Title: FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational Frequencies
Venue: NeurIPS 2025
Accepted: yes
Keywords: Event cameras

Object detection

RGB-Event fusion

High-dynamic perception
Abstract: Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to the microsecond-level temporal resolution and asynchronous operation. Existing event detectors, however, are limited by fixed-frequency paradigms and fail to fully exploit the high-temporal resolution and adaptability of event data. To address these limitations, we propose FlexEvent, a novel framework that enables detection at varying frequencies. Our approach consists of two key components: FlexFuse, an adaptive event-frame fusion module that integrates high-frequency event data with rich semantic information from RGB frames, and FlexTune, a frequency-adaptive fine-tuning mechanism that generates frequency-adjusted labels to enhance model generalization across varying operational frequencies. This combination allows our method to detect objects with high accuracy in both fast-moving and static scenarios, while adapting to dynamic environments. Extensive experiments on large-scale event camera datasets demonstrate that our approach surpasses state-of-the-art methods, achieving significant improvements in both standard and high-frequency settings. Notably, our method maintains robust performance when scaling from 20 Hz to 90 Hz and delivers accurate detection up to 180 Hz, proving its effectiveness in extreme conditions. Our framework sets a new benchmark for event-based object detection and paves the way for more adaptable, real-time vision systems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes FlexEvent, an RGB-event fusion-based detector that adapts to varying input frequencies. It includes FlexFuse, an adaptive fusion module that integrates event and RGB data, and FlexTune, a strategy that adjusts supervision signals to improve generalization across input rates.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. Good presentation and clear methodology\n2. Comprehensive experiment setup\n3. Code is promised to be released, which will benefit the community\n4. Significant improvement over prior SOTA methods\n\n**Weaknesses**  \nSome details are confusing to me. Please refer to the **Questions** below.\n\nQUESTIONS:\nIn Section 3.1, regarding Event Processing:\n- Does Eq. (2) use Kronecker delta functions? This should be clarified; otherwise, the unspecified meaning of \\\\( \\delta \\\\) may puzzle readers.\n- The event representation and pre-processing follow the design in RVT, but no citation is provided here. A proper reference should be added.\n- The paper states that events are constructed into a 4D tensor, but it is unclear how such a 4D tensor is processed with 2D convolutions for subsequent RGB-event fusion. RVT gives a detailed explanation, and a similar clarification should be included here.\n\nIn Section 3.2, regarding FlexFuse:\n- For dynamic event aggregation with \\\\(\\Delta t_a = 100\\\\) ms and \\\\(a = 10\\\\), assuming \\\\(b = 100\\\\), the time interval is split into 10 ms sub-intervals, and a high-frequency event sample \\\\(E_b\\\\) is randomly selected from these sub-intervals. How does this ensure that the selected \\\\(E_b\\\\) (e.g., from 10--20 ms) is aligned with the prediction timestamp at the end of \\\\(\\Delta t_a\\\\) (i.e., 100 ms)?\n- RGB and event cameras may have different intrinsic/extrinsic calibrations and resolutions, and event images have object duplication due to temporal accumulation. Has the fusion method considered alignment between these two modalities?\n\nRegarding loss function: \n- In Eq. (7), what value is used for the weighting factor \\\\(\\lambda\\\\)? Also, if \\\\(\\lambda = 0\\\\) (i.e., no regularization is applied), how does this affect the performance? \n- Similarly, in the definition of \\\\( \\mathcal{L} _ {tune} = \\mathcal{L} _ {GT} + \\beta \\sum \\mathcal{L} _ {det}(\\tilde{y}, \\hat{y}) \\\\), what value is used for the coefficient \\\\(\\beta\\\\)? Also, when the frequency is sufficiently high, the low-frequency label \\\\(y\\\\) and the high-frequency pseudo label \\\\(\\tilde{y}\\\\) become very similar. I have concern about the necessity of the term \\\\(\\mathcal{L} _ {det}(\\tilde{y}, \\hat{y})\\\\).\n\nLIMITATIONS:\nThe limitations have been discussed in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes FlexEvent, a novel framework for event-based object detection that is designed to operate effectively across varying operational frequencies. The authors evaluate FlexEvent on multiple large-scale event camera datasets (DSEC-Det, DSEC-Detection, DSEC-MOD). The proposed method outperforms both event-only and event-frame fusion baselines, showing significant improvements in mean average precision (mAP), particularly under high-frequency and challenging dynamic scenarios. The framework also demonstrates efficiency in terms of inference time and parameter count, supporting its potential for real-time applications.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nRecognizing the limitations of existing event camera detectors—particularly their inability to adapt to high-frequency data and dynamic environments—the authors introduce two key components:\n1）FlexFuse: An adaptive event-frame fusion module that dynamically integrates high-temporal-resolution event data with the rich semantic information from RGB frames at the feature level. This fusion enables the detector to balance temporal and spatial information under different motion dynamics.\n2）FlexTune: A frequency-adaptive fine-tuning mechanism that leverages both labeled low-frequency data and unlabeled high-frequency data. Through iterative self-training and temporal consistency calibration, FlexTune generates reliable high-frequency pseudo-labels, allowing the model to generalize across frequencies without requiring extensive high-frequency annotations.\n\nWeaknesses:\nThe paper briefly discusses societal impacts and limitations (e.g., applications in autonomous driving). However, it would benefit from a deeper discussion on potential safety implications of detection errors in critical environments, and the risk of bias in the training data.\n\nQUESTIONS:\n1）Demonstrate robustness to modality-specific noise. The adaptive fusion in FlexFuse dynamically balances event and frame features, but it’s unclear how this affects performance in scenarios where one modality is significantly noisier (e.g., low-light frames or sparse events).\n2）Show FlexTune’s superiority over semi-supervised baselines. FlexTune uses pseudo-labels for high-frequency data, but the paper does not compare against other semi-supervised or self-training methods (e.g., consistency regularization, teacher-student models).\n3）Prove feasibility for edge deployment. While the paper claims efficiency, the model size (45.4M parameters) is larger than event-only baselines (e.g., RVT: 18.5M). How does this impact real-world deployment on resource-constrained platforms?\n4）Validate generalization to untrained frequencies. The model is evaluated on frequencies up to 180Hz, but it’s unclear how it extrapolates to intermediate or higher frequencies (e.g., 200Hz+).\n5）Address potential biases. The paper mentions societal benefits (e.g., autonomous driving) but does not discuss potential biases (e.g., performance disparities across object types or lighting conditions).\n\nLIMITATIONS:\nWhile FlexEvent demonstrates significant performance improvements, there are several known limitations to the paper, which can be summarized as follows:\nDependence on the Quality of Event Camera Data.\nLimited Generalization to Unseen Scenarios.\nResource Requirements for High-Frequency Data.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nFlexEvent proposes a method by pairing an adaptive fusion module (FlexFuse) with a self-training scheme (FlexTune) so the detector can process asynchronous event streams and low-rate RGB frames at any frequency between 20 Hz and 180 Hz, learning from unlabeled data to fill the supervision gap. The evaluation and DSEC datasets show FlexEvent outperforms other multi-modal and single-modal detectors while adding only ~1.5 M parameters and staying real-time.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength**\n- Intuitive Motivation: Fusing frame modality and label-efficient learning are the most promising and effective approaches for event camera detection. Combining them is intuitive, and the performance shown in this paper validates the motivation.\n- Label efficient frequency adaptation: FlexTune’s bootstrapping + temporal-consistency calibration exploits unlabeled streams to generalise from sparse 20 Hz ground truth to 180 Hz without manual annotation, which is a clear practical advantage over existing methods.\n- Real-time suitability: The lightweight fusion head only needs ~1.5M parameters, making FlexEvent outperform baselines without introducing extra computation overhead.\n\n**Weakness**\n- Novelty over concurrent work is under-analysed: The idea of combining frequency adaptation and modality fusion has been explored by other works. The work *Frequency-Adaptive Low-Latency Object Detection Using Events and Frames* also addresses high-frequency event-frame fusion. \n- Lack of generality analysis: All evaluations use DSEC datasets. It is unclear whether FlexEvent transfers to other scenes, such as indoor scenes.\n\nQUESTIONS:\n- Could you explain why the mAP increases from 57.4 to 60.1 as frequency increases from 20Hz to 36Hz, and then the mAP degrades as the frequency increases from 36Hz to 180Hz?\n- Could you compare FlexFuse and other works that combine frequency adaptation and modality fusion?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a fusion-based object detection pipeline that attempts to promote event+RGB fused object detection at varying frequencies. More specifically, a FlexFuse module is deigned to align the rich semantic information from RGB frames with the high-frequency event data, a FlexTune strategy is proposed to generate frequency-adjusted labels for unlabelled event streams. Experiments on the DSEC datasets demonstrate a superior performance of the proposed method over previous fusion-based methods. Also, the model maintains a robust performance across events of varying frequencies (up to 180 Hz).\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n- Fusing the complementary advantages of events and RGB images for object detection is a promising direction that can power applications that are sensitive to operational latency, for example, autonomous driving, industrial anomaly detection. In this context, exploring the robust detection across varying event frequencies and pushing its speed upper bound is important.\n- The proposed FlexTune strategy extends the classic pseudo label strategy to the event-RGB fused object detection, where high-frequency events are treated as unlabeled images, and several strategies grounded on the characteristics of events are deaigned to generate pseudo labels to boost the performance across different frequencies. \n- Experiments on the DSEC datasets (Car and Pedestrian classes) demonstrate the significant performance improvements of the proposed method. Meanwhile, the proposed methods maintain robustness across varying frequencies.\n- This paper is overall well-structured and easy to follow.\n\nCons:\nAlthough the task and method sound compelling, I have some major concerns about the data configuration and experimental comparison:\n- The quality of “high-frequency events” and the resulting performance degradation seems to be dependent on the proposed way of generating event frames. Section 3.2 mentions that the event is divided into several sub-intervals and aggregated within each interval to simulate high-frequency scenarios, while this setting may make the definition of high-frequency events problematic. For example, we can simply aggregate information for a longer period for each event aggregation, while still maintaining a low temporal gap between two sampling points. It will be helpful to provide the results on this setting with both high-frequency events and richer semantic information.\n- In the experimental section, this paper didn’t compare with models with solely RGB input. Therefore, it is hard to see the advantages of Event+RGB fusion compared to only using events or RGB frames. Also, it is not clear why only one fusion-based method is compared on the DSEC-Det dataset while more are compared on other datasets. \n- Although three datasets have been used for experiments, results on only 3 classes (Car, Pedestrian L-Veh.) are provided across all these datasets. This makes it hard to verify the methods’ generalization ability across more classes and scenarios, especially considering that pretrained weights are used during experiments.\n- It is not explained why using two significantly different backbones for events (RVT) and RGB frames (ResNet-50). Moreover, both pretrained weights are used for RVT and ResNet50 while it is not clear what are the pretrained datasets and how much they would contribute to the final performance.\n\nQUESTIONS:\nMy major concern is about the configuration of high-frequency events and the fairness of experimental comparison, I could reassess my score if these weaknesses are properly addressed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}