Recognition: unknown
CNN-based Multi-In-Multi-Out Model for Efficient Spatiotemporal Prediction
Pith reviewed 2026-05-09 14:54 UTC · model grok-4.3
The pith
MIMO-ESP uses a CNN-based Transformer with independent time axis and dilation to achieve efficient and high-performance spatiotemporal prediction.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
MIMO-ESP considers global information and significantly improves complexity by configuring a Transformer architecture based on CNN. In addition, it treats the time axis as an independent axis without combining it, and effectively considers spatiotemporal information together by applying dilation. This structure makes MIMO-ESP efficient and high performance.
What carries the argument
MIMO-ESP, a CNN-based Multi-In-Multi-Out model that configures Transformer elements using CNN, handles the time dimension separately, and applies dilation to jointly model space and time.
Load-bearing premise
The performance gains come primarily from the CNN-Transformer configuration, independent time axis, and dilation rather than from dataset-specific optimizations or training details not described.
What would settle it
A re-implementation and evaluation on the same three benchmark datasets where MIMO-ESP fails to show better accuracy or efficiency than the models it claims to surpass.
Figures
read the original abstract
Recently, Convolutional Neural Network (CNN) or Transformer architecture based models have been proposed to overcome the limitations of Recurrent Neural Network (RNN) based models in spatiotemporal prediction. These models prevent the inefficiency of parallelization limitation due to the sequential properties and stacked error due to the recursive method, and show high performance. Novertheless, there are still some challengies. First, CNN based models have difficulty considering global information due to the local properties of the kernel, and their performance is limited. In addition, information is mixed because the time axis is combined with the channel axis of the image for processing. Models based on Transformer architecture have high complexity due to the self-attention calcuation and take a long training time. In this paper, we propose a new structure model called CNN-based Multi-In-Multi-Out model for Efficient Spatiotemporal Prediction (MIMO-ESP) to overcome these limitations. MIMO-ESP considers global information and significantly improves complexity by configuring a Transformer architecture based on CNN. In addition, it treats the time axis as an independent axis without combining it, and effectively considers spatiotemporal information together by applying dilation. This structure makes MIMO-ESP efficient and high performance. Extensive experiment results on three promising benchmark datasets which including video, traffic, and precipitation prediction tasks demonstrate that the usefulness of MIMO-ESP due to the achieved competitive efficiency while outperforming existing models. Furthermore, the ablation study results demonstrate the usefulness of the components of MIMO-ESP, emphasizing the potential of the proposed approaches.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes MIMO-ESP, a CNN-based Multi-In-Multi-Out architecture for spatiotemporal prediction tasks. It claims to overcome RNN limitations (sequential processing and error accumulation), CNN limitations (local receptive fields and time-channel mixing), and Transformer limitations (high self-attention complexity) by configuring a Transformer-like structure on CNN foundations for global context, treating the time axis as independent, and applying dilation to jointly model spatiotemporal information. The authors assert that this yields both higher performance and competitive efficiency, validated through experiments on three benchmark datasets covering video, traffic, and precipitation prediction, plus ablation studies confirming the value of each component.
Significance. If the performance and efficiency claims are supported by detailed, reproducible experiments with proper baselines and ablations, the work could contribute a practical hybrid architecture that balances global context capture with reduced computational cost for spatiotemporal forecasting applications.
major comments (2)
- Abstract: The central empirical claims—that MIMO-ESP outperforms existing models while achieving competitive efficiency—are asserted without any quantitative results, specific metrics (e.g., MSE, PSNR), baseline comparisons, error bars, training details, or ablation numbers. This prevents evaluation of the soundness of the performance and usefulness assertions.
- Abstract (and implied Method section): The description of how a 'Transformer architecture based on CNN' is configured to consider global information and reduce complexity, along with the independent time-axis treatment and dilation mechanism, remains at a high level with no equations, pseudocode, or architectural specifications. Without these, it is impossible to assess whether the design actually achieves the claimed properties or introduces new issues.
minor comments (2)
- Abstract: Multiple typos and grammatical issues ('Novertheless' → 'Nevertheless'; 'challengies' → 'challenges'; 'calcuation' → 'calculation'; awkward phrasing in 'demonstrate that the usefulness of MIMO-ESP due to the achieved competitive efficiency while outperforming existing models').
- Abstract: The sentence structure in the final paragraph is unclear and should be revised for readability.
Simulated Author's Rebuttal
We thank the referee for the constructive comments on our manuscript. We address each major comment below and will revise the manuscript to improve the abstract's informativeness and the clarity of the architectural description.
read point-by-point responses
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Referee: Abstract: The central empirical claims—that MIMO-ESP outperforms existing models while achieving competitive efficiency—are asserted without any quantitative results, specific metrics (e.g., MSE, PSNR), baseline comparisons, error bars, training details, or ablation numbers. This prevents evaluation of the soundness of the performance and usefulness assertions.
Authors: We agree that the abstract would benefit from quantitative support. In the revised version, we will incorporate key results such as specific MSE or PSNR improvements on the video, traffic, and precipitation benchmarks, along with baseline comparisons and efficiency metrics (e.g., training time or FLOPs). The full Experiments section already contains these details with error bars and ablations; we will summarize the most salient ones in the abstract to allow immediate evaluation of the claims. revision: yes
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Referee: Abstract (and implied Method section): The description of how a 'Transformer architecture based on CNN' is configured to consider global information and reduce complexity, along with the independent time-axis treatment and dilation mechanism, remains at a high level with no equations, pseudocode, or architectural specifications. Without these, it is impossible to assess whether the design actually achieves the claimed properties or introduces new issues.
Authors: The abstract is intentionally high-level for brevity. The Method section of the full manuscript provides the configuration details for the CNN-based Transformer-like structure, including how global context is captured with reduced complexity, the independent time-axis handling, and the dilation mechanism. We will revise the abstract to include a brief reference to these elements and point explicitly to the Method section. If the current Method description is deemed insufficient, we will expand it in the revision with additional equations, pseudocode, and specifications to ensure the design can be fully assessed and reproduced. revision: partial
Circularity Check
No significant circularity; empirical architecture proposal with no derivational reduction
full rationale
The paper introduces MIMO-ESP as a CNN-based Transformer hybrid that handles global context, treats time as an independent axis, and applies dilation for spatiotemporal tasks. All performance claims are framed as results from experiments on video, traffic, and precipitation benchmarks plus ablations. No equations, parameter-fitting steps presented as predictions, self-citation load-bearing uniqueness theorems, or ansatz smuggling appear in the provided text. The central claims rest on empirical outperformance rather than any chain that reduces by construction to the model's own inputs or prior self-work.
Axiom & Free-Parameter Ledger
Reference graph
Works this paper leans on
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[1]
Backgound Spatiotemporal prediction is the task of learning meaningful features from past data including temporal and spatial information and accurately predicting future
INTRODUCTIONA. Backgound Spatiotemporal prediction is the task of learning meaningful features from past data including temporal and spatial information and accurately predicting future. It extracts complex spatial, temporal, and spatiotemporal correlations in a self-supervised manner using unlabeled data [1], and can play a key role in intelligent system...
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[2]
Recurrent-based SISO models Recurrent-based SISO models use a structure that combines CNN and RNNs as shown in Fig
RELATED WORKSA. Recurrent-based SISO models Recurrent-based SISO models use a structure that combines CNN and RNNs as shown in Fig. 2.1. Data corresponding to each time step is input into recurrent cell and processed sequentially. ⊗ and ⊕ denotes hadamard product and element-wise add, respectively. multiple Each recurrent cell contain convolution operatio...
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[3]
Preliminaries Given spatiotemporal data in with multiple time steps , the goal is to predict the next time steps out
METHODA. Preliminaries Given spatiotemporal data in with multiple time steps , the goal is to predict the next time steps out. Each input and output data is represented as a 5-dimensional tensor: in∈B×T×C×H×W and out∈B×T×C×H×W. , , , , and denote batch size, number of time steps, number of channels, height, and w...
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[4]
EXPERIMENTAL SETTINGSA. Dataset descriptions To comprehensively evaluate the prediction performance of MIMO-ESP, we conduct extensive experiments using three promising spatiotemporal benchmark datasets. These datasets were selected to evaluate spatiotemporal prediction performance in various scenarios, including video, traffic flow, and precipitation pred...
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[5]
EXPERIMENTAL RESULTS In this section, we report both quantatitive and qualitative experimental results and provide a detailed analysis of them. In quantitative comparison results, bold in each table indicates best performance and underline represents second. To evaluate the accuracy yet efficiency of the proposed MIMO-ESP, we conduct comprehensive compara...
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[6]
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
CONCLUSION We proposed novel accurate yet efficient recurrent-free MIMO model, MIMO-ESP. To improving MIMO models, we designed the novel structure that optimized for spatiotemporal prediction including novel patchfy and reshape process, two attention blocks including SA Block and STA Block. Specifically, novel patchfy and reshape process allows MIMO-ESP t...
work page internal anchor Pith review arXiv 2024
discussion (0)
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