random, unseeded test split whose comparability to prior work is not established.

Significance. If the empirical results are reproducible, the paper is a useful contribution: the architecture combines raw-event fidelity with dramatically reduced MACs, reports throughput above 200 FPS, shows favorable preprocessing time for Event Cloud versus frames/voxels, and demonstrates scaling to much larger event counts than prior point-based methods. The complexity argument for replacing convolution with frequency-domain Hadamard products is concrete, and the ablation study, while incomplete, gives evidence that polarity, the event-based G&S module, and temporal frequency filtering each contribute. The code links in both abstract and full text are a strength, though the two links currently point to differently named repositories and must be reconciled. The main risk is not the mechanism but the reproducibility and comparability of the benchmark numbers, particularly the UCF101-DVS split and the absence of error bars for close margins.

major comments (4)
  1. [4.1, Table 4] The UCF101-DVS result that carries the SOTA claim (0.916 vs 0.787 for VMST-Net) is obtained on a "randomly selected 20%" test split with no seed, no stratification, and no check for source-video overlap between train and test. Because the reported accuracy advantage is +16.4 percentage points, this single number is load-bearing; without releasing the exact split (or a reproducible file list) and confirming that the same split is used for all baselines, the claimed SOTA on action recognition is not verifiable.
  2. [Title, Abstract, 4.1, Table 1] The manuscript is internally inconsistent about its own identity and scope: the arXiv title and abstract describe "SECNet" evaluated on "ten" datasets, while the full text (title, Section 4.1, Table 1, Section 5) describes "FECNet" evaluated on "nine" datasets. This is not just a typographical issue: the abstract's claim of ten datasets cannot be checked, and the naming mismatch prevents readers from connecting the paper to the code repositories (SECNet_ICML in the abstract, FECNet in the full text). The authors must harmonize these and either account for the missing tenth dataset or correct the abstract.
  3. [Tables 2, 4, 6] None of the main accuracy tables report variance or the number of seeds. Given that two of the three action-recognition gains are marginal (0.989 vs 0.988 on DVS128 Gesture; 0.993 vs 0.991 on Daily DVS), the differences could be within run-to-run noise; without mean ± std over multiple seeds, or a clear statement that all runs use fixed and reported seeds, the empirical claims are not supported at the precision implied by the tables.
  4. [3.3, Table 8] The paper attributes the long-sequence gains to the Temporal-FA module, but the evidence is aggregate: Table 8 compares FECNet with an LSTM variant and point-based networks, not with a temporal convolution or attention module at matched capacity, and no analysis shows which frequencies are preserved or discarded. Since the frequency-aware mechanism is the paper's central design novelty, a matched-capacity temporal-convolution ablation (or a learned-filter frequency analysis, e.g., plotting the magnitude of the learned filters) is needed to support the attribution; without it, the accuracy gains could be due to the Event Cloud representation or other modules rather than to the temporal frequency filtering.
minor comments (6)
  1. [3.3, Eq. (10)-(12)] The summation notation is garbled (e.g., "T −1X" and "j 2π T kn"), and "specturm" should be "spectrum"; these formatting errors obscure the DFT definitions and should be corrected.
  2. [Table 2] The column header "ALS-DVS" should read "ASL-DVS", and the caption contains typos such as "Excperimemt"; these should be fixed throughout the tables and text.
  3. [4.2] The batch size is described as "dynamically set to take full use of the GPU's memory", but no actual batch size, number of workers, or training seed is given; this makes the experimental setup hard to reproduce even with the code.
  4. [4.5, Table 5] There are typos such as "ACCuracy" and the claim of "one-thousandth of the MACs" is stated without naming the specific baseline; Table 5 compares only a subset of methods, so the claim should be made relative to the particular entries shown.
  5. [References, [21]] Reference [21] for the tonic package is incomplete: it lacks a proper title and should include a version or publication venue.
  6. [Algorithm 1, Figure 3] The captions and pseudocode contain incomplete sentences and inconsistent terminology, e.g., "Classifer Regressor" and "Get category ˆc Get 13 skeleton points ˆp"; these should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: FECNet's central claims are empirical benchmark results and standard frequency-domain operations, not reduced from their inputs.

full rationale

The paper's central claim is that FECNet achieves SOTA accuracy on action-recognition benchmarks while using very few MACs. This is an empirical result measured on held-out test data, not a quantity constructed from the network's own equations or fitted parameters. The Fourier modules are defined directly from the standard DFT and inverse DFT (Eqs. 10-12), with a learnable global filter; this is a normal, externally grounded architecture choice, not a prediction that reduces to its input. The Event Cloud representation is explicitly distinguished from Point Cloud by including polarity and preserving temporal order, so it is not a renamed known result. Several cited prior works are by the same group (TTPOINT [41], SpikePoint [42], EventMamba [43], FAPNet [25]), but they appear as related-work context or as comparison baselines, not as load-bearing justification for the current architecture, and the comparisons are empirical rather than derived from those papers. The ablation studies (Tabs. 7-8) test modules and event counts directly rather than assuming the conclusion. The UCF101-DVS 0.916 result relies on a randomly selected 20% test split with no stated seed, which is a reproducibility and comparability concern, but it is not circular because the accuracy is measured on held-out data rather than being generated by the method's definitions. No uniqueness theorem, ansatz smuggled via citation, or fitted-input-called-prediction step was found. The self-citations are not load-bearing, so under the stated rubric the appropriate score is 0.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper's central claims rest on standard Fourier mathematics, domain assumptions about event streams, and a hand-designed architecture with several unspecified hyperparameters. No new physical entities are introduced.

free parameters (5)
  • D-FPS scaling factor α = Not reported (vector in R4)
    Scales x, y, t, p coordinates before farthest-point sampling (Eq. 3). This hand-set hyperparameter changes which events become centroids and is not specified in the paper.
  • Number of sampled events T per dataset = 1024 to 10240 depending on dataset
    Table 1 sets point counts, e.g., 4096 for NMNIST, 8192 for N-Caltech101, 1024 for DVS128 Gesture. This choice directly affects accuracy and MACs and is dataset-specific.
  • Number of centroids/groups in first stage = 512, 1024, or 2048 per dataset
    Section 4.2 sets varying group counts, halved each loop. Hand-chosen per dataset and affects receptive field and efficiency.
  • K nearest neighbors = Not reported
    K in EF-KNN (Eq. 4) is not stated in the paper, yet it defines group size and is load-bearing for local feature extraction.
  • Random test split seed = Not specified
    For five datasets the 20% test split follows prior work but no seed is given, making the exact split and results non-reproducible.
assumptions (5)
  • standard math The convolution theorem for the discrete Fourier transform (Eq. 10-12)
    Used to replace convolution with Hadamard product in Spatial-FA and Temporal-FA modules.
  • standard math The spectrum of a real signal is conjugate symmetric, so only T/2+1 frequency components are needed
    Invoked to reduce filter size in frequency domain.
  • domain assumption Event Cloud requires only downsampling to retain task-relevant information
    Section 3.1 asserts Event Cloud is the closest representation to raw output, but no analysis shows what information is lost in downsampling.
  • domain assumption More events contain finer temporal information and monotonically improve performance
    Section 3.3 states performance is positively correlated with event count; Table 8 supports this only up to 10240 events, not asymptotically.
  • ad hoc to paper Global frequency filters can capture long-term temporal dependencies at least as well as attention or LSTM
    Section 3.3 claims this, but the paper provides no theoretical justification or ablation isolating this assumption for long sequences beyond Table 8.

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Cite this review

Pith. "Pith review of Scalable Event Cloud Network for Event-based Classification." pith.science (2026). https://pith.science/paper/HRU3NNMA

@misc{pith2026241220803,
  author       = {Pith},
  title        = {Pith review of: Scalable Event Cloud Network for Event-based Classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HRU3NNMA}},
  note         = {Machine review of arXiv:2412.20803}
}
read the original abstract

Event cameras are biologically inspired sensors garnering significant attention from both industry and academia. Mainstream methods favor frame and voxel representations, which reach a satisfactory performance while introducing time-consuming transformations, bulky models, and sacrificing fine-grained temporal information. Alternatively, Point Cloud representation demonstrates promise in addressing the mentioned weaknesses, but it has limited scalability in abstracting features of higher spatial resolution and longer temporal sequence events. In this paper, we propose a Scalable Network named SECNet to leverage Event Cloud representation. SECNet integrates polarity at the structural level by innovating the Event-based Group and Sampling module rather than only at the input level. To accommodate the surge in the number of events, SECNet embraces feature extraction in the frequency domain via the Fourier transform.This approach not only substantially extinguishes the explosion of Multiply Accumulate Operations but also effectively abstracts spatio-temporal features. We conducted extensive experiments on \textbf{ten} event-based datasets, and substantiate the scalability, effectiveness, and efficiency of SECNet. Our code will be available at: https://github.com/rhwxmx/SECNet_ICML.

Figures

Figures reproduced from arXiv: 2412.20803 by the authors.

Figure 1
Figure 1. Gain of FECNet on DVS128 Gesture. The horizontal [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Visualization of different representations in yin-yang [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. FECNet’s architecture. It accomplishes three tasks by processing the Event Cloud into a sequence of distinct modules: em [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visualization of Event Cloud’s coordinates and features [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.