FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational Frequencies
Authors: Dongyue Lu
Lingdong Kong
Gim Hee Lee
Camille Simon Chane
Wei Tsang Ooi
Venue: NeurIPS 2025 poster ()
Keywords: Event cameras
Object detection
RGB-Event fusion
High-dynamic perception
Primary area: applications

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.

FlexEvent: Towards Flexible Event-Frame Object
Detection at Varying Operational Frequencies
Dongyue Lu 1.2,2 Lingdong Kong', Gim Hee Lee', Camille Simon Chane,, Wei Tsang Ooi
National University of Singapore PIPLL, CNRS IRL 2955, Singapore ETTI UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France
Project Page & Code: flexevent, github. .oo
Event:
Stream
Frequency
Middle Frequency
High Frequency
Event Imput
LOW Freq
RVT iT Mid Free
RVT High Freg
RVTT
Ground Truth
Durs Low Frep
Oura Mid
Cura High Freg
Durs Emety
00
Figure 1: Illustrative examples of event camera object detection at varying frequencies. The detection performance of the classic RVT detector [18] tends to drop significantly at higher event operational frequencies. Motivated by this observation, we propose FlexEvent, a robust and flexible event-frame detector that maintains high detection accuracy across a wide range of frequencies (low, middle, high), ensuring strong adaptability in real-world, dynamic sensing environments.
Abstract
Event cameras offer unparalleled advantages for real-time perception in dynamic en- vironments, thanks to the microsecond-level temporal resolution and asynchronous operation. Existing event detectors, however, are limited by fixed- rredffreuencyy paradigms and fail to fully exploit the high-temporal resolution and adaptabil- ity of event data. To address these limitations, propose FlexEvent, novel framework that enables detection at varying frequencies. Our approach consists of two key components: FlexFuss
adaptive event- feent-frame fusion module that integrates high- high-frequency event data with rich semantic information from RGB frames, and FlexTuuee
cooquencc-adaapiin fine-tuming mechanism that generates frequency-adjuster labels to enhance model generalization across varying oper- ational frequencies. This combination allows 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 demon- strate that our approach surpasses state-of-the-art methods, achieving significant improvements in both standard and high-frequency settings. Notably, our method
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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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.
Introduction
Event cameras have garnered significant attention for their ability to capture dynamic scenes with microsecond-level temporal resolution [12]. Unlike RGB cameras, which capture entire frames at fixed intervals, event cameras operate asynchronously, responding to changes in pixel intensity at each location [68]. This low-latency operation reduces motion blur and enables highly energy-efficieee sensing, making event cameras ideal for real-time applications [45, 30, 6].
Despite their potential, existing event detectors often fail to fully leverage the high-frequene temporal information [8, 15, 23]. Most approaches align event data with the lower frequency of frames by adopting fixed time intervals between event streams and frame-based annotations [40, 17]. While this simplifies data processing, inevitably overlooks the rich temporal details embedded in high- frequency event streams. Given that human annotations are often synchronized with slower frame rates, current detectors miss valuable information from higher frequencies, resulting in suboptimal performance when rapid object detection is required in dynamic environments [36, 43).
To address these limitations, we introduce FlexEvent, a novel framework designed to tackle the challenging problem of object detection varying operational frequencies. Our approach addresses the need for high-frequency detection in fast-changing environments, while adapting to different operational frequencies. We propose two key innovations: 1) an adaptive event-frame fusion module, and 2) a eiqquency-adaptive fine-tuning mechanism.
The first component, FlexFuse, addresses the limitations of event data, which often lacks semantic and texture-rich information, especially at higher frequencies [66], by integrating the rich spatial and semantic information from RGB frames with the high-temporal resolution of event streams. It enables high detection accuracy even in fast-moving environments. Furthermore, training on high-frequency event data is computationally expensive and impractical due to the significant human effort required to label such data. FlexFuse mitigates this by sampling event data at varying frequencies, aligning them with the normal frame rate during training, thus maintaining efficiency while preserving the high-frequency benefits at inference time.
The second component, FlexTune enhances the generalization capability of event camera detectors across varying operational frequencies, by generating frequency-adjusted labels for the unlabeled high-frequency data. These labels allow the model to learn from high-frequency event streams without manual annotations, and iterative refinement through self-training ensures that the model remains robust across different motion dynamics and frequency settings. Together, these two components allow for accurate real-time detection in rapid scene changes and adapt to a wide range of operational frequencies, by leveraging the temporal richness of event data and the semantic detail of RGB frames.
Our extensive experiments validate the effectiveness of FlexEvent on multiple large-scale event camera datasets. Our approach consistently outperforms recent detectors across both standard and fighffreueecyy settings. particular, we achieve mAP gains of 15.5% 9. 9.4%, and 10.33 10.%% over the previous best-performing detectors on DSEC-Dei [16], DSEC-Detection [50], and DSEC-MOD [66], respectively. Our model also maintains 96. 2% of its performance when the operational frequency shifts from 20 Hz to 90 Hz, and delivers accurate detection at frequencies as high as 180 Hz, proving its robustness under extreme conditions. summary, our contributions are listed as follows:
The proposed FlexEvent framework is aimed at tackling the challenging problem of event camera object detection at varying frequencies, being one of the first works to address this challenging problem explicitly in real-world conditions.
We propose FlexFuse, adaptive event-frame fusion module that leverages the strengths of both event and frame modalities, enabling more efficient and accurate weentbasedd object detection in dynamic environments.
We introduce Tune, frequency-adaptive fine-tuning mechanism that generates frequency- adjusted labels and improves generalization across various motion frequencies.

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We demonstrate that our approach achieves promising results across large-scale datasets, particularly in high--ighhee frequenyy scenarios, validating its effectiveness handle real-world. safety-critices problems.
2
Related Work
Event Camera Object Detection. Event-based detectors can be broadly split into two approaches: GNNs/SNNs and dense feed-forward models. GNNs build dynamic spatio-temporal graphs by sub-sampling events [15, 47, 36, 43], but they face challenges propagating information over large spatio-temporal regions, especially for slow-moving objects. SNNs offer efficient sparse information transmission but are often hindered by their non-differentiable nature, complicating optimization processes [9, 8, 59]. Dense, feed-forward models represent the second approach. Initial methods using fixed temporal windows [5, 22, 25, 19] struggle with slow-moving or stationary objects due to their limited capability to capture long-term temporal data. Subsequent work incorporates RNNs and transformers enhance temporal modeling [40, 69, 34, 18, 39], but these models still lack semantic richness and face difficulties in adapting to variable frequencies and highly dynamic scenarios.
Event-Frame Multimodal Learning. Combining events with frame-based data has proven effective in tasks such as deblurring [48, 61]. depth estimation [14, 51], and tracking [62, 13]. Early fusion methods perform post-processing combinations [33, 4], but lack feature-level interaction. More recent works propose deeper feature fusion techniques [50, 1, 2]. introducing pixel-level attention or temporal transformers for asynchronous processing [66, 32, 16, 3J. Some explore asynchronous multi-modal fusion for flexible inference at different frequencies [32, 14, 60), yet they do not explicitly address high-fie seggffreuency event data or fully exploit temporal richness. Additionally, balancing contributions from event and frame modalities remains challenging. Unlike these works, FlexEvent introduces a unified fusion framework that adaptively integrates high-frequene event data with semantic-rich frames, achieving robust detection across diverse frequencies while addressing feature imbalance.
Label-Efficient Learning from Event Data. Due limited annotated datasets, label-efficient learning has become an important area for event-based vision. Several studies attempt to reconstruct images from event data [41, 42, 46] or distill knowledge from pre-trained frame-based models [52, 49, 57, 29]. Other approaches use pre-trained models or self-supervised losses [28, 56, 67J. LEOD [55] pioneers object detection with limited labels but does not address high-frequency generalization. A recent state-space model [70] adapts to varying frequencies without retraining but struggles to detect static objects at high frequencies due its reliance solely on event data. In contrast, FlexEvent is specifically designed to adapt to varying event frequencies, ensuring consistent performance even in scenarios with limited labels, and effectively detecting both stationary and fast-moving objects.
3 FlexEvent: A Flexible Event-Frame Object Detector
In this section, we elaborate the technical details of our FlexEvent framework. We start with the foundational concepts of event data and their representation in Sec. 3.1. We then introduce the FlexFuse module in Sec. 3.2, which adaptively fuses event and frame data enhance detection across varying frequencies. Finally, we detail FlexTTune, our frequency-adaphe Sec. 3.3, which enables our model to generalize effectively across diverse temporal conditions via adaptive label generation. The overall framework is illustrated in Fig. 2.
fine-tuning mechanism, in
3.1 Background & Preliminaries
Event Processing. Event cameras are bio-inspired vision sensors that capture changes in log intensity per pixel asynchronously, rather than capturing entire frames at fixed intervals. Formally, let I [(x, t) denote the log intensity at pixel coordinates (a., y) and time t. An event e is generated at (x, y, t) whenever the change in log intensity AI exceeds a certain threshold C, which is modeled as:
AI v, t) = [x, v, (x, y. At) C.
Each event e is a tuple (x, y. t. p). where (I, y) are the pixel coordinates, is the timestamp, and p E 1- denotes the polarity of the event which indicates the direction of the intensity change.
To leverage event data with convolutions, we pre-proe prepprocess events into a 4D tensor with dimensions [18] representing the polarity, temporal discretization T,, and spatial dimensions (H, W), respectively.
3

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Frame Branch
Eq 171
Frame
e h
h97
Pred
GT
Jf
Shared
Fusso
Fuse
Fuse
Fuse
FlexT TanTune
(P
E"/E"
Event
Event Branch
Pred
Eq.
[9]
RES
The ResNet Blockss
RVT
The RVT Blocis
Fuse
Event: Frame Fusion (Soc.. 3.2)
Tune Froq Adoptive Fine turing (Ser. 3.3)
Figure 2: Framework Overview.
The proposed FlexEvent consists of two branches: Event and
Frame. The event branch eaptures high-temporal resolution data, while the frame branch leverages the rich semantic information from frames (ef. Sec. 3.1). These branches are fused dynamically through FlexFuse, allowing adaptive integration event and frame data (cf. Sec. 3.2). Additionally, the FlexTune learning mechanism ensures robust detection performance across varying operational
frequencies (cf. See. 3.3). Together, these components enable the model to handle diverse motion dynamics and maintain high detection accuracy in varying frequency scenarios.
This representation involves mapping set of events & within time interval [t1, 12) into the following:
Ep, T,
Σ ENEE ö(p Pk (6(z= In. yx)) Tk),
(2)
where 7h
باي 12-1. T Such a 4D tensor captures event activity discrete time slices, yielding a compact representation suitable for 2D operators by flattening the polarity and temporal dimensions.
Problem Formulation. Given two consecutive frames F1 and F2 captured at timestamps f and f2, our objective is to leverage the event stream over the interval [t1. ty] to detect objects at the end timestamp f2. Existing event detectors use fixed time intervals At, limiting adaptability to dynamic environments [40]. Additionally, integrating semantic information from RGB frames remains challenging. affecting performance in complex scenarios [10]. To address this, we propose to synchronize the event data with frames and explore varying training frequencies, leveraging the
temporal richness event cameras to improve detection accuracy.
3.2 FlexFuse: Adaptive Event-Frame Fusion
In dynamic environments, object detection systems must adapt to varying motion frequencies [48]. While event cameras excel at capturing rapid changes in pixel intensity, they often lack rich spatial and semantic information provided by frames. To address this limitation and fully leverage the complementary strengths of both modalities, we introduce FlexFuse, an adaptive fusion module designed to dynamically combine event data different frequencies with frames.
Dynamic Event Aggregation. Given a dataset D, consisting of sequences of calibrated event data and frame data with resolution of H W, along with corresponding bounding box annotations y collected at frequency a, we begin by selecting a batch frame data F paired with event data Ea, both captured at frequency To aggregate event data from higher frequency b (where b >> a), we divide the time interval Â! corresponding to E into b/a smaller sub-intervals. From each b/a sub-interval, we obtain a high-frequency event set (EE) Ji=0 as defined in Eq. 2. From this set, randomly sample one data point' E.. As detections are predicted for the end of Ara this strategy pairs each frame with the preceding high-frequency events while introducing only millisecond-scale jitter that acts implicit temporal augmentation, strengthening robustness real-world synchronization
For simplicity, we use E to represent a sample from the set of high- freeffryyynncy event data (E.) b/a Tizo rather than explicitly referencing each individual sample from the event The same applies other frequencies.

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noise. Consequently, (F, E", E") are effectively paired across modalities and frequencies, and this synchronization of image streams with event streams different frequencies ensures consistent and reliable processing for all subsequent stages.
Feature Extraction. Let ( and or (-) represent the event- and frame-hased networks, respectively, where the former employs the RVT [18] for extracting features from event data, and the latter uses ResNet-50 [20] for feature extraction from frames. Both networks are structured into four stages, as shown in Fig. 2. At each scale i, extract features (+) h h from the event data and hh from the frame data. This process is formulated as:
h =(1) ΦE (E())) h OE (E"), he =(1) Oe (F),
(3)
where hg and n² BR (9 R Here, denotes the scale, B is the batch size, and Cg and CF the dimensions of the event and frame feature maps, respectively.
Event-Frame Adaptive Fusion. To effectively fuse the event and frame data, we employ an adaptive fusion that is consistent across different event data frequencies. frequency event features (4) hy, as example, we concatenate the feature maps from both the event and frame branches as follows:
each scale i, taking the low-
Prev
Next
(
huse
juse
shared
hg. (0) [hr] E E (BBx(C++C))xXx...
(4)
Wh
Inspired by previous work [66, 64], our goal is to dynamically fuse these two modalities in a flexible manner. The proposed FlexFuse module computes adaptive soft weights that regulate the contribution of each branch (event and frame) based on the current input conditions. As shown in Fig. 3, these adaptive soft weights are computed using gating function, which incorporates learned noise to introduce perturbations for improved adaptability:
(
(0
Softmax
C 6
a a, ( 3 Softmaxx( h Shared
W)
a c),
(5)
Gate
Noise
(h
where ( W E Ig(C=+CF)x2 is trainable weight matrix, Wa and B are the adaptive soft weights for the event and frame branches, respectively. Here, (6) is a learned standard deviation that controls the magnitude of the noise perturbation, and ((0,, 1) represents a Gaussian noise term. The fused feature map each scale is then obtained by applying adaptive soft weights:
(hh
b
Frame Feature
Event Feature
ConCat
Add
Multiply
( a
(8)
h (() B
(
he
(6)
Figure 3: Illustration of the Flex- Fuse module. We show general example of event and frame un- der frequency a at the i-stage.
where @ denotes element-wise multiplication. This fusion pro- cess dynamically balances the contribution of each modality
based on the input data, allowing for more adaptive feature rep-
resentation across varying conditions.
Then, at each scale i, the final feature map combining event data at different frequencies and the frame data is obtained by adding the fused features from different frequencies. Specifically, we combine the fused feature maps hhuse =!1) hi (() b After obtaining the fused feature maps across all scales, multi-scale features are concatenated and fed into the detection head to produce the predicted bounding box
addition to the standard detection loss (y, $), we introduce regularization term to ensure balanced utilization of both the event and frame branches. This term penalizes large variations in the soft weights, encouraging a more uniform contribution from both modalities and preventing overfitting to single branch:
Optimization Regularization.
Var (a)
Var (g)
Liuse Laot ())
(E (al)
(E [3])
where À is weighting factor.
5

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3.3 FlexTune: Frequency-Adaptive Fine- Line-Tuning
FlexFuse aggregates information from different frequencies using labeled low cowfrequency data. To tune the model adaptively to handle diverse frequencies by leveraging both labeled low-frequency data and unlabeled high-frequency data, we design a FlexTune learning mechanism, incorporating multi-frest fueuui-y information into the training process through iterative self-training. This enhances the ability to generalize across varying frequencies, making it more robust in different scenarios.
As shown in Fig. 4, FlexTune consists of two main stages: Low-Frequency Sparse Training to learn foundational knowledge from labeled data, and Fross-Firquency Propagation to transfer and refine the learned knowledge with high- regh-freueeccy unlabeled data. This iterative mechanism bridges sparse supervision with dense temporal patterns, enabling robust detection under varying frequencies.
Low- Now-Fregunncy Sparse Training. Rather than training solely at frequency a, we en- hance the model's Iss capability by training it at a higher frequency b. To efficiently leverage the available sparse labels, we select only the last event from the high-frequency event set [ which corresponds to the labeled timestamp. This allows the model to capture valuable high-frequency temporal information while still utilizing fow-frequency labels, im- proving its temporal understanding and robust- ness. The training objective is to minimize the detection loss over the sparse labeled data as:
Low-Frequency Sparse Training
Cross-Frequency Propagation
Fvant intarval
Event Interval
Bajs
TCC
Yaja
Figure 4: Illustration of the FlexTune learning mechanism. We first train on high-frequency events with sparse freffeeuency labels, then we gener- ate and refine high-frequency labels for eyclic self- training across frequencies.
LGT Latt (y,
(8)
where the summation is taken over samples (F, E. b/a E D.
Cross- Cross-Frequency Propagation. Building on
the low-frequency training. we transfer and refine model knowledge to unlabeled high- frequerrquncc data through three sequential steps: High-Frequency Bootstrapping, Temporal Consistency Calibration, and Cyclic Self-Training. Together, these steps generate accurate high-frequency pseudo-labels, enabling robust detection in diverse temporal settings.
High-Frreueency Bootstrapping. For the unlabeled data D captured at frequency b, the pre-trained model generates high-frequency labels ý by performing inference the entire high-frequency event set [ These generated labels ý serve as pseudo-labels for bootstrapping further training at higher frequencies, improving the model's 's ability to generalize across different temporal conditions.
Temporal Consistency Calibration. To refine high-frequency labels, we enforce temporal coherence through three steps. (1) Bidirectional Event Augmentation. Process event streams forward and backward capture diverse object motions, enhancing recall. (2) Confidence-Awann Filtering. Apply Non-Maximum Suppression (NMS) and a low confidence threshold 7 to eliminate duplicates and retain high-potentia detections. (3) Tracklet Pruning, Link detections across frames using IoU- based tracking (rl) ) and prune short tracks (< L track) to suppress transient noise. This approach ensures that the refined high-frequenee labels ý are accurate, temporally consistent, and reliable, improving detection quality in gigh-frequency data even in the absence of ground truth labels.
Cyclic Self- Frainnrain The model is iteratively trained using the refined tigh-frequency labels ý and the ground truth low-frequency label. The total loss function combines the base training loss and the pseudo-label loss as:
Lrune LGT + B Σ Ledet (),
$1=00 . .9) E D, and the coefficient 33 balances the contribution of the high- frequeeyyy label loss. The complete FlexEvent framework combines Flex and Flex Tune, allowing the model to dynamically fuse event and frame data while adapting to varying frequencies.
where the summation here is taken over high-frequency samples (F, //b-b/a-1

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Table Comparative study of state-of-the-art event camera detectors on the validation of the DSEC-De Det [16] dataset. Both event-only and event-frame fusion methods are compared. The best and 2nd best scores from each metric are highlighted bold and underlined, respectively.
Modulity
Method
Ref
Venue
AP 50
AP
APs
APı
RVT
[18]
CVPR'22 23
38.4
58.7
41.3
29.5
50.3
81.7
SAST
[39
CVPR 24
38.1
60.1
40.0
29.8
18.9
79.7
E
SSM
[70]
CVPR'24
38.0
55.2
62.2
77.8
LEOD
[55]
CVPR'22 24
41.1
65.2
43.6
35.1
47.3
73.3
HDI-Former
[31]
arXiv'24 24
46.7
69.1
DAGr- 18
[16]
Nature 24
37.6
E+F
DAGr-34
[16]
Nature'
39.0
DAGr-50
Nature'24 24
66.0
44.3
77.8
FlexEvent
Ours
57.4
78.2
66.6
51.7
641.9
83.7
Table 2: Comparative study of state-of-the-art event camera detectors the test set of DSEC- Detection [50]. Both event-only and event-frame fusion methods are compared. The reported results the mAP scores of the Car. Pedesstrin, and Large- "Large-Vehicle Vehicle (L-Veh.) classes. The best and 2nd best scores from each metric are highlighted in bold and underlined, respectively.
Modality
Method
Venue
Ref
Type
Car
Pedestrian
L Veh.
Average
E
CAFR
ECCV'24
Event
12.0
SENet
CVPR'18
[21] [54]
38.4
14.9
26.0
26.2
CBAM
ECCV'18
Attention
37.7
13.5
27.0
26.1
CVPR'20
[53]
27.5
SAGate
ECCV-20
16.0
DCF
CVPR'21
[24]
RGB Depth
36.3
12.7
28.0
25.7
SPNet
ICCV'21
[65]
39.2
17.8
26.2
27.7
RAMNet
RA-L'21
[14]
24.4
10.8
17.6
17.6
FAGC
Sensors'21
39.8
1414
33.6
29.3
FPN-Fusion
ICRA'22
[50]
37.5
10.9
24.9
24.4
EFNet
ECCV'22
[48]
41.1
15.8
32.6
30.0
E+F
DRFuser
EAAI'23
[37]
RGB Event
38.6
15.1
30.6
28.1
CMX
TITS'23
[58]
41.6
16.4
29.4
29.1
RENet
ICRA'23
[66]
40.5
17.2
30.6
29.4
CAFR
ECCV"24
49.9
25.8
38.2
38.0
FlexEvent
Ours
59.3
37.4
45.5
47.4
4 Experiments
4.1 Experimental Settings
Datasets. We conduct experiments based three large-scale datasets: DSEC-Det [16]. DDEEC- Detection [50], and 3 DDEC-MOD [66]. These datasets comprise 78,344 frames across 60 sequences, 52,727 frames over 41 sequences, and 13,314 frames within 16 sequences, respectively. making them 1
suitable for evaluating event-based object detection methods. We prioritize DSEC-Det [16] as the primary benchmark for comparisons, it is the largest, most recent, and most comprehensive event- frame perception dataset. We report results on Car and Pedestrian classes to ensure fair comparison with the state-of-the-art method DAGr [16]. For more details, please refer to Appendix Sec. A.
Baselines. evaluate our method, we compare it against both state-of -hre--r-tth--tttt event-only and event- frame fusion methods. For event-only detectors, we compare RVT [18], SAST [39], LFOD [55], and SSM [70], retraining them on DSEC-Det [16] following their respective protocols For event-frame fusion methods, compare DAGr [16] and HDI-Former [31] on DSEC-Det [ [16] using results from the original paper and retrain our method on DSEC-Detection [50] and DSEC-MOD [66] using standard settings to compare with CAFR [3] and RENet [66]. For other methods DSEC-Detection and DSEC-MOD, we reference results reported the CAFR and RENet papers. For more details, please refer to Appendix Sec. A.2.
Evaluation Metrics. We evaluate object detectors using the mean Average Precision (mAP) as the primary metric, along with APso. AP75, APs, APM. and APr, from the COCO evaluation protocol [35]. These metrics provide comprehensive assessment of detection performance across different IoU thresholds and object sizes. Please refer to the Appendix Sec. A.3 for more details.

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Ground Truth
RVT (CVPR'23)
SAST [QVPR124]
DWGr (Nature 24)
FlexEvent [Durs]
Scene #111
Seeene #2
Sizenne #:3
Sieenne ne in-4
Figure 5: Qualitative comparisons of state-of satte-of-thr off-the-art event camera detectors. We compare FlexEvent with RVT [18], SAST [39], and DAGr [16] on the test set of DSEC-Det. Best viewed in colors.
MAP
RVT
SAST
SSM
DAGr
FlexEvent
Table 3: Comparisons of state-of-the-arr event-frame fusion
(9)
587
501
detectors on the test set of the DSEC-MOD [66] dataset.
501
40.5
Modal
Method
Venue
Type
EmAP
VmAP
Avg
40
SENet [2]]
CVPR118
29.3
17.5
23.4
30
CBAM
ECCV'18
Attention
36.2
16.1
26.3
000
ECA-Net [53]
CVPR'20
34.5
18.8
26.7
SAGate [7]
ECCV
33.6
17.8
25.7
E+F
RGB Depth
SPNet [65]
ICCV'21
32.7
14.7
23.7
H0
Hz
S0Hz
180 Hz
PPN Fusion [50]
ICRA'22
32.3
15.0
21.7
Figure 6: Results of detectors under varied frequencies on DSEC-Det.
RENet [66]
BFNet [48]
BCCV
ICRA'23
RGB Event
38.1
35.3
18.1
19.6
29.0
26.7
ElexEvent
Ours
48.6
25.2
36.9
Implementation Details. Our training follows YOLO-X [63], using the standard detection loss consisting of IoU loss, classification loss, and regression loss, plus a lightweight regularizer that balances the contributions of each modality. The model is trained for 100,000 iterations with batch size of 8 and a sequence length of 11, using a learning rate of 1e-4. Experiments are conducted on two NVIDIA RTX A5000 GPUs with 24GB memory, with the entire training process completed in approximately one day. Due to space limits, more details are Appendix Sec. A.4.
4.2 Comparisons to State of the Arts
Comparisons with Event-Only Detectors. We compare FlexEvent with state-of-the-art event-only detectors, including RVT [18], SSM [70], SAST [39], and LEOD [55], as shown in Tab. 1. Our model substantially outperforms all these methods, with the gap widening at higher event frequencies. Event-only methods struggle to maintain detection accuracy higher frequencies because they lack rich semantic cues. In contrast, we overcome these limitations through the FlexFuse module, which integrates RGB data to compensate for the lack of semantic richness in the event stream. By fusing both event and frame data, we excel complex and dynamic environments, achieving superior detection accuracy where event-only methods fall short.
Comparisons with Multi-Modal Detectors. We compare FlexEvent with multimodal event-camera object detection methods such as DAGr [16], HDI-Former [31], CAFR [3], and RENet [66], which fuse event data with other sensor inputs to improve detection accuracy. The results are shown in Tab. 1, Tab. 2 and Tab. 3. While these methods enhance performance over event-only approaches,

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Table 4: Comparative efficiency analysis of event detectors on DSEC-Der [16]. We report the inference times of event detectors at various frequencies, measured in milliseconds (ms).
Table Ablation on FlexFuse and FlexTune learning mechanism on DSEC-Det [16]. Symbol denotes the use of interpolated ground truth labels at high frequencies in PlexTune.
Method
Size
(M)
20.0 Hz
Operational Frequency
36.0 Hz
90.0 Hz
180 Hlz
Tune
Fuse
20.0
36.0
Frequency (Hz)
45.0
60.0
90.0
180
Avg.
[VT[18]
18.5
9.20 ms
7.95 ms
7:10 ms
6.77 ms
X
X
53:2
52.0
49.4
45.9
38.8
22.8
43.7
SAST [39]
18.9
14.06
12.37
11.52
11.10
V
51.6
51.3
53.3
50.7
46.6
30.4
48.0
SSM [70]
ms
6.90
6.54
64.9
57.8
57.2
53.7
48.3
DAG=-50 [16]
34.6
73:35 mss
55.11 ms
45.20 ms
43.80 ms
x
58.0
39.6
50.0
57.6
54.8
49.2
56.4
FlexEvent
45.4
14.27 ms
13.00 ms
12:47 ms
12:37 ms
5
07.4
60.1
59.5
58.8
66.5
50.9
57.2
they struggle to adapt to varying operational frequencies and often exhibit inadequate feature fusion in dynamic environments. Our approach addresses these limitations by dynamically balancing the contributions of event and frame data. This flexible combination, along with the ability to generalize across different temporal resolutions, enables our method excel in high-frequency event-based detection scenarios, surpassing state-of-ther the-thh-rtttt art approaches.
Generalization on High-Frequency Data. A key contribution of FlexEvent is its generalization capability across various operational frequencies, particularly in evaluate this by testing detection performance at different temporal offsets, : A7, where 10, i= O.... 10, and AT = 50ms. Ground truth labels are generated by linearly interpolating object positions between frames following DAGr [16]. In this setting, event-based methods tested across multiple time durations, while event-frame fusion methods process one RGB frame followed by event data of varying time durations. As shown in Fig. 6, existing methods degrade significantly at higher frequencies due to fixed temporal intervals and limited ability to capture fast scene changes, while our method achieves consistent, strong performance.
scenarios. We
Comparisons on Efficiency. In Tab. 4, we compare inference times and parameter counts using an NVIDIA A5000 24GB GPU and AMD EPYC 32-Core Processor. Although FlexEvent comprises slightly more parameters overall, its speed matches SAST [39] and clearly surpasses DAGr [16] at all tested frequencies. As FlexTune is performed off-line, the figh-frequency generalizability is already propagated in the model and introduces no runtime overhead. Our lightweight fusion head contributes only 1.5M parameters, while the event-frame backbone adds further 441 that already highly optimized and incurs virtually no extra latency. This confirms our efficiency and real-time suitability.
Qualitative Assessments. We provide visual comparisons of FlexEvent and other state-of-the-arr methods under different event operation frequencies, as shown in Fig. 5. Unlike RVT [18] and DAGr [16], which often miss misinterpret critical objects, our model consistently detects objects with high accuracy, even in challenging cases involving fast-moving vehicles and occluded pedestrians. This robustness is also evident in examples of Fig. 7, where our method detects a pedestrian missed by RVT [18] due to sparse event data at high frequencies. For more detailed analyses, visualizations, and failure cases, please refer to Appendix Sec. C.
4.3 Ablation Study
Analysis of FlexFuse. We assess the effect of integrating frame data via FlexFuse. As shown in Tab. 5, adding frame information boosts average mAP from 43.7% to 56.4%, with greater gains at higher frequencies where event data alone becomes sparse. Our adaptive gating fuses event and frame features more effectively than simple Add, Concat, or vanilla Attention (Fig, 8), achieving the best accuracy across all frequencies and providing robust representations under diverse conditions.
Analysis of FlexTune. In parallel, we assess Flex Tune, focusing scenarios where event data are sparse and fast-evolving. Without it. performance at high frequencies collapses, as seen in the first row of Tab. 5 where mAP at 180 Hz is only 22.9%. However, activating FlexTunee raises this score to 30.4%, demonstrating the value of iterative self-training and refinement for frequency adaptation. We also test training with interpolated high-frequency labels, but this approach struggles with objects that appear or disappear rapidly, reducing recall of the label. By contrast, Flex updates the model with refined, temporally consistent labels, improving detection quality.

---

Ground Truth
Freq 20 Hz
Freq 60 Hz
Freq 90 Hz
Free 180 Hz
HVT
RVV
FlexE Eerris
Figure 7: Qualitative assessment of FlexEvent and RVT [18] under different operation frequencies.
mAP (1)
Nona add Concat Attannin FixFusse
Table 6: Ablation study on hyperparameters. T", rP denotes the confidence threshold for Car and Pedestrian, respectively. iou denotes the loU threshold when filter by tracking, L mock denotes the minimum track length. The reported results the mAP scores on the test set of DSEC-Det [16].
85
67.8
58
50
40.1
20.2
Frequency (Hz)
TP
T Packk
Avg
20
80
90 Hz
180
0.3
10
56.5
57.2
57.1
56.7
51.5
49.2
55.2
0.6
0.3
10
0.6
56.7
57.9
57.7
57.0
54.3
47.0
55.1
Figure 8: Ablation on event-frame fusion strategies under different fre- quencies on DSEC-Del [16].
0.3
8
56.3
59.1
58.8
38.1
56.2
51.2
56.7
0.6
0.3
6
0.6
57.3
59.9
59.3
58.5
55.7
48.8
56.5
0.6
6
0.6
57.
60.
59.5
58.8
56.5
50.9
37.2
0.8
6
56.6
58.9
58.4
57.4
55.6
50.2
56.2
Hyperparameter Tuning, We investigate key hyperparameters in the FlexTune mechanism, specifi- cally, the confidence thresholds for cars 7" and pedestrians 7P, the IoU threshold for bounding -bo association pinn and the minimum track length for temporal refinement L track As shown in Tab. 6, lowering confidence thresholds can improve recall by admitting more detections, but risks increas- ing false positives and thus reducing overall precision. Conversely, setting overly strict thresholds, such as a higher loU requirement or confidence level, might filter out potential detections, lowering recall. An intermediate setting of 0.6 for both classes, with track length of and loU threshold of 0.6, emerges as the best trade-off, balancing recall and precision across both low- and high-free feeeefryy conditions. These moderate, carefully tuned parameters ensure that FlexEvent remains robust and accurate in a range of real-world scenarios.
5
Conclusion
This paper introduces FlexEvent, event-camera detection framework designed to operate across varying frequencies. By combining FlexFuse for adaptive event-frame fusion and FlexTune for learning, we leverage the rich temporal information of event data and the se- mantic detail of RGB frames to overcome the limitations of existing methods, providing a flexible solution for dynamic environments. Extensive experiments on large-scale datasets show that our approach significantly outperforms state-of-the-ar methods, partieularly in high-frequency scenarios, demonstrating its robustness and adaptability for real-world applications.
10

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Acknowledgments
The authors would like to sincerely thank the Program Chairs, Area Chairs, and Reviewers for the time and effort devoted during the review process.
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Appendix
Additional Implementation Details
22
A.1 Datasets
22
A.2 Baselines
23
A.3 Evaluation Metrics
A.4 Training & Inference Details
24
Additional Quantitative Results
25
B.1 Complete Results of Efficiency Analysis
B.2 Complete Results of Ablation Study
25
B.3 Complete Results of Hyperparameter Search
26
Additional Qualitative Results
26
C.I
Qualitative Comparisons of Event Detectors
26
C.2 Comparisons under Different Frequencies
28
Potential Societal Impact & Limitations
28
D.1 Societal Impact
28
D.2 Broader Impact
28
D.3 Known Limitations
29
E Public Resources Used
30
E.1 Public Datasets Used
30
E,2 Public Implementations Used
30
A
Additional Implementation Details
In this section, to facilitate future reproductions, elaborate the necessary details in terms of the datasets, evaluation metrics, and implementation details adopted in our experiments.
A.1 Datasets
In this work, we develop and validate our proposed approach on the large-scale DSEC dataset [17]. DSEC serves as a high-resolution, large-scale multimodal dataset designed to capture real-world driving scenarios under various conditions. It combines data from stereo Prophesce Gen3 event cameras with resolution of 640 x 480 pixels and FLIR Blackfly S RGB cameras operating at 20 FPS, enabling high-fidelity capture of dynamic scenes. To align the RGB frames with the event camera data, an infinite-depti alignment process employed, which involves undistorting, rotating, and re-distorting the RGB images. This alignment ensures that the event data and RGB frames temporally and spatially synchronized.
In our experiments, we utilize three comprehensive versions of DSEC tailored for object detection: DSEC-Det [16], DSEC-Detection [50], and DSEC-MOD [66]. summary of the key statistics of these datasets is listed in Tab. 7.
DSEC-Det [16]: This version is developed by the original DSEC team and includes annota- tions generated using the QDTrack multi-object tracker (1]. The annotation process combines automated multi-object tracking with manual refinement to ensure high-qualip
22

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Table 7: Summary of key statistics from the three event datasets [16, 50, 66] used in this work.
Dataset
Classes
Frames
Sequences
Class Names
DSEC-MOD [66]
1
13, 314
16
Car
DSEC-Detectin [50]
3
52, 727
41
Pedestrian
Large- Vehicle
Car Pedestrian
Bicycle
DSEC-Det [16]
8
78, 344
60
Truck
Motorcycle
Rider
Train
and accurate detection labels. Compared to the original DSEC dataset, DSEC-Det [16] introduces additional sequences specifically designed to capture complex and dynamic urban environments, featuring crowded pedestrian areas, moving vehicles, and diverse lighting conditions [29]. These challenging scenarios provide rich testing ground for evaluating object detection algorithms in real-world driving settings. DSEC-Det [16] comprises 60 sequences and 78,344 frames, making it the most extensive dataset used in this study. It captures diverse urban scenes with dynamic elements, such as crowded pedestrian areas and moving vehicles. Covering eight object categories relevant autonomous driving, Car, Pedestrian, Bus, Bicycle, Truck, Motorcycle, Rider, and Train, the dataset provides a robust foundation for training and evaluating object detection models across various driving scenar- ios. In our experiments on DSEC-Det [16], to maintain consistency with the experimental setup of previous work DAGr [16]. we report results on two categories: Car and Pedestrian.
DSEC-Detection [50]: This dataset comprises 41 sequences with total of 52, 727 frames. Focusing on three fundamental object categories Car, Pedestrian, and Large Vehicle this version emphasizes high-precision annotations for these critical classes in autonomous driving. The initial annotations are generated using the YOLOv5 model [26] on RGB frames, leveraging its robust performance in real-time object detection. These annotations are then transferred to the corresponding event frames through homographic transformation, ensuring spatial alignment between the modalities. subsequent manual refinement process corrects any discrepancies and enhances annotation quality, resulting in a dataset that provides accurate and reliable labels for event-based object detection.
DSEC-MOD [66]: This dataset extends the object detection capabilities moving-object detection across diverse urban environments. It includes sequences containing 13,314 frames and is specifically focused on the Car category, making it highly suitable for complex detection tasks in varied urban settings, such as intersections, highways, and residential areas. Featuring high-frequency and dense annotations, the dataset provides valuable resource for evaluating performance of event-based object detectors under challenging real-world conditions.
These three versions the DSEC dataset together provide a comprehensive platform for benchmark- ing and evaluating event-basee object detection methods, capturing wide spectrum of scenarios, object categories, and environmental conditions. Among them, DSEC-Det [16] is the largest, most recent, and we prioritize it as the primary benchmark for reporting results, ensuring relevance and reliability. DSEC-Detection [50] and DSEC-MOD [66] datasets used by two recent event-frame fusion methods, CAFR [3] and RENet [66]. To validate the effectiveness of our method, we also report results on these datasets.
comprehensive, annotated, and released by the original DSEC authors. Thus,
A.2 Baselines
To evaluate the effectiveness of our method, we compare it against both event-only and event-frame fusion state-of-the- art methods.
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Event-Only Methods. We include state-of of--o-ffthhtaae event-only object detectors, namely RVT [18]. SAST [39], LEOD [55]. and SSM [70], which are originally trained on event-only datasets like Gen1 [10] and 1Mpx [40]. To ensure a fair comparison, we retrain these methods on the DSFC-Det [1 [16] dataset following their respective training protocols.
Erent-Frame Fusion Methods. For event-frame fusion methods on DSEC-Det [16), we include DAGr [16] and HDI-Former [31], as they have been evaluated on this dataset. We report the scores of DAGr and HDI-Former from the original paper to ensure consistency and fairness. For the DSEC-Detection [50] and DSEC-MOD [66] datasets, we train our model following the standard training and evaluation settings, and compare against state- of-the-art methods CAFR [3] and RENet [66], as reported in their respective papers. For other methods evaluated on Deteetttii [50] and DSEC-MOD [66]. we reference the results reported in the CAFR and RENet papers, respectively.
These comparisons among state-of-the works ensure fair and comprehensive evaluation while adhering to resource and code availability constraints.
A.3 Evaluation Metrics
In this work, adopt the mean Average Precision (mAP) as the primary metric evaluate the performance of our object detection models, consistent with standard practices in the field. The mAP metric provides a comprehensive measure of detection accuracy across multiple categories and intersection-over-umion (IoU) thresholds.
Mathematically, the Average Precision (AP) for single class is calculated as:
AP [." p(r) dr.
(10)
where p(r) represents the precision at a given recall level r. The mean Average Precision (mAP) is then computed as the mean of the AP values across all object categories and range of IoU thresholds (lypically from 0.5 to 0.95 with a step size of 0.05). This provides an overall measure of model performance across different levels of localization precision.
In addition to mAP, we also report following metries from the COCO evaluation protocol [35]:
Ao
The average precision when evaluated fixed IoU threshold of 0.50, indicating how well the model performs with relatively lenient localization criteria.
AP75: The average precision at fixed IoU threshold of 0.75, representing performance under stricter localization requirements.
APs, AP M. and APL: These metrics represent the average precision for small (S), medium (M), and large (T.) objects, respectively. Object sizes are defined based on their pixel area, with APs typically representing objects with areas less than 32 32 pixels, APM representing areas between 32 x 96 x 96 pixels.
32 and 96 x 96 pixels, and AP, for objects larger than
By reporting these metrics, we obtain more nuanced understanding of the model's detection capabilities across varying object sizes and localization precision levels, ensuring a comprehensive evaluation of detection performance.
A.4 Training & Inference Details
We train our models using mixed precision to optimize both memory efficiency and training speed. The training process spans 100,000 iterations, utilizing the Adam optimizer [27] with OneCycle learning rate schedule [44], which gradually decays from peak learning rate to enhance convergence.
Consistent with RVT [18], we employ mixed batching strategy to balance computational efficiency and memory usage. Specifically:
Standard Backpropagation Through Time (BPTT): Applied to half of the training samples, allowing for full sequence training.
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Table 8: The complete results of the efficiency analysis of ofate-o-thhe-eee art event detectors the test set of DSEC-Der [16], comparing both event-only and event-frame fusion methods. This table reports inference times at various frequencies, measured in milliseconds (ms).
Operational Frequency
Modal
Method
(M)
20.0 Hz
36 Hz
45 Hz
60 Hz
90 Hz
180 Hz
200 Hz
RVT 18.5
9.20
7.93
7.61
7.51
7.19 ms
6.77
6.34
E
SAST [39]
18.9
14.06 ms
12.37 ms
11.95 ms
11.63 ms
11.52 ms
11.10 ms
10.36 ms
[70]
18.2
8.79 ms
7.55 ms
6.90 ms
6.12 ms
DAGr-50 [16]
34.6
73.35
55.11
51.00
48.00
45.29
43.89
37.58
E+F
FlexEvent
15.1
14:27 ms
13.00 ms
12.79 ms
12.38 ms
12.47 ms
12.37 ms
12.12 ms
Truncated BPTT (TBPTT): Used for the other half, reducing memory usage by splitting sequences into smaller segments.
For data augmentation, we apply random horizontal flipping and zoom transformations (both zoom-in and zoom-out) to enhance the diversity of training samples.
Our training process utilizes the YOLOX framework [63], a versatile object detection framework known for its efficient and high-performing architecture. We employ multi-component loss function to optimize our model effectively:
Intersection over Union (loU) Loss: This loss component measures the overlap between the predicted bounding boxes and the ground-truth truth boxes, ensuring that the predicted regions closely match the actual object locations.
Classification Loss: This component evaluates the accuracy of class predictions for each detected object, ensuring that the model correctly identifies the category of each detected instance.
Regression Loss:
This loss assesses the precision of the predicted bounding box coordinates, helping the model refine the location and size of bounding boxes to align closely with the ground-truth annotations.
To ensure stable training, these loss components averaged across both the batch and sequence length at each optimization step. This averaging process helps to reduce variance during training and facilitates smoother convergence of the model parameters. We also include a regularization term to balance the contribution of both modalities.
Training Configuration. The training is conducted with batch size of 8, which provides optimal balance between efficient GPU utilization and memory requirements. Each training sample contains sequence length of 11 frames, allowing the model to learn temporal dependencies effectively. The frame backbone's 's weights are initialized using pre-trained ResNet, and the event backbone's 's weights initialized using pre-trained RVT. The learning rate is set to 1 10-1, following OneCycle learning rate schedule that allows efficient exploration of the learning space and helps in achieving faster convergence.
Hardware & Training Time. All training experiments are carried out on two NVIDIA RTX A5000 GPUs, each with 24GB of memory, providing the computational resources necessary for handling the high-resolution event data and RGB frames. The complete training process, including all iterations and model optimization, takes approximately one day, demonstrating the efficiency of our implementation in terms of both training speed and resource utilization.
B Additional Quantitative Results
B.1 Complete Results of Efficiency Analysis
We include the complete results the efficiency analysis in Tab. 8.
B.2
Complete Results of Ablation Study
We include the complete results of the ablation study in Tab. 9.
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The complete ablation results of different components in the FlexEvent framework. FlexFuse denotes the adaptive event-frame fusion module. FlexTune denotes the FlexTune learning mechanism. The reported results the mAP, AP so. AP APs. s, APM- and APL scores the test set of DSEC- Der [16]. The symbol + denotes the use of interpolated ground truth labels at high frequencies in FlexTune. The best and 2nd best scores of each metric are highlighted in bold and underline.
Table 9:
Modality
FlexTune
FlexFuse
Frequency (Hz)
mAP
AP
AP
APs
AP
APP
77.2%
58.1%
46.4%
64.4%%
27.5
54.0%
76.8%
59.3%
46.4%
66.6%
85.2
X
X
30.0
53.5%
75. 5%
59.3%
45.6%
66.3 8%
85.0%
36.0
52.0%
73.3%
58.1%
11.0%
65.5%
81.9%
Event
K
X
45.0
49.4%
69.5%
55.4%
40.7%
64.1%
84.3%
60.0
15.9%
64.2%
51.8%
36.5%
62.3%
90.0
38.8%
55.4%
43.9%
28.5%
55.89
79.9%
X
180.0
22.9%
36.19% 196
23.9%
14.1%
34.5%
1%6
X 20.0
54.6%
79.1%
61.
47.4%
6414%%
81.4%
27.5
54.9%
78.
61.1%
6%
66.1%
83,
X
30.0
54.9%
5%%
61.3%
47.4%
66.9%
83.3%
54.3%
77.1%
60.3%
75%
Event
X
45.0
53.3%
75.39
39.8%
45.6%
65. 13%
83.8%
60.0
50.7%
72. 45%
57.3%
42.3%
63.55
83.5%
90.0
44.6%
65. 1%
49.9%
35.3%
58.9%
81.9%
X
180.0
30.4%
48.1%
32.2%
20.7%
44.0%
72.9%
20.0
54.9%
74.0%
63.2%
50.7%
61.35%
85.5%
27.5
57.3%
66.3%
52.8%
65.8%%
86,
30.0
57.7%
75.99
86%
52.7%
67.256
87.5%
0
87
66.5%
52.5%
87.25
Event + Frame
45.0
57.2%
75.5%
65.4%
51.6%
68.2%%
87.5%
60.0
96.1%
74. 25%
63.4%
50.1%
68. 156
86.55 66.5%
73.7%
296
59.3%
17.1%
86, 2%
180.0
48.35
66.99%
52.2%
40.8%
60.65%
84,2%
X
20.0
0%.0
76. 5%
66.4%
52.7%
66. 2%
86.3%
59.6%
78.
54.1%
69.9%
30.0
60.0%
78. 19%
69.3%
53.7%
71.3%
87.8%
Event + Frame
36.0
59
77.2%
53.1%
71.1%
45.0
59.0%
76. 7%
67.1%
52.1%
71.1%
87.8%
60.0
57.6%
75. 29%
65.6%
50.2%
70.6%
87. 2%
54.8%
46.8%
60.3%
180.0
19.2%
67. .1%
53.3%
10.8%
62.3%
85, 85.19
/
20.0
57.4%
78. 29%
66.6%
51%
64.0%
83.7%
60.0%
79.456
70.1%
53.5%
1%
30.0
60.0%
79.7%
70. 87%
63.6%
69.9%
86 1%
36.0
60.1%
79.65
70. 8%
53.2%
70.356
85. 7%
Event Frame
45.0
59.5%
79.0%
69.3%
52.5%
70.8%
85.3%
60.0
58.8%
78.5%
69.0%
51.1%
71.1%
85.3%
56.5%
76.55
65.4%
70.1%
83.8%
180.0
50.9%
71.19%
56.2%
11.6%
63. 15%
82.9%
B.3
Complete Results of Hyperparameter Search
We include the complete results of the hyperparameter searching in Tab. 10.
C Additional Qualitative Results
C.1 Qualitative Comparisons of Event Detectors
We present additional qualitative comparisons in Fig. 9, Fig. 10, and Fig. 11 to further illustrate the advantages FlexEvent over three state-of-the-art methods RVT [18], SAST [39], and DAGr [16]- across nine diverse scenes.
As shown in Fig. under fast-motion conditions, RVT and SAST fail to detect pedestrians, while DAGr misclassifies many distant objects as pedestrians or vehicles. In contrast, FlexEvent accurately captures all objects, demonstrating superior robustness in challenging high-speed scenarios.
Similarly, in Fig. 10, RVT frequently misses objects in cluttered scenes, and both SAST and DAGr mistakenly recognize pedestrians and distant vehicles under noisy, occluded conditions, Here again,
26

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Table 10: The complete ablation results of different hyperparameter configurations in the FlexEvent framework. 7 y denotes the confidence threshold for car and pedestrian, respectively. y denotes the IoU threshold when filter tracking, track denotes the minimum track length. The reported results are the mAP scores on the test set of DSEC-De [16]. The best and 2nd best scores of each metric from each hyperparameter configuration highlighted in bold and underline.
prfar
ped
L rrack
ylou
Frequency (Hz)
mAP
AP 50
AP
APs
AP
APL
0.6
10
20.0
56.5%
81.3%
66.4%
51.8%
62.2%
82.8%
0.6
27.5
35.9%
74.7%
65.1
51.9%
61.4%
87.0%
0.6
0.3
10
0.8
30.0
56.7%
75.3%
66.0%
52.3%
63.5%
87.0%
0.6
0.3
10
0.8
36.0
57.2%
75.9%
67.0%
52.5%
64.8%
86.9%
0.6
0.3
10
0.8
45.0
57.1%
75.7%
66.2%
51.5%
66.1%
87.2%
0.6
0.3
10
0.8
60.0
56. 7%
75.3%
65.7%
50.3%
67.4%
87.3%
0.6
0.3
10
0.8
90.0
54.5%
73.2%
62.3%
47.3%
66.3%
86.2%
0.6
0.3
10
0.8
180.0
49.2%
68.2%
54.2%
40.8%
62.4%
85.5%
0.6
0.3
10
0.6
20.0
56. 7%
80.6%
65.5%
51.2%
63.0%
81.7%
0.6
0.3
10
0.6
27.5
37. 27
79.3%
63, 03.0%
51.9%
65.2%
84.5%
0.6
0.3
LO
30.0
37.7%
79.4%
66.0%
52.3%
66.3%
85.0%
0.6
0.3
10
36.0
57. .%
79.5%
66.
32.2%
66.8%
84.8%
0.6
10
45.0
37.7%
79.2%
65.6%
51.7%
67.1%
85.1%
0.6
0.3
10
60.0
37.0%
78.8%
64.8%
50.5%
67.6%
85 85.3%
0.6
0.3
10
0.6
90.0
34.3%
76.4%
60.0%
46.9%
66.3%
84.5%
0.6
0.3
10
0.6
180.0
47.0%
69.0%
49.1%
37.8%
61.1%
83.9%
0.6
0.3
8
0.6
20.0
56.3%
77 2%
64.9%
50.4%
64.1%
83.3%
0.6
0.3
0.6
27.5
58.5%
78.3%
18%1%
32.5%
66.8%
84.8%
0.6
0.3
0.6
30.0
38.8%
78.5%
68.7%
52.6%
67 67.9%
85.7%
0.6
0.3
0.6
36.0
59.1 59.1%
78.8%
69.0%
52.7%
68 68.8%
86.5%
0.6
0.3
0.6
45.0
58.8%
78.2%
68.6%
52.3%
68.
86.1%
0.6
0.3
60.0
38.4%
77.9%
67.5%
51.3%
69.6%
85.5%
0.6
90.0
36.2%
76.6%
64.8%
48.5%
68.3%
84.7%
0.6
180.0
51.2%
71.9%
56.3%
42.6%
64.2%
82.9%
0.6
0.3
0.6
20.0
57.3%
80.0%
65. 65.2%
51. 51.2%
65.8%
84. 1
0.6
27.5
39.4%
81.3%
68.5%
53.4
68 68.8%
85.7%
0.6
30.0
39.7%
81.7%
69.
53.7%
69.0%
85.3%
0.6
36.0
59.9%
81.4%
69. 0%
53.6%
69.8%
85.7%
0.6
0.3
0.6
45.0
39.3%
80.5%
67.9%
52.8%
69.4%
85.6%
0.6
0.3
0.6
60.0
58.5%
79.6%
67.0%
51.5%
69.6%
84.9%
0.6
0.3
0.6
90.0
35.7 7%
77.2%
62.8%
48.1% 1%
67 67.9%
84.3%
0.6
0.3
6
0.6
180.0
48.8%
70.6%
50.9%
80.8%
61.1%
83.4%
0.6
0.6
6
0.6
20.0
37.4%
78.2%
66.6%
51. 7%
64.9%
83.7%
0.6
0.6
6
0.6
27.5
60.0%
79.4%
70.1%
53.5%
68.4%
86.1%
0.6
0.6
6
0.6
30.0
60.0%
79.7%
70. 70.8%
53.6%
69 69.9%
86.11 86.1%
0.6
0.6
36.0
60.1%
79.6%
70.8%
33.2%
70.3%
85.7%
0.6
39.5%
79.0%
69.3%
52.5%
70.8%
85.3%
0.6
60.0
38.8%
78.5%
69.0%
51.1%
71.1%
85.3%
0.6
0.6
0.6
90.0
56.5%
76.5%
65.4%
48.2%
70.1%
83.8%
0.6
0.6
0.6
180.0
50.9%
71.4%
56.2%
41.6%
65.4%
82.9%
0.8
20.0
36.6%
80.7%
65.5%
50.8%
65.4%
82.6%
0.8
0.8
0.6
27.5
58.7%
81.9%
68.9%
52.8%
68.6%
84.9%
0.8
0.8
0.6
30.0
59.1%
82.0%
69. 2%
52.7%
69.5%
84.8%
0.8
0.8
6
0.6
36.0
58.9%
81.7%
68.8%
52.6%
69.9%
85.0%
0.8
0.8
6
0.6
45.0
58.4%
81.4%
67.7%
51.6%
69.8%
85.1%
0.8
0.8
6
0.6
60.0
57.4%
80.1%
66. 66.6%
50.2%
69.9%
84.3%
0.8
0.8
90.0
35.7%
78.6%
63. 7%
47.8%
68 89.9%
84.2%
0.8
180.0
50.2%
74.0%
55.2%
63.7%
83.1%
FlexEvent excels by reliably detecting all relevant objects, highlighting its ability to handle complex urban environments.
Finally, in Fig. 11, rapidly changing illumination leads RVT and DAGr to misinterpret subtle motion cues; yet FlexEvent preserves stable performance, underscoring enhanced temporal awareness. These observations confirm that FlexEvent not only surpasses competing methods in quantitative benchmarks but also consistently delivers high detection accuracy real-world conditions ensuring reliable performance in fast-moving, cluttered, dynamically lit scenes.
27

---

C.2 Comparisons under Different Frequencies
We further evaluate the performance FlexEvent across diverse event frequencies Fig. 12, Fig. 13, and Fig. 14. Specifically, we compare method with RVT under settings ranging from low- frequency (20 Hz) to high-frequency (180 Hz) event streams, including an extreme scenario with empty events.
In Fig. 12, RVT struggles to detect distant or partially occluded objects at both low and high frequencies, whereas FlexEvent consistently identifies all targets by adaptively fusing frame-based based evidence with event cues.
Similarly, in Fig. 13, RVT suffers substantial performance drops under sparse event conditions, often overlooking pedestrians; yet FlexEvent maintains reliable detection through its frequency-adaptive training strategy.
Finally, in Fig. 14, even when event input is minimal missing, FlexEvent retains high accuracy by leveraging complementary frame information. These comparisons reaffirm our robustness and adaptability, highlighting our ability to handle broad spectrum of event frequencies while preserving superior detection performance.
D Potential Societal Impact & Limitations
In this section, we discuss the potential societal impact of FlexEvent, including its positive contribu- tions, broader implications, and known limitations. While our method offers significant advancements in event camera object detection, it is important consider its broader consequences and areas for future improvement.
D.1 Societal Impact
The development of FlexEvent introduces several positive societal benefits, particularly in safety- critical applications such as autonomous driving, robotics, and surveillance. By enhancing the ability to detect fast-moving objects in real time, framework can improve the responsiveness and safety of autonomous systems operating in dynamic environments. This especially important for avoiding collisions responding to hazards in high-speed scenarios. For example, autonomous vehicles equipped with our approach can better detect pedestrians, cyclists, and other vehicles in real time, potentially reducing accidents and saving lives.
Additionally, the computational efficiency provided by the adaptive event-frame fusion (FlexFuse) and frequency addpttive learning (FlexTune) mechanisms reduces the need for resource-intensive training processes. This contributes to the broader societal goal of making advanced Al technologies more accessible and less energy-intensive, thereby minimizing the environmental impact of large-scale Al models. Our approach could also benefit industries beyond transportation, such as robotics for healthcare, industrial automation, and public safety.
D.2 Broader Impact
The broader implications of FlexEvent include its potential to advance the field of event-based vision and enable new applications where high temporal resolution is crucial. By overcoming the limitations of conventional fixed-frequency object detection methods, our approach paves the way for more flexible, adaptable AI systems. This could lead to improvements in areas such as drone navigation, real-time video analysis for security purposes, and human-robot collaboration, where detecting fast-moving objects and adapting to changing environments are critical.
Moreover, the development of efficient and scalable detection systems like our approach can drive constrained environments, such as low-power edge devices. These further innovation in high-performance detection systems more widely available, particularly in advancements could make developing regions or areas with limited access to computational resources.
However, as with any powerful technology. there is risk of misuse. Enhanced object detection capabilities could potentially be exploited for surveillance purposes, raising privacy concerns. As event camera technology becomes more widespread, it is important to establish ethical guidelines
28

---

and regulatory frameworks to ensure that these systems are used responsibly, particularly when monitoring public spaces or collecting sensitive data.
D.3 Known Limitations
While FlexEvent demonstrates significant performance improvements, there are several known limitations approach, which be summarized follows:
Dependence on the Quality Event Camera Data. The effectiveness of our approach relies on the quality of the event camera sensor. Inconsistent noisy event data, especially under poor lighting or extreme weather conditions, could affect detection performance. Future work could explore robustness sensor noise and adaptation to diverse environmental conditions.
Limited Generalization to Unseen Scenarios. Although our approach shows strong performance across varying frequencies, it may still face challenges in completely unseen environments, where the motion dynamics and scene conditions differ significantly from the training data. Investigating methods for domain adaptation online learning could help improve generalization to new contexts.
Resource Requirements for High-Fregg FreghFFry Data. While FlexFuse mitigates the computational cost of training on high-frequency event data, processing extremely tigh-frequency event streams still requires substantial computational resources during inference. This could limit the scalability on Assource-constrane devices or in real-time applications with stringent latency requirements.
29

---

E
Public Resources Used
In this section, we acknowledge the public resources used, during the course of this work.
E.1
Public Datasets Used
DSEC
CC BY-SA 4.0
DSEC-Det")
GNU General Public License v3.0
DSFC-Dettction"
Creative Commons Zero v1.0 Universal
DSEC-MOD
Unknown
Gen 1
Prophesee Genl Automotive Detection Dataset License
Mpxx
Prophesee IMpx Automotive Detection Dataset License
E.2
Public Implementations Used RVT
MIT License
SAST
MIT License
SSM
Unknown
LEOD
MIT License
DAGr
GNU General Public License v3.0
!ENe
Unknown
hactpe//dsec..ff..uh..h
http:://wwwaaaaaaa////////////// dataset
eeeeteeeeeeeeeeeeeeeeeeeeeee based dataset
30

---

Scene #1
Scene #2
Scene #3
PARP-3 SAST
FloxEverd Oun (0
Figure 9: Additional qualitative results of state-of-the-an event camera detectors. We compare the
proposed FlexEvent with RVT [18]. SAST [39], and DAGr [16] on the test set of Det. Best viewed in colors.
31

---

Scene #4
Scene #5
Scene #6
Fra nu Srruun
FVT CEEPPS 122
SAST CA-F-
1e- Lun FlexEvert
Figure 10: Additional qualitative results of state-of the-ttthettttt event camera detectors. We compare the
proposed FlexEvent with RVT [18]. SAST [39], and DAGr [16] on the test set of Det. Best viewed in colors.
32

---

Scene #7
Scene #B
Scene #9
Truth nu
22 SHH
SAYE FV
PRA--A SAST
FloxEvent (uur
Figure 11: Additional qualitative results of state-of the-hahert event camera detectors. We compare the
proposed FlexEvent with RVT [18]. SAST [39], and DAGr [16] on the test set of Det. Best viewed in colors.
33

---

Ground Truth
RVT [CVPR23]
FlexEvent (Ours)
20 HiZ
Fred e
1 H2
Emply Event
Figure 12: Additional qualitative comparisons of the RVT model [18] and the proposed FlexEvent under different event camera operation frequencies (20 Hz, 60 Hz, 90 Hz, and 180 Hz) and the empty event scenario. The experiments are conducted on the test set of DSEC-Det. Best viewed in colors.
34

---

Ground Truth
RVT [CVPR23
FlexEvent (Uurs)
L1 20 8 P00
ZH09 & hou
HH
a 0O
DOLL
Emply Event
Figure 13: Additional qualitative comparisons of the RVT model [18] and the proposed FlexEvent under different event camera operation frequencies (20 Hz, 60 Hz, 90 Hz, and 180 Hz) and the empty event scenario. The experiments are conducted on the test set of DSEC-Det. Best viewed in colors.
35

---

Ground Truth
RVT [CVPR23
FlexEvent (Ours)
20 HiZ
Freq e
Di
Trod 2
FROL 8 880 FIZ
Emply Evont
Figure 14: Additional qualitative comparisons of the RVT model [18] and the proposed FlexEvent under different event camera operation frequencies (20 Hz, 60 Hz, 90 Hz, and 180 Hz) and the empty event scenario. The experiments are conducted on the test set of DSEC-Det. Best viewed in colors.
36


Reviews:
[{"review_id": "2Mf5kTvPXD", "reviewer": "Reviewer_kyoZ", "summary": "This 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).", "questions": "My 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.", "limitations": "yes", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Pros:\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.", "was_revised": true, "final_justification": "Fusing the complementary advantages of events and RGB images is interesting for high-speed object detection. However, I am still not convinced of the experimental results with a better initialised backbone with RVT. Although the additional results in the rebuttal demonstrated a better performance without initialisation with RVT on one dataset, while the main experiments (including comparison with previous methods on several datasets and ablations) in the paper are highly based on this better initialised configuration. For me, it is hard to accept the current version without a major revision and a review process. I lean towards keeping my original score.", "rebuttal": "We sincerely thank Reviewer `kyoZ` for the thoughtful assessment and helpful suggestions, and we appreciate the recognition of our work as well-structured and promising. Below, we respond to your comments and suggestions in detail.\n\n---\n> **`Q1`:** *\"Aggregate information for a longer period for each event aggregation.\"*\n\n**A:** Thanks for your suggestion. In our setup, “high frequency” refers to the **sampling cadence** (label rate $a$ vs. higher event rate $b>a$) rather than to a fixed aggregation window. Our detection is **anchored at the end timestamp** $t_2$ of the labeled interval $[t_1, t_2]$ with $t_2 = t_1 + \\Delta t_a$. The inputs are: the RGB frame $F$ captured in $t_2$, the low-frequency event stream $E_a$ captured in $[t_1, t_2]$, and one high-frequency event slice $E_b$ of duration $\\Delta t_b$. \n\nWe **split the interval $\\Delta t_a$ into $b/a$ sub-intervals** and **sample one high-frequency event $E^b$** captured from the $b/a$ sub-intervals, detections are produced **at the end of $\\Delta t_a$**. This keeps the sampling gap small, pairs each RGB frame with its preceding high-frequency events, and introduces only **millisecond-scale jitter** that acts as implicit temporal augmentation for synchronization noise. \n\nTo address the reviewer’s suggestion, we add an ablation that keeps the **sampling gap $\\Delta t_a$** fixed but enriches semantics, we use **multi-sample encoding** within each $[t, t+\\Delta t_a]$: **take $K$ event slices captured from the $b/a$ sub-intervals instead of sampling only one**, encode each, then average features to align with one RGB frame. We report this variant as the FlexEvent** row in Table `R1` (see below from `Q2`), and the result shows that it yields a slight mAP improvement, while inference time increases approximately linearly with $K$. \n\nThis averaging effectively behaves as a low-pass filter, **weakening high-frequency event cues** (especially for small/fast targets) without improving supervision at >20 Hz (pseudo labels remain and can **introduce timing mismatch**), so the marginal accuracy gain does not justify the near-linear increase in latency/compute. \nAccordingly, we retain single-slice inference with cadence-aware gating and consider lightweight learned temporal pooling as a better trade-off.\n\n---\n> **`Q2`:** *\"More experiments with RGB-based and fusion-based methods.\"*\n\n**A:** Thank you for pointing this out. For a fair comparison with recent fusion-based detectors, we follow the **ACGR (CVPR'25) evaluation protocol** and report results on the **eight DSEC-Det categories**. We merge our numbers into ACGR’s Table `1` for a side-by-side view (Table `R1`). We also add the results of two other recent fusion-based methods, FAOD and HDI-Former. \n\nCompared with RGB-only baselines, our model achieves consistently **higher mAP**, reflecting the complementarity between frames (semantics) and events (high-frequency motion cues). Relative to prior fusion methods, we also observe gains in mAP, which we attribute to improved cross-modal alignment and frequency-aware supervision in our design.\n\n---\n***Table R1.** Additional experiments on DSEC-Det*\n\n| Modality        | Method             | Params | mAP   | mAP_50 | Runtime (ms) |\n|-----------------|--------------------|-:|:-:|:-:|:-:|\n| F           | Faster R-CNN       | 41.9M | 0.182 | 0.354   | 14.4         |\n| F           | YOLOX              | 48.6M | 0.266 | 0.482   | 12.1         |\n| F           | Swin Transformer   | 44.8M | 0.269 | 0.487   | 18.2         |\n| F           | Deformable DETR    | 48.2M | 0.257 | 0.474   | 16.3         |\n| E           | EMS-YOLO           | 6.2M  | 0.082 | 0.202   | 21.9         |\n| E           | AsyNet             | 15.2M | 0.106 | 0.243   | 10.0         |\n| E           | AEGNN              | 27.8M | 0.128 | 0.269   | 14.2         |\n| E           | ASTM-Net           | 39.6M | 0.159 | 0.306   | 22.9         |\n| F + E   | FPN-fusion         | /     | 0.152 | 0.289   | /            |\n| F + E   | SFNet              | /     | 0.268 | 0.412   | /            |\n| F + E   | DAGr               | 34.6M | 0.258 | 0.427   | 24.2         |\n| F + E   | SODFormer          | 87.8M | 0.281 | 0.504   | 33.9         |\n| F + E   | DAGr-ACGR          | 2.7M  | 0.284 | 0.502   | 9.1          |\n| F + E   | YOLOX-ACGR         | 48.7M | 0.313 | 0.513   | 13.2         |\n| F + E   | SODFormer-ACGR     | 41.4M | 0.317 | 0.519   | 16.6         |\n| F + E   | HDI-Former         | 64.2M | 0.291 | 0.522   | /            |\n| F + E   | FAOD               | 20.3M | 0.425 | 0.635   | 14.6         |\n| F + E   | FlexEvent*         | 45.4M | 0.441 | 0.652   | 14.3         |\n| F + E   | FlexEvent**        | 45.4M | 0.505 | 0.729   | 45.1         |\n| F + E   | **FlexEvent**          | 45.4M | **0.498** | **0.726**   | 14.3         |\n\n\n---\n> **`Q3`:** *\"Generalization to more object categories.\"*\n\n**A:**  Thank you for highlighting this. In Table `R2`, per-category results show that **large objects** (truck, bus) obtain the highest AP, while pedestrian, rider, and bicycle are more challenging due to **small scale and appearance variability**. Train is the weakest class because it has very few instances in the dataset. \n\nAll numbers are computed under the same settings as ACGR.  Pretraining is kept identical across compared methods/backbones, ablations indicate that the main gains stem from our FlexFuse/FlexTune rather than pretraining.\n\n---\n\n***Table R2.** Per-class on eight categories*\n| Class        | AP    | Class       | AP    |\n|--------------|:-:|----|:-:|\n| pedestrian   | 0.456 | rider       | 0.398 |\n| car          | 0.672 | bus         | 0.729 |\n| truck        | 0.763 | bicycle     | 0.401 |\n| motorcycle   | 0.565 | train       | 0.000 |\n| **mAP (mean)** | **0.498** |             |       |\n\n---\n> **`Q4`:** *\"RVT for events backbone and ResNet-50 for RGB backbone and pre-trained weights.\"*\n\n**A:** We appreciate this concern. We adopt **modality-appropriate** encoders, ResNet-50 for RGB frames and RVT for events, because the two signals have **very different statistics** (intensity images vs. asynchronous polarity streams). Our focus is on flexible event–image fusion and frequency-adaptive learning, so we keep the backbones standard and isolate our contributions in the fusion/adaptation modules. ResNet-50 is initialized from ImageNet-1K and RVT from the 1Mpx event dataset, then both are fine-tuned in our detector. \n\nIn ablations, RVT pretraining gives a small gain, and the main improvements come from FlexFuse and FlexTune. When we train RVT from scratch (FlexEvent* row in table above), our method still outperforms prior work, which shows that pretraining is helpful but not the reason for the overall gains. Given the small size of DSEC and the common ImageNet practice, we did not repeat a from-scratch ResNet study.\n\n---\nLast but not least, we would like to sincerely thank Reviewer `kyoZ` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "FioUnxMteV", "reviewer": "Reviewer_xQ9Q", "summary": "FlexEvent 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.", "questions": "- 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I do not have a major concern for this paper. The author addressed my questions. The weakness of this paper is a minor improvement compared to concurrent work.  The author explained the evaluation limitation and I accept that. I will keep my score.", "rebuttal": "We sincerely thank reviewer `xQ9Q` for the thoughtful assessment and helpful suggestions, and we appreciate the recognition of our intuitive motivation and real-time suitability. Below, we respond to your comments and suggestions in detail.\n\n---\n> **`Q1`:** *\"Novelty over concurrent work FAOD.\"*\n\n**A:** We appreciate this insightful concern. FAOD and our work are concurrent, and both are in submission. The comparisons are summarized as below:\n\n- **Label-efficient frequency adaptation.**  \n  FlexTune transfers low-frequency supervision to high frequencies via **pseudo-labels + temporal-consistency calibration + cyclic self-training**, requiring no extra high-frequency labels. By contrast, FAOD’s Time-Shift is closer to a data-augmentation/consistency trick: it does not create high-frequency supervision, and its effect depends on tuned hyperparameters (shift range/schedule). Large shifts can introduce label/content misalignment, so it is more sensitive to tuning and has greater limitations. FlexTune thus provides a more reliable, label-efficient path to frequency adaptation.\n\n- **More input-adaptive and interpretable fusion.**  \n  FlexFuse learns gated Softmax weights for the event and frame branches and adds a variance regularizer to prevent collapse, yielding explicit, interpretable branch weights that show how fusion adjusts with the input condition and confidence. FAOD emphasizes alignment correction followed by attention, which improves geometry/style consistency but does not expose a direct, per-input fusion weighting. FlexFuse, therefore, offers stronger interpretability and direct input adaptivity.\n\n- **High-frequency robustness and scalable evaluation.**  \n  FlexEvent provides fine-grained results from 20→180 Hz; it maintains 50.9% mAP at 180 Hz (full model). With events-only, enabling FlexTune lifts 180 Hz mAP from 22.9→30.4, evidencing true frequency adaptation rather than reliance on augmentation alone. Results on DSEC-Detection / DSEC-MOD further indicate cross-dataset transferability. On DSEC-Det, FlexEvent achieves an eight-class mean of 49.8%, outperforming FAOD’s reported 42.5%, as shown in Table `R1` below.\n\n- **Engineering practicality.**  \n  FlexEvent performs frequency adaptation offline, adding no inference overhead, and inference runs at ~12–14 ms/frame over 20–180 Hz. Both FlexEvent and FAOD are real-time, but FlexEvent sustains real-time performance across a broader frequency range with stronger robustness.\n\nIn summary, FlexEvent is novel in unifying **frequency-aware fusion (FlexFuse)** with **label-efficient frequency adaptation (FlexTune)**, yielding higher accuracy on shared benchmarks and broader frequency robustness, while keeping real-time inference without extra cost.\n\n---\n\n***Table R1.** Additional experiments on DSEC-Det*\n\n| Modality        | Method             | Params | mAP   | mAP_50 | Runtime (ms) |\n|-----------------|--------------------|-:|:-:|:-:|:-:|\n| F           | Faster R-CNN       | 41.9M | 0.182 | 0.354   | 14.4         |\n| F           | YOLOX              | 48.6M | 0.266 | 0.482   | 12.1         |\n| F           | Swin Transformer   | 44.8M | 0.269 | 0.487   | 18.2         |\n| F           | Deformable DETR    | 48.2M | 0.257 | 0.474   | 16.3         |\n| E           | EMS-YOLO           | 6.2M  | 0.082 | 0.202   | 21.9         |\n| E           | AsyNet             | 15.2M | 0.106 | 0.243   | 10.0         |\n| E           | AEGNN              | 27.8M | 0.128 | 0.269   | 14.2         |\n| E           | ASTM-Net           | 39.6M | 0.159 | 0.306   | 22.9         |\n| F + E   | FPN-fusion         | /     | 0.152 | 0.289   | /            |\n| F + E   | SFNet              | /     | 0.268 | 0.412   | /            |\n| F + E   | DAGr               | 34.6M | 0.258 | 0.427   | 24.2         |\n| F + E   | SODFormer          | 87.8M | 0.281 | 0.504   | 33.9         |\n| F + E   | DAGr-ACGR          | 2.7M  | 0.284 | 0.502   | 9.1          |\n| F + E   | YOLOX-ACGR         | 48.7M | 0.313 | 0.513   | 13.2         |\n| F + E   | SODFormer-ACGR     | 41.4M | 0.317 | 0.519   | 16.6         |\n| F + E   | HDI-Former         | 64.2M | 0.291 | 0.522   | /            |\n| F + E   | FAOD               | 20.3M | 0.425 | 0.635   | 14.6         |\n| F + E   | FlexEvent*         | 45.4M | 0.441 | 0.652   | 14.3         |\n| F + E   | FlexEvent**        | 45.4M | 0.505 | 0.729   | 45.1         |\n| F + E   | **FlexEvent**          | 45.4M | **0.498** | **0.726**   | 14.3         |\n\n---\n> **`Q2`:** *\"Lack of generality analysis.\"*\n\n**A:** Thank you for your question. We mainly follow **ACGR (CVPR’25) and DAGr (Nature’24)** evaluation protocols and evaluate on DSEC because it is a **standard, large-scale** benchmark for outdoor event detection with paired event + frame data, and event cameras have shown great potential in outdoor autonomous driving scenarios, while large-scale indoor datasets are **limited**. \n\nNevertheless, across three variants of the DSEC dataset, our method has shown strong superiority over previous methods across different object categories and scenarios, which proves our effectiveness. In the revision, we will add more experiments on another larger outdoor event–image dataset (PKU-DAVIS-SOD) and on an indoor event dataset with minimal tuning, and we will release code/configs to facilitate third-party validation.\n\n---\n> **`Q3`:** *\"mAP rises from 20→36 Hz, then degrades toward 180 Hz?\"*\n\n**A:** We appreciate your emphasis on this point. The 20 Hz results are evaluated against **human-annotated ground truth**, whereas >20 Hz results use **interpolated/pseudo ground truth (as stated in line 277) because no human ground truth at high frequency is available**. Because the supervision differs, the numbers are not directly comparable from 20→36 Hz. As the frequency increases from 36 to 180 Hz, with a fixed aggregation window, each event slice becomes sparser and noisier (background activity dominates), which hurts small/fast classes; in parallel, pseudo-label noise grows at higher rates. Together, these effects explain the mAP drop toward 180 Hz.\n\n---\n> **`Q4`:** *\"Comparison to other fusion+frequency-adaptive works.\"*\n\n**A:** Thank you for pointing this out. We compare our approach with **FAOD** and **DAGr (Nature’24)**:\n\n- ***Fusion / Alignment.*** FlexEvent (FlexFuse) learns frequency-aware gating to balance event/RGB features; FAOD uses an Align Module (e.g., AdaIN/deformable ops) plus attention after alignment; DAGr fuses unidirectional image features into an asynchronous GNN over events.\n\n- ***Frequency adaptation.*** FlexEvent (FlexTune) provides label-efficient adaptation via pseudo-labels + temporal consistency+cyclic self-training; FAOD leverages Time-Shift training to reduce train–test frequency mismatch; DAGr does not propose a dedicated module to handle frequency adaptation.\n\n- ***Scope.*** FlexEvent is backbone-agnostic and covers 20–180 Hz with the same detector; FAOD focuses on high-frequency fusion with alignment; DAGr demonstrates a hybrid image+event pipeline for low-latency automotive detection.\n\n- ***Performance.*** FlexEvent demonstrates the strongest result across different frequencies, as shown in Table `R1`. In terms of efficiency, FlexEvent performs adaptation offline and adds ~1.5 M params in the head; inference remains real-time. FAOD emphasizes light parameter count and alignment; DAGr is the slowest, even though it has a smaller model size.\n\nAs suggested, we have included a side-by-side summary table in the revised manuscript for better clarity.\n\n---\nLast but not least, we would like to sincerely thank Reviewer `xQ9Q` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "EVIixL6C4v", "reviewer": "Reviewer_BJuF", "summary": "The 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.", "questions": "1）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).", "limitations": "While 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.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thank you for your response. The majority of the concerns raised during the initial review have been adequately addressed. I will keep my rating.", "rebuttal": "We sincerely thank Reviewer `BJuF` for devoting time to this review and providing constructive feedback. We are encouraged by the thoughtful assessment and helpful suggestions. Below, we respond to your comments and suggestions in detail.\n\n---\n> **`Q1`:** *\"Robustness to modality-specific noise.\"*\n\n**A:** Thank you for emphasizing this important aspect. Event cameras are particularly valuable in adverse conditions (e.g., low light, rapid motion). As shown in Table `R1` below, our fusion model consistently outperforms RGB-only baselines, which tend to degrade severely under low illumination. \n\n***Table R1.** Additional experiments on DSEC-Det*\n\n| Modality        | Method             | Params | mAP   | mAP_50 | Runtime (ms) |\n|-----------------|--------------------|-:|:-:|:-:|:-:|\n| F           | Faster R-CNN       | 41.9M | 0.182 | 0.354   | 14.4         |\n| F           | YOLOX              | 48.6M | 0.266 | 0.482   | 12.1         |\n| F           | Swin Transformer   | 44.8M | 0.269 | 0.487   | 18.2         |\n| F           | Deformable DETR    | 48.2M | 0.257 | 0.474   | 16.3         |\n| E           | EMS-YOLO           | 6.2M  | 0.082 | 0.202   | 21.9         |\n| E           | AsyNet             | 15.2M | 0.106 | 0.243   | 10.0         |\n| E           | AEGNN              | 27.8M | 0.128 | 0.269   | 14.2         |\n| E           | ASTM-Net           | 39.6M | 0.159 | 0.306   | 22.9         |\n| F + E   | FPN-fusion         | /     | 0.152 | 0.289   | /            |\n| F + E   | SFNet              | /     | 0.268 | 0.412   | /            |\n| F + E   | DAGr               | 34.6M | 0.258 | 0.427   | 24.2         |\n| F + E   | SODFormer          | 87.8M | 0.281 | 0.504   | 33.9         |\n| F + E   | DAGr-ACGR          | 2.7M  | 0.284 | 0.502   | 9.1          |\n| F + E   | YOLOX-ACGR         | 48.7M | 0.313 | 0.513   | 13.2         |\n| F + E   | SODFormer-ACGR     | 41.4M | 0.317 | 0.519   | 16.6         |\n| F + E   | HDI-Former         | 64.2M | 0.291 | 0.522   | /            |\n| F + E   | FAOD               | 20.3M | 0.425 | 0.635   | 14.6         |\n| F + E   | FlexEvent*         | 45.4M | 0.441 | 0.652   | 14.3         |\n| F + E   | FlexEvent**        | 45.4M | 0.505 | 0.729   | 45.1         |\n| F + E   | **FlexEvent**          | 45.4M | **0.498** | **0.726**   | 14.3         |\n\nOn DSEC **night** sequences (noisy RGB), our model achieves 53.2 mAP, suggesting that the fusion gate effectively up-weights the event branch when frames deteriorate. In **sparse-event** regimes (high-frequency inputs), at 180 Hz (events-only) we obtain 30.4 mAP vs 22.9 for the baseline, indicating resilience when the event stream becomes sparse. \n\nIn the revision, we will provide additional diagnostics, like noise-stress tests (low-light RGB, background-activity in events). We believe these additions will make the robustness story clearer and more reproducible.\n\n---\n> **`Q2`:** *\"FlexTune vs semi-supervised baselines.\"*\n\n**A:** We appreciate the request for clearer positioning. We compare with **LEOD** (a semi-supervised baseline) in Table `1`, where we obtain **higher mAP**. In ablations, even with events-only, we reach 54.6 mAP (LEOD 41.1), underscoring that our FlexTune (pseudo labels + temporal-consistency calibration + cyclic self-training) is effective beyond backbone choice. There are no widely used SSL baselines with consistency regularization or teacher-student models tailored to event-frame detection. In the future, we will try EMA teacher–student, consistency regularization, and FixMatch/Noisy-Student-style bootstrapping under the same unlabeled high-frequency pool and compare with our work.\n\n---\n> **`Q3`:** *\"Edge feasibility with a 45.4 M-param model.\"*\n\n**A:** This is an important practical consideration. The **fusion head contributes ~1.5 M** parameters; the remainder (~44 M) lies in the backbone, which is already optimized in prior work. Our FP32 checkpoint is ~**546 MB**. As summarized in Table `R1` in `Q1`, our model is **faster and/or smaller** than several prior detectors that have been successfully deployed, and we sustain **real-time** inference. To address feasibility more thoroughly, we will report real-time throughput/memory on an edge SoC and include FP16 results in the revised version. \n\n---\n> **`Q4`:** *\"Generalization to untrained frequencies.\"*\n\n**A:** Thank you for pointing this out. We already report 20–180 Hz, covering sampling gaps from **50 ms → 5.56 ms**. Moving to 200 Hz+ reduces the gap only to ~**5 ms**, a marginal change. We expect similar behavior,  and in the revision/supplement, we will add zero-shot results at 200 Hz (and higher) under the same protocol (no re-training).\n\n---\n> **`Q5`:** *\"Potential biases.\"*\n\n**A:** We appreciate this reminder. **Per-class** results (Table `R2`) show higher AP for **large vehicles** and lower AP for **small objects** (bicycle/rider), and performance is better in **day** (61.6 mAP) than **night** (53.2 mAP), according to our experiments. Importantly, our method remains stronger than all compared baselines across these conditions. In the revision, we will include more class-wise and condition-wise results, and propose some practical mitigations (rebalancing, small-object augmentation, confidence calibration for deployment). We see this as essential documentation for safe use in real-world settings.\n\n***Table R2.** Per-class on eight categories*\n| Class        | AP    | Class       | AP    |\n|--------------|:-:|----|:-:|\n| pedestrian   | 0.456 | rider       | 0.398 |\n| car          | 0.672 | bus         | 0.729 |\n| truck        | 0.763 | bicycle     | 0.401 |\n| motorcycle   | 0.565 | train       | 0.000 |\n| **mAP (mean)** | **0.498** |             |       |\n\n---\n> **`Q6`:** *\"Safety implications and dataset bias.\"*\n\n**A:** Thank you for emphasizing safety. We will add a dedicated section outlining: (i) operational design domain (lighting/motion ranges), (ii) risks from **detection errors** in autonomous scenarios and recommended **thresholding/fallbacks** under low confidence, (iii) **shift detection** (e.g., day → night) and suggested interventions (recalibration or conservative policies), and (iv) a brief discussion of **dataset bias** and its implications. \n\n---\nLast but not least, we would like to sincerely thank Reviewer `BJuF` again for the valuable time and constructive feedback provided during this review."}, {"review_id": "r6AExpOG8o", "reviewer": "Reviewer_RR3R", "summary": "This 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.", "questions": "In 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})\\\\).", "limitations": "The limitations have been discussed in the appendix.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The rebuttal addresses most concerns, so I maintain my original score.", "rebuttal": "We sincerely thank Reviewer `RR3R` for the thoughtful assessment and helpful suggestions, and we appreciate the recognition of our good presentation and comprehensive experiments. Below, we respond to your comments and suggestions in detail.\n\n---\n> **`Q1`:** *\"Does Eq. (2) use Kronecker delta functions? Please clarify the notation.\"*\n\n**A:** Thanks for your comment. Yes. In Eq. `2`, the symbols $\\delta(·)$ denote **Kronecker deltas over discrete indices (polarity, time bin, and pixel coordinates)**. We will add a sentence in Sec. `3.1`, stating this explicitly to avoid ambiguity. This event-tensor construction (discrete binning with deltas) is standard and follows the RVT formulation.\n\n---\n> **`Q2`:** *\"The event representation and pre-processing follow the design in RVT, but no citation is provided here.\"*\n\n**A:** Thank you for asking. We apologize for our oversight. We will add an explicit citation to RVT in Sec. `3.1`, where we construct the event tensor, and state that our implementation follows RVT to enable 2D convolutions.\n\n---\n> **`Q3`:** *\"It is unclear how a 4D tensor is processed with 2D convolutions for subsequent RGB-event fusion.\"*\n\n**A:** Thanks for your insightful concern. As in RVT, we **flatten polarity and time into the channel axis**, converting the 4D tensor to a 3D tensor of shape (2T, H, W) that is directly compatible with 2D convolutions. We will add more details about the data processing in the supplementary material. \n\n---\n> **`Q4`:** *\"With dynamic aggregation, how is the high-frequency event slice aligned with the prediction time at the end of the sampling interval?\"*\n\n**A:** We appreciate this valuable question. Our detection is **anchored at the end timestamp** $t_2$ of the labeled interval $[t_1, t_2]$ with $t_2 = t_1 + \\Delta t_a$. The inputs are: the RGB frame $F(t_2)$, the low-frequency event aggregation $E_a=[t_1, t_2]$, and one high-frequency event slice $E_b$ of duration $\\Delta t_b$. We randomly sample $E_b$ within $[t_1, t_2]$; in practice, we allow a **millisecond-scale jitter** so the slice may end at $t_2-\\varepsilon$ ($\\varepsilon$ is small), which serves as implicit temporal augmentation against synchronization noise. This keeps the prediction target (the boxes at $t_2$) **aligned** with its preceding event evidence, ensuring $(F, E_a, E_b)$ are consistently paired across modalities and frequencies for subsequent fusion and decoding.\n\n---\n> **`Q5`:** *\"RGB and event cameras may differ in intrinsics/extrinsics/resolution; events also show duplication from temporal accumulation. Did you consider cross-modal alignment?\"*\n\n**A:** Yes. We **synchronize the event stream and frames** over $[t_1, t_2]$ and operate in a common image plane $H \\times W$ before fusion (resize/rectify using the dataset’s provided calibration). FlexFuse then adapts the contribution of the two modalities via learned soft weights, which mitigates residual appearance/geometric mismatch and duplication artifacts at the feature level. We will add a short implementation note on using the provided calibration and the shared resolution in the revised version.\n\n---\n> **`Q6`:** *\"In Eq. (7), what is the weighting factor $\\lambda$? What happens if $\\lambda=0$ (no regularization)?\"*\n\n**A:** Thank you for pointing this out. Eq. `7` adds a variance-over-mean regularizer on the fusion gates ($\\alpha$, $\\beta$) with weight $\\lambda$. We use a small positive $\\lambda = 0.1$ chosen on the validation set. This regularization ensures balanced utilization of both the event and frame branches,  and it penalizes large variations in the soft weights, encouraging a more uniform contribution from both modalities. Intuitively, setting $\\lambda = 0$ removes the balance constraint and can **let one branch dominate**. In practice, we observe a small drop in mAP without this term, and we will report the numbers in an ablation table in the revised version.\n\n---\n> **`Q7`:** *\"What is $\\beta$ in $\\mathcal{L}_\\mathrm{tune}$? If the low-frequency label and high-frequency pseudo label become very similar at high rates, is the term still necessary?\"*\n\n**A:** Thanks for raising this point. The tuning loss mixes human GT and high-frequency pseudo labels with coefficient $\\beta$ (chosen on validation, **$\\beta = 0.5$** in our experiments to reflect moderate trust in the pseudo labels). When the low-frequency label $y$ and the high-frequency pseudo label $\\tilde y$ become very similar at high rates, the pseudo-label term behaves as a **consistency regularizer** across dense time steps: if $\\hat y \\approx \\tilde y \\approx y$, its contribution (and gradient) is near zero, so it **does not bias** training; if small misalignments arise from sync jitter or occlusions, it **stabilizes** predictions and helps propagate supervision between sparse GT frames. In ablations, setting **$\\beta=0$** means not using pseudo high-frequency label, which yields a performance drop (48 → 43.7), especially at mid/high frequencies. \n\nAs a practical refinement, we can **confidence-weight** the pseudo-label term and/or **decay $\\beta$** with frequency to avoid over-reliance when $\\tilde y$ is already near-perfect.\n\n---\nLast but not least, we would like to sincerely thank Reviewer `RR3R` again for the valuable time and constructive feedback provided during this review."}]

Author rebuttal:

Meta-review: The paper introduces FlexEvent, a frequency-adaptive detection framework for event cameras that combines FlexFuse—an event–RGB fusion module tuned on-the-fly—with FlexTune, a label-adjustment mechanism that lets a single model operate accurately from 20 Hz to 180 Hz. Extensive tests on large-scale datasets show consistent gains over prior art across both standard and extreme-frequency settings, and code will be released.

Three positive reviews appreciate the intuitive motivation, real-time suitability, and clear writing, while one negative review questions the fairness of the experimental comparison. In rebuttal, the authors supplied additional results demonstrating that FlexEvent’s superiority is not an artifact of pretraining. Although the lone negative stance persists, the new evidence adequately supports the claimed effectiveness. Balancing the strong motivation, solid results, and community benefit, I recommend Accept.
Decision: Accept (poster)