LiFR-Seg: Anytime High-Frame-Rate Segmentation via Event-Guided Propagation
Authors: Xiaoshan Wu
Xiaoyang Lyu
Yifei Yu
Bo Wang
Zhongrui Wang
Xiaojuan Qi
Venue: ICLR 2026 Poster (2026)
Keywords: Event Camera
Neuromorphic Computing
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Dense semantic segmentation in dynamic environments is fundamentally limited by the low-frame-rate (LFR) nature of standard cameras, which creates critical perceptual gaps between frames.
To solve this, we introduce *Anytime Interframe Semantic Segmentation*: a new task for predicting segmentation at any arbitrary time using only a single past RGB frame and a stream of asynchronous event data.
This task presents a core challenge: how to robustly propagate dense semantic features using a motion field derived from sparse and often noisy event data, all while mitigating feature degradation in highly dynamic scenes.
We propose LiFR-Seg, a novel framework that directly addresses these challenges by propagating deep semantic features through time. The core of our method is an *uncertainty-aware warping process*, guided by an event-driven motion field and its learned, explicit confidence. A *temporal memory attention* module further ensures coherence in dynamic scenarios.
We validate our method on the DSEC dataset and a new high-frequency synthetic benchmark (SHF-DSEC) we contribute. Remarkably, our LFR system achieves performance (73.82\% mIoU on DSEC) that is statistically indistinguishable from an HFR upper-bound (within 0.09\%) that has full access to the target frame.
% We further demonstrate superior robustness in *highly dynamic* (M3ED-Drone \& Quadruped) and *low-light* (DSEC-Night) scenarios, where our method can even surpass the HFR baseline.
We further demonstrate superior robustness across extreme scenarios: in highly dynamic (M3ED) tests, our method closely matches the HFR baseline's performance, while in the low-light (DSEC-Night) evaluation, it even surpasses it.
This work presents a new, efficient paradigm for achieving robust, high-frame-rate perception with low-frame-rate hardware.

Published as a conference paper at ICLR 2026
LIFR-SEG: ANYTIME HiGH-FRAME-RAY SEGMENTA- TION VIA EVENT-GUIDED PROPAGATION
Xiaoshan Wu, Xiaoyang Lyu', Yifei Yu & Bo Wang Department of Electrical and Computer Engineering
The University of Hong Kong (xiaoshan, shawl 1 yu, y fuu, bowang98 (eeegg99)@ccoceeeee @connect. hku. hk
Zhongrui Wang
Xiaojuan Qi'
School of Microelectronics
Department of Electrical and Computer Engineering The University of Hong Kong x xjqi eegi@eees @eee. hku. hk
Southern University of Science and Technology wangzr sustech. edu. on
ABSTRACT
Dense semantic segmentation in dynamic environments is fundamentally limited by the wowfrrae--rate (LFR) nature of standard cameras, which creates critical perceptual gaps between frames. To solve this, we introduce Anytime Interframe Semantic Segmentation: new task for predicting segmentation at any arbitrary time using only single past RGB frame and stream of asynchronous event data. This task presents a core challenge: how to robustly propagate dense semantic features using a motion field derived from sparse and often noisy event data, all while mitigating feature degradation highly dynamic scenes. We propose LiFR-Seg novel framework that directly addresses these challenges by propagating deep semantic features through time. The core of our method is an uncertainty- -aware warping process, guided by event-driven motion field and its learned, explicit confidence. A temporal memory attention module further ensures coherence in dynamic scenarios. We validate our method on the DSEC dataset and new high- frequency synthetic benchmark (SHF-DSEC) we contribute. Remarkably, our LFR system achieves performance (73.82% mloU on DSEC) that is statistically indistinguishable from an HFR upper-bound (within 0.09%) that has full access the target frame. We further demonstrate superior robustness across extreme scenarios: in highly dynamic (M3ED) tests, our method closely matches the HFR baseline's performance, while the low-light (DSEC-Night) evaluation, it even surpasses it. This work presents new, efficient paradigm for achieving robust, high-framee fraee-rate perception with frame-rate hardware.
INTRODUCTION
Accurate and dense semantic understanding of dynamic scenes is critical capability for autonomous systems, including self-driving cars, drones, and robotics Dovesi al. (2020); Tsai et al. (2023). The prevalent paradigm, however, relies conventional RGB cameras that capture information at discrete, often low, frequencies (c.g., 20 Hz). This low sampling rate creates significant "perceptual gaps". or "blind spots", between frames. This results in crucial "blind time interval" during which fast-moving or abruptly appearing objects are not perceived, posing a severe risk in high-speed scenarios Gallego et al. (2020); Guo et al. (2018).
The scenario in Fig. illustrates this danger: a pedestrian suddenly enters a vehicle's path. At timestamp the Cow-Frame-RRte (LFR) system (Fig. 1b) detects no hazard. In the very next frame, at timestamp + Al, the pedestrian appears, but is already too late for the autonomous system to react. This motivates the need for High-Frame- (HFR) perception system that can produce
PProject lead.
1 "Corresponding author.

---

Published as a conference paper at ICLR 2026
Timestarp
Tinesaani
Timestarpp
?
Timestmp C
Danger
Late!
(b) LFR System
Tinestanp
Danger Detected!
Timesamp
Event Stream
(a) Perceptual Gap Scenario in 3D
(<) HFR System
Figure 1: Bridging the Perceptual Gap in High-Speed Scenarios. A critical "Blind Time Interval" for LFR systems is illustrated: (a) During to + At, a pedestrian rapidly enters the vehicle's s path. (b) A standard LFR system, constrained by discrete frames, Too Late detects danger only by t + At. (c) In stark contrast, our HFR Anytime System leverages continuous events to detect imminent danger + 81, providing crucial early warning and bridging this gap.
reliable predictions at any moment in time. To formalize this ambition, we propose a new task, Anytime Interframe Semantic Segmentation. While high-speed RGB cameras could mitigate this gap, their prohibitive cost, high power consumption, and massive data bandwidth requirements make them impractical for scalable, real-world deployment (detailed in Appendix D).
Event cameras, in contrast, record brightness changes asynchronously at microsecond resolution Gal- lego et al. (2020); Berlincioni et al. (2023); Wang al. (2024), naturally capturing nigh-tempooal- resolution motion while consuming far less power and bandwidth. However, an event camera is a temporally dense but spatially sparse camera, which limits its semantic information. Thus RGB camera cooperating with event streams offers a more practical and scalable way to build an HFR perception system.
Formally, we define the anytime interframe semantic segmentation task as predicting a dense semantic map at any arbitrary timestamp L + 8t within a perceptual gap (4, + Af). given only the initial RGB frame I, and the corresponding event stream & Et-A--u+6t- This formulation imposes two critical constraints that differentiate our work from standard paradigms: Causality (requiring no future frames like T++ and Anytime Prediction (predicting for any 5t, not just at fixed frame times). As we will illustrate our baseline comparison (Fig. 3), existing paradigms fail one or both of these constraints: standard video interpolation is often non-causal, while multi-modal fusion methods are typically not anytime-capable. Our work presents the first framework designed to satisfy both.
This task presents a non-trivial challenge: how to effectively merge the rich, static semantic context from the past RGB frame I, with the temporally dense, but spatially sparse and often noisy, event stream Ei- Our core insight is to leverage the continuous event stream estimate high-frequency motion field ($3.2), which serves as a robust bridge to temporally propagate the deep semantic features from Te to the target time ( + St. To achieve this, framework, LiFR-Seg, introduces three key technical designs. First, we operate on and propagate multi-scale deep semantic features, as this preserves semantic detail. Second, we integrate an uncertainty-awas mechanism (detailed in $3.3) that explicitly learns to modulate the feature propagation based on the estimated motion reliability. Finally, ensure temporal consistency and handle occlusions over long prediction intervals, we incorporate temporal memory attention module ($3.4).
warping
We rigorously evaluate our framework on a comprehensive benchmark comprising the real-world DSEC dataset Gehrig et al. (2021a) and a new, figh-frreuency synthetic dataset (SHF-DSEF that we contribute. Our experiments show that combining low-frame-rate RGB camera with asynchronous event streams achieves performance on par with fully high-frame-rand system. On DSEC, our method, despite having no access to the interframe RGB data at + St, still achieves 73.82% mIoU, demonstrating gap of less than 0.09 % compared to the 73.91% mIoU achieved by an ideal HFR upper-bound model ($5.1) with full access to the target frame. Furthermore, we demonstrate strong robustness in challenging scenarios; the highly dynamic M3ED dataset, approach outperforms all baselines, and in zero-shot evaluation on the DSEC-Night benchmark, even surpasses the upper bound, confirming its efficacy in high-motion and low-light conditions.
2

---

Published as a conference paper at ICLR 2026
Our contributions are threefold:
We introduce Anytime Interframe Semantic Segmentation, novel and practical task for perception in dynamic environments, effectively bridging the "perceptual gap" inherent in standard camera systems.
We propose multi-modal framework that robustly propagates semantics from single RGB frame using event-driven motion cues. Key components include uncertainty-awar feature warping and temporal memory mechanism.
We release a new high-frequency synthetic dataset (SHF-DSEC) and establish strong benchmark. We demonstrate state-of-the-air performance, showing that our low-frame-rate system matches a high-frame-ras frame-rate upper bound and excels in high-dynamic and low-light scenarios.
RELATED WORK
Video Semantic Segmentation (VSS) Video Semantic Segmentation (VSS) leverages temporal coherence to improve segmentation consistency and efficiency across frames Guo et al. 20; Mo al. (2022). Early methods focused on spatial feature extraction Yu et al. (2018); Li et al. (2022), while more recent approaches explicitly propagate information. For instance, Deep Feature Flow Zhu et al. (2017) uses optical flow to warp features from keyframes to subsequent frames, but this is primarily for acceleration of an already dense video stream. Other modern methods Ravi al. (2024) integrate temporal memory to exploit motion cues. However, all these methods are fundamentally frame-based. They presuppose dense, high-frame-rate (HFR) RGB video stream their input. They are not designed to solve the "perceptual gap" problem we address: predicting segmentation at arbitrary time + öt using only single past RGB frame from time t.
Event-based Vision and Segmentation Event cameras offer an alternative sensing modality, captur- ing pixel-level brightness changes asynchronously with high temporal resolution and high dynamic range Gallego et al. (2020); Berlincioni et al. (2023). This makes them ideal for capturing motion. However, their data is spatially sparse and lacks the rich semantic texture of RGB frames. Conse- quently, methods for event-only semantic segmentation Alonso & Murillo (2019); Binas et al. (2017); Zhu al. (2021) often struggle to produce dense, high-fidelity semantic maps, which highlights the clear necessity of multi-modal approach that includes RGB context.
Multi-Modal RGB-Event Fusion and Propagation To leverage the strengths of both sensors, several multi-modal frameworks have been proposed. One dominant paradigm is feature fusion. Methods like CMNeXt Zhang et al. (2023) and EISNet Xie et al. (2024) (which we compare against in Sec. 5) use parallel encoders to extract features from both RGB and event data, then fuse them using attention or concatenation. However, these methods are designed for enhancement that is, using events from At to improve the segmentation of the RGB frame at time t. They do not perform temporal propagation into a future "blind gap" from a single I, which the core of our task.
more relevant paradigm is temporal propagation using event- based optical flow Wan et al. (2022); Gehrig al. (2024). The concept of feature warping via flow is established in VSS Zhu al. (2017) and video interpolation Niklaus & Liu (2020). However, these prior works typically warp information between two known RGB frames, requiring future frame 4++1r- Our work is the first leverage the unique properties of event- based flow to propagate deep semantic features from a single past RGB frame to an arbitrary future timestamp. We further innovate by introducing an uncertainty- mechanism to handle flow inaccuracies from sparse events and a memory module to ensure long-term temporal consistency.
3 METHOD
3.1 FRAMEWORK OVERVIEW
Our goal is to solve the task of Anytime Interframe Semantic Segmentation. We formally define this as estimating the dense semantic label probability distribution P(Seg Te, & Ar-st+d) for
3

---

Published as a conference paper at ICLR 2026
Camera Stroim (-10 FPS)
Memory Bank
Cross Atication
leagy
Cross Artention
Cross Attention
Memory Dank
Splatied Fesuure
Fosture Splatted Festure
Manney Atiantion
Menory Altestion
Dupicer
(6) Memory attention
Splatting Modsle
Spaattin Moduls
Sofumax Splating
Softma Splacting
Imagge Festune
1
Memory
M
Setok
Net
Scare Net
Outpui
Flow Estimattr
Flow Falimtation
Event Frcoddrr
Flow Enecdler
Event Fncoderr
Flow Frcceder
I- &t
1-1 at
Event Stream 10 TPS)
M.
Eusene
M
proposed
Splatting module
Figure 2: Overview of LiFR-Seg framework. (a) The overall architecture. (b) The Splatting Module performs uncertainty-guice feature propagation using an event-driven motion field (M) and its learned confidence (S). (Note that E++at is used strictly for training supervision to generate Se)(+ (c) The Memory Attention module refines the propagated feature by integrating historical context for long-term consistency.
target time + 8t. We define At the fixed interval between consecutive LFR frames (e.g., 50ms), and öt E (0, At] as the relative offset to the target timestamp within this interval. This is a challenging spatio-temporal prediction problem: we must infer the dense, per-pixel semantic state (Seg.+5t) conditioned only on single, spatially dense but temporally sparse image observation (Ia) and the temporally dense but spatially sparse stream of intermediate motion cues (E:-ar-c45r).
As illustrated in Fig. 2, our framework decomposes this problem into three core stages, First, to robustly model the scene dynamics, we estimate an Event-Driven Uncertainty-Aware Motion Field Feature Propagation
($3.2). Second, using this motion field, we perform Uncertainty-Guicn to warp the initial deep features to the target timestamp ($3.3). Finally, to ensure Long-Term Consistency, the propagated features are refined using temporal memory module ($3.4).
3.2 EVENT-DRIVEN UNCERTAINTY-AWAR MOTION FIELD
To propagate dense features, we require a dense motion field. We estimate this field from the raw, asynchronous event stream, denoted as E. However, this estimation from sparse data has inherent uncertainty. Therefore, we model the true (but unknown) motion field M probabilistically. Our goal is to estimate not only its mean (the flow vector M) but also its precision (a confidence score S).
First, the raw, asynchronous event stream & within specific time window converted into discrete, grid-based representation, the event voxel E. This is achieved by accumulating the polarity of events into several temporal bins. The value for given pixel u (z. y) and bin index is computed as:
E(u - Pj [u, = u] max (0,
bl),
EgEE
where [-] denotes the Iverson bracket, which is if the condition inside is true and otherwise, Pj is the event polarity (+1 -1- The term f (B-1)(t,-to) is the normalized event timestamp, where AT
B = 4 is the total number of bins, b E (0.... ... B-1) is the specific bin index, AT is the time window, and lo is its start time. These voxels, Er-Ar-et Ex E Bxx are then fed into an event- based optical flow network to predict the conditional mean of the motion field, M, E 2 HxW This network, denoted as Fetow follows a modern RAFT-like architecture Teed & Deng (2020). First, feature encoder (6 is applied once to extract features from each event voxel, which are used to build 4D correlation volume Veorr. Then, starting from an initial estimate M 0, an update operator (Luppatee) iteratively refines the flow for k = O,..., K - steps:
M k+1 Mupdaate ( C(M Veorr))-
4

---

Published as a conference paper at ICLR 2026
where C is the correlation lookup operator. The final Mc-1+5e is the output of the last iteration, M
Next, to estimate the reliability of this prediction, we introduce a ScoreNet (Fig. 2b) that learns confidence map S, which serves the log-precision of the flow distribution. The ScoreNet function, Setore maps the input pair (Et+ M to single-channel log- precision map S E IRI This function is a composition of three main stages. First, separate encoders extract features from the event voxel and motion field: FE = Oevent (E-+ and FM = onow (M, These are fused into joint embedding Fointt Concat( FM) Finally, the ScoreNet processes this embedding to regress the pixel-wise log-precision map S:
St-t++t St Sescoreee (Fjsi))
This log preeiieciioon map S is critical, as it serves key input to our feature propagation module ($3.3), where it will modulate the influence of each flow vector in a manner analogous a weighted likelihood estimation.
3.3 UNCERTAINTY-GUDDE FEATURE PROPAGATION
With the estimated motion field M and its confidence map S, we can now address the core task of temporally propagating semantic features. Our core design choice is to warp the multi-scale features, segmentation backbone. As confirmed by our ablation study
Fr, extracted from the LFR (Table 4), this strategy is superior warping raw images or final segmentation maps.
This propagation is performed using Softmax Splatting Niklaus & Liu (2020). We make this operation uncertainty-guias by incorporating event-guided confidence map S as the log-space importance weight:
Fi+ E(exp(S Fr, Fr,Me+4451
(4)
E(exp(Sr-(+d1), M(-s++6t) This ensures that features warped by unreliable flow vectors (low S) are given less "vote" in the final propagated feature map. To further correct for any residual warping artifacts, we then apply lightweight RefineNet, composed of two sequential convolutional layers, which acts as a learned spatial regularizer to enhance the final feature consistency before decoding.
3.4 LONG-TERM CONSISTENCY VIA TEMPORAL MEMORY
The feature propagation described so far a Markovian process the state at + öt depends only on the state at L. This is insufficient for real-world scenarios involving long temporal gaps complex occlusions, where long-term context is required. To overcome this limitation, we introduce a memory mechanism to integrate this non-Markovian history.
We model this as a recurrent state update. A memory bank M stores features from previous key timestamps. As shown in Fig.2c, after a feature Edeep is generated via warping, it undergoes temporal enhancement step. The propagated feature queries the entire memory bank M via cross-attention, producing an updated feature that is enriched with available historical context. This updated feature is then stored back in M for future use. We apply this mechanism only to the deepest, most semantic feature layer (Frdeep) to effectively balance performance and computational cost, choice validated by our experiments (Table 5) which show its critical role long-interval robustness.
BENCHMARK
We evaluate our framework on four diverse datasets to assess its performance across a range of real- world conditions. For training and primary evaluation, we use the real-world DSEC dataset Gehrig et al. (2021a) along with our newly introduced SHF-DSEC, both of which focus on autonomous driving scenarios. Specifically, SHF-DSEC features higher temporal resolution of 100 Hz, enabling effective demonstration of our method's 's anytime prediction capability. To further validate the robustness of our method across different domains, we include the M3ED dataset Chaney al. (2023), which features sequences captured from drones and quadruped robots. Additionally, test on the DSEC-Night benchmark Xia et al. (2023) to demonstrate the effectiveness of our approach under extreme low-light conditions. Please check the Appendix A for detailed information.
5

---

Published as a conference paper at ICLR 2026
5 EXPERIMENTS
5.1
MAIN RESULTS
RGB
Event Vonal
Yand
RGB
RGB
FloM
Peatere
Fz
Bosker
Paatern
Feus
Backie
-uttrrdaiiaee Casnal)
Fusix (Net Anytine))
LiFi-Sgg
Figure 3: Perception Paradigm Comparison. Visual definition of the four experimental settings: (a) The LFR (Baseline), which is causal but not anytime-capable. (b) Interpolation-basel methods, which are non-causal. (c) The original Event-Image Image Fusion paradigm. (d) Our (LiFR-Seg) framework, which is the only one that is both causal and anytime-capable.
Baseline Paradigm Comparison. We establish our experimental setting by defining the key baseline paradigms, which are visually compared Fig. and quantitatively evaluated in Table 1. We first define two theoretical bounds: the HFR Upper Bound, representing an ideal (but impractical) system with access to the privileged target frame ōt and the LFR (Baseline) (Fig. 3a), naive causal method using only I. which is not anytime-capable and thus suffers from "Perceptual Gap". We then evaluate two competing paradigms. Interpolation- non-causal, as their core design requires the future frame I++At making them incompatible with the causal constraints of our predictive task. The standard Event-Image Fusion paradigm (Fig. 3c) (e.g., CMNeXt Zhang al. (2023)) is causal but not inherently capable it is designed to fuse It with co-located (often past) events (c.g., E,- (t-At)))) to enhance Seg. To create a robust LFR+ + Fusion baseline for our task, we adapted this paradigm by providing it with 7, and the howardd-oooingg event stream Ft-+t+5t- This adapted approach, however, still suffers from the fundamental limitation of direct fusion: struggles to effectively merge spatially dense semantic features (from RGB) with sparse, low-texture motion cues (from events), which can degrade segmentation accuracy. In stark contrast, our LiFR-Seg framework (Fig. 3d) is designed from the ground up be both fully causal and truly anytime-capabi robustly propagating features (rather than fusing them) by leveraging the full available event context (I, and Et-
methods (Fig. 3b) are inherently
Experiment Setup. To rigorously validate our framework, we conducted extensive evaluations on the real-world DSEC and M3ED datasets, our synthetic SHF-DSEC dataset, and the DSEC-Night benchmark. For fair comparison, all methods leverage the same Segformer-B2 backbone and are trained to convergence on their respective datasets, with the exception of the zero-shot DSEC-Night evaluation. We employ the OhemCrossEntropy loss Shrivastava et al. (2016) for end-to-end training to handle class imbalance. Supervision is applied at timestamps t + At, aligned via the second warping strategy (details in Appendix C.I).
Quantitative Comparison. The quantitative results, presented in Table 1, confirm the limitations of these baseline paradigms and reveal how our method successfully bridges the perceptual gap. On the standard DSEC dataset, our approach's 's efficacy is highlighted by its proximity to ideal performance; 73.82% % mloU, closes the performance gap the HFR Upper Bound (73.91% (1%) to a mere 0.09 % despite having no access to the target RGB frame. This trend of near-HFR performance continues on SHF-DSEC and the M3ED-Quadruper dataset. The advantage of our propagation mechanism is particularly pronounced in high-speed scenarios; on M3ED-Drone, our method attains 64.28 % mloU, remarkable improvement of 9.05% % over the LFR (Baseline) (55.23%). Most strikingly, our framework demonstrates unparalleled robustness in extreme low-light conditions. In the zero-shot DSEC-Night test, our approach (41.86% %)) not only functions effectively where the RGB-only HFR Upper Bound collapses (41.83%) but even surpasses it. This pivotal result proves that our event- driven system is not just a substitute for, but can be superior to, HFR systems when traditional vision fails.
Next, we evaluate against the Interpolation-based paradigm (e.g.. TLX + Seg.), which is constrained by two fundamental limitations. Architecturally, it is non-causal, as its core design requires the future frame Lt (Table 1), precluding its use in predictive scenarios. Performance-wine it suffers from mismatch between photometric reconstruction and semantic understanding. These flaws

---

Published as a conference paper at ICLR 2026
Table Comprchensive performance comparison across five diverse benchmarks. Our method the only one satisfying the crucial causal (CS) and anytime (AT) constraints for real-world prediction. not only bridges the perceptual gap by matching HFR performance on standard datasets but also demonstrates superior robustness in high-speed (M3ED) and low-light (DSEC-Night) scenarios. Results are reported at = 50ms (DSEC, SHE, Night) and ót 40ms (M3ED).
Method
Input
AT
DSEC SHF
M3ED-D
M3ED-Q
D-Night
(Ideal)
x
73.91
65.40
64.57
69.27
41.83
LFR (Baseline)
I
x
67.67
61.73
55.23
63.20
37.44
LFR Interpolation
TLX Seg.
Je. 1.. Alt.,.E... E...
x /
68.17
55.89
60.60
62.92
NaN
LFR Fusion
EISNet
Ir, E1-145
/
68.11
61.28
62.98
37.28
CMNeXt
Ic, B2-
/
70.13
61.40
59.56
65.52
39.38
Ours
11, Ei A4-1444
/
73.82
64.80
64.28
68.89
41.86
Imng
Baseline DSEC
TLX+5eg.
CMNeXt
Img
TLX-Seg.
SHF-DSBC
(9 M3ED-Drene
(d) MEED-Quadraped
Figure 4: Qualitative comparison of anytime interframe segmentation. The top row establishes the visual context, displaying the input RGB frame time f, the event stream from to t ôt, and the target Ground Truth (GT) segmentation at time t + St. The bottom row presents a zoomed-in comparison of the GT against the outputs of all evaluated methods.
evident across our benchmarks. While interpolation offers a modest improvement over the LFR (Baseline) real-world datasets like DSEC and M3ED, it struggles significantly synthetic data. On SHF-DSEC, it performs even worse than the LFR (Baseline) (55.89% vs. 61. 31.73%) despite successful fine-tuning of the interpolation model (PSNR improved from 23.45 26.07). We observe a "PSNR-mloU Paradox": improving photometric quality (26.07 + 27.43 dB) via lower interpolation ratios paradoxically degrades semantic accuracy (55. 595 + 55.03%). This confirms an objective misalignment: reconstruction targets perceptual smoothness, often blurring discriminative boundaries. In contrast, our feature feature-space approach is inherently robust the pixel-level micro-misale microo-msalignments that plague image interpolation. The model's lack of robustness is further underscored on DSEC-Night, where the severe day-to-night domain shift renders the pre-trained interpolation model ineffective, making meaningful comparison inapplicable. This demonstrates that our direct feature propagation a more robust, practical, and causally-sous solution.
Finally, we compare against causal LFR + Fusion baselines (e.g., CMNeXt), which represent an alternative multi-modal approach. While this fusion strategy offers an improvement over the LFR (Baseline) in most real-word world scenarios (Table 1), it still falls significantly short of propagation- based framework. The performance gap is particularly pronounced on the high-dynamic M3ED- Drone dataset, where our method (64.28%) outperforms CMNeXt (9.56%) by substantial margin of
7

---

Published as a conference paper at ICLR 2026
4.72% mloU. We conjecture that this stems from the inherent difficulty of direct fusion: the network must implicitly learn to align dense semantic features with sparse, texture-less event cues. This can be suboptimal, especially in high-mou figh-motion scenes. In contrast, our framework's 's explicit, flow-guided propagation provides a stronger inductive bias for motion, geometrically warping features to maintain semantic consistency over time. This architectural advantage makes approach fundamentally more effective for the anytime segmentation task.
Qualitative Comparison. Qualitatively, Fig. 4 provides compelling visual evidence for our method's superiority. We first observe the LFR (Baseline), which fails to account for object/ego motion during the blind interval, resulting in a clear temporal misalignment or "perceptual gap" where segmented objects are visibly offset from their ground truth locations. This is particularly evident in the highly dynamic M3ED datasets. In contrast, while the LFR Interpolation (TLX+Seg.) method corrects for motion, it often produces blurry and indistinct object boundaries, an artifact of the image interpolation process that struggles to create photorealistic details. The LFR Fusion (CMNeXt) approach suffers from a different issue: by directly fusing sparse event features with dense image features, it can create semantic ambiguity, as seen the M3ED-Quadruped example where object shapes are distorted.
Our method (Ours) overcomes all these limitations. It not only accurately compensates for the temporal gap, but also generates sharp and precise boundaries. This fine-graine accuracy is consis- tently demonstrated across datasets: on DSEC and M3ED, our method successfully delineates the challenging narrow gaps between pedestrians' legs, and SHF-DSEC, it clearly separates the small figure of a person from their motorcycle. This ability to capture intricate detail while maintaining temporal consistency underscores the effectiveness of our explicit feature propagation framework.
Anytime Performance and Robustness to Temporal Gaps. We further analyze the any- time performance of all causal methods by eval- uating their robustness to increasing temporal gaps (8t) on the high-frequency SHF-DSEC
mioli (%)
62
dataset, as visualized in Figure 5. Our frame- work (solid blue line) demonstrates exceptional stability, maintaining a consistently high mloU across the entire 10-100 ms range, which show- cases its true anytime capability. In stark con- trast, the LFR (Baseline) (dark gray dashed line) suffers from a dramatic performance collapse, plummeting from 64.94% 10ms to 58.80% at 100ms. This steep decline visually quantifies the severity of the "perceptual gap" for naive LFR systems. Interestingly, the LFR + Fusion method (light green line) exhibits more nuanced behavior: while starting with a lower mloU than the LFR (Baseline), its degradation is less severe, leading to crossover point at approximately 81 60 ms. This suggests that simple fusion provides some resilience against temporal decay but is ultimately an insufficient and suboptimal solution. Our method is the only approach that remains robustly effective across all intervals, proving the superiority of our explicit propagation mechanism for bridging the blind time interval.
Method
Ours
LFR (Baseline)
CMNeXt
Sk
&t (ms)
Figure 5: Anytime performance SHF-DSEC. Our method (solid blue) remains stable, while base- lines degrade the temporal gap öt increases.
5.2 ABLATION STUDIES
To demonstrate the effectiveness and robustness of our proposed method, we conducted a series of ablation studies on the DSEC dataset. These studies analyze the impact of optical flow accuracy, the choice of warping method, and the contribution of the memory bank.
Robustness of Pretrained Flow. To verify that model does overfit to specific flow supervision, we conducted two gross-domain experiments. First, our method achieves state-of- state-of-the-art the-hartt performance on the unseen M3ED dataset using flow estimator pretrained only on DSEC, demonstrating strong zero-shot transferability. Second, replacing the pset--ret flow network (Prophesee sensors / 640x440) with one trained on the distinct MVSEC Zhu et al. (2018) dataset (DAVIS346) 346x260, small displacements) results in negligible 0.14% mloU drop on DSEC. These results confirm that our framework learns robust motion representations and generalizes well across different domains.
8

---

Published as a conference paper at ICLR 2026
Robustness to Different Flow Estimators. Our framework demonstrates strong robust- ness when paired with different optical flow estimation methods. In Table 2, we com- pare the segmentation performance (mloU%) of our framework when equipped with differ- ent optical flow estimators, including the image- based RAFT Teed & Deng (2020) and sev- eral event-based methods such as bflow Gehrig et al. (2024), IDNet Wu et al. (2024), and E- RAFT Gehrig et al. (2021b). The competing paradigms of interpolation and direct fusion (de-
Table Ablation of different paradigms and their specific implementations PSEC-Semantes *In- dicates inputs adapted for the anytime task.
Method / Paradigm
mloU (%)
LFR Interpolation (Fig. 3b) TLX + Seg.
68.17
LFR Fusion (Fig. 3c*) CMNeXt
70.13
Ours (LFR + Propagation) (Fig. 3d)
w/ RAFT
72.93
tailed $5.1) are significantly outperformed by propagation-ban approach, achieving sub-
w/ bflow
73.38
w/ IDNet (iter 4, 1/8)
73.00
optimal mloU scores of 70.38% and 70.13 10, re-
W/ IDNet (iter 4, 1/4)
73.62
spectively. In contrast, method consistently achieves much higher performance across all tested flow estimators. Notably, our framework demonstrates robustness to lower-quality flow estimates, achieving a strong 73.49% mloU even when using a coarse flow map from E-Raft-Lite version flow estimator generated with only 1/16th resolution. This demonstrates that our method is largely agnostic to the specific choice of flow estimator and does not rely on highly precise flow. This robustness stems from two key design elements: (1) Uncertainty-aware warping down-weights unreliable motion regions, limiting the negative impact of imperfect flow; (2) The temporal memory module provides long-term context to correct local misalignments. Together, these components ensure reliable temporal propagation and stable segmentation performance, even with imperfect flow inputs.
W/ E- E-RAF--Lite (iter 12, 1/16)
73.49
W/ E-RAFT
73,82
Ablation of Uncertainty Map To understand the empirical behavior of the uncertainty-awarr warping mechanism, we visualize the learned Uncertainty Map (S) alongside the input Event Voxel and Estimated Flow in Figure 6. The visualization confirms that the ScoreNet effectively acts reliability filter by measuring the consensus between the two modalities:
Sqaie Sivent
Righ Unevrtauty
Gl Low Uncomsttry
Vald Dvene
Armunae Plrs
Figure Visualization of Uncertainty Map behavior. (a) Sparsity: Flow unsupported by sparse events triggers high uncertainty, suppressing hallucinations. (b) Inaccuracy: Disagreement between event edges and inaccurate flow triggers high uncertainty, filtering errors. (c) Alignment: Consistent flow-event alignment (e.g., pedestrians, riders) yields high confidence for propagation. Table 3: Impact of ScoreNet.
We validate the uncertainty module quantitatively in Ta- ble 3. Incorporating the learned confidence map consis- tently improves performance across all benchmarks (e.g.. +1.08% on DSEC), confirming its effectiveness in filtering unreliable motion cues caused by noise sparsity.
Method
DSEC
SHF
Night
w/o Score
72.74
63.31
41.46
Ours
73.82
64.80
41.86
Ablation on the Warping Domain. To effectively prop- agate information over time, the choice of what data to warp, the warping domain, critical design decision. We conducted an ablation study to compare our proposed feature-level warping against two common alternatives: warping the raw input images (Image Warping) and warping the final segmentation predictions (Segmentation Warping). As detailed in Table 4, our feature warping strategy achieves a state-of-the-art 73.82% mloU. This result significantly surpasses warping at the image level (72.37%) and the prediction level (71.63%). Furthermore, all methods employing explicit motion compensation (warping) show a distinct advantage over the simple interpolation baseline (70.38%). These results provide clear evidence for central hypothesis: propagating rich, deep semantic features through mooiinn-compenseee alignment the most effective strategy for maintaining high-quality, temporally consistent results in anytime segmentation task.

---

Published as a conference paper at ICLR 2026
Table 4: Different warping strategies on
Table The Effectiveness of the Memory Module Over Long Temporal Gaps on DSEC (mloU %)
DSEC (ōt = 50 ms).
Method
mIoU (%)
Method / öt (ms)
50
200
400
800
Image Interpolation
70.38
Lower Bound
67.67
57.06
51.18
45.34
Image Warping
72.37
Ours (w/o Mem)
73.49
72.00
67.02
57.33
Segmentation Warping
71.63
Ours (w/ Mem)
73.82
72.72
68.60
59.55
Feature Warping (Ours) 73.82
Influence of Memory. consistency is systematically evaluated over increasingly long temporal intervals, up to 800 ms, on the DSEC dataset. As shown in Table 5, the performance gap between the model with and without the memory module remains small at a short interval of 50 ms, with mloU improving only slightly from 73.49% to 73.82%. However, the temporal gap increases, the benefits of the memory module become increasingly significant. At 200 ms, the model equipped with memory achieves an mloU of 72.72%, outperforming the counterpart without memory by 0.72 percentage points. This trend continues at 400 ms, where the improvement grows to 1.58 percentage points, demonstrating the module's ability to retain semantic information over time. Most notably, the longest interval of 800 ms, the model with memory reaches an mloU of 59.55%, surpassing both the lower bound by large margin and the memory-fre variant by 2.22 percentage points-nearly eight times the initial gain observed at 50 ms. These results clearly illustrate that the memory module plays a critical role in mitigating feature decay and maintaining robust temporal alignment, especially in challenging scenarios with sparse infreeuent RGB observations. Its contribution is not merely incremental but becomes indispensable as temporal continuity is increasingly disrupted.
The effectiveness of our long-term memory module in preserving temporal
CONCLUSION
In this work, we addressed the critical problem of "perceptual gaps" that plague standard low-frame- rate (LFR) systems in dynamic environments. We introduced and formalized new task, Anytime Interframe Semantic Segmentation, and proposed LiFR-Seg, a novel framework that effectively bridges these gaps. Our approach propagates rich semantic information from a single RGB frame forward in time, guided by an event-driven motion field. The core of our method lies in an uncertainty- aware feature warping mechanism that robustly handles noisy motion estimates, and a temporal memory module that ensures coherence in highly dynamic scenes.
Our extensive experiments provide compelling evidence for the efficacy of this paradigm. We demonstrated that our LFR system achieves performance remarkably comparable to an ideal high- frame-rate (HFR) upper bound, closing the performance gap to less than 0.09% on the DSEC dataset. Furthermore, we validated the extreme robustness of our framework: in highly dynamic M3ED tests, our method closely matches the HFR baseline, while challenging zero-shot test on DSEC-Night, it even surpasses the RGB-based upper bound, proving its viability where traditional cameras fail. We believe this work presents a significant step towards a new paradigm of efficient and reliable perception. The principles of event-guided propagation demonstrated here can be extended to other dense prediction tasks, such as depth or flow estimation. Ultimately, this works showeases a promising path towards decoupling perceptual frequency from sensor hardware limitations, paving the way for more ubiquitous and reliable autonomy.
7 ACKNOWLEDGEMENTS
The work has been supported by Hong Kong Research Grant Council General Research Fund Scheme (Grant No. 17202422, 17212923, 17215025) Theme-based Research (Grant No. T45-701/22- R). Strategic Topics Grant (Grant No. STG3/E-605/25-N, and Voyager Research, Didi chuxing. Part of the described research work is conducted in the JC STEM Lab of Robotics for Soft Materials funded by The Hong Kong Jockey Club Charities Trust. This research was partially conducted by ACCESS AI Chip Center for Emerging Smart Systems, supported by the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government.
10

---

Published as a conference paper at ICLR 2026
8 ETHICS STATEMENT
We confirm adherence to the ICLR Code of Ethics and have carefully evaluated the ethical implications of our research. We present our key considerations below.
1. Applications and Responsible Use
Our work advances perception ability in real scenarios, aiming to improve scene understand- ing and safety in transportation systems. We acknowledge that perception technologies may have applications beyond our intended scope. We encourage the responsible deployment of our methods in accordance with applicable regulations and safety standards for autonomous systems development.
2. Data Handling and Compliance
We utilize established public datasets (DSEC, M3ED) under their respective licensing agreements. Furthermore, we also leverage Carla to generate a synthetic dataset, SHF- DSEC, which only contains a virtual environment and identity. These datasets contain anonymized sequences without personal identifiers. Our research strictly follows the data usage policies established by the dataset providers and does not involve additional data collection or processing of sensitive information.
REPRODUCIBILITY STATEMENT
To ensure reproducibility of our results, have provided comprehensive details necessary to replicate our experiments. The main text outlines our experimental settings in Section C.I, including dataset usage, evaluation metrics, and training configurations. Further implementation specifics documented in Appendix C.I. which covers network architecture details, hyperparameter settings, and the use of software libraries. All experiments are based on publicly available datasets, including the DSEC and the M3ED dataset, and the self-created dataset SHF-DSEC, and use clearly defined data splits and evaluation protocols consistent with prior work. To further support the research community, we commit to releasing our full source code and preprocessed datasets upon acceptance of this paper.
REFERENCES
Manideep Reddy Aliminati, Bharatesh Chakravarthi, Aayush Atul Verma, Arpitsinh Vaghela, Hua Wei, Xuesong Zhou, and Yezhou Yang. Sevd: Synthetic event-based vision dataset for ego and fixed traffic perception. arXiv preprint arXiv:2404. 10540, 2024.
Inigo Alonso and Ana C Murillo. Ev-segnet: Semantic segmentation for event-based cameras. In Proceedings of the IEFE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0 0, 2019.
Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, and Alberto Del Bimbo. Neuromorphic event-based facial expression recogni- tion. In Proceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition, pp. 4109-4119, 2023.
Jonathan Binas, Daniel Neil, Shih-Chii Liu, and Tobi Delbruck. Ddd17: End-to-end davis driving dataset. arXiv preprint 17110:/711.015555 2017.
Kenneth Chaney, Fernando Cladera, Ziyun Wang, Anthony Bisulco, M Ani Hsieh, Christopher Korpela, Vijay Kumar, Camillo Taylor, and Kostas Danilidis. M3ed: Multi-robot, multi-sensor, multi-environment event dataset. In Proceedings of the IEEESCVF Conference on Computer Vision and Pattern Recognition, pp. 4016 4023, 2023.
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Proceedings of the 1st Annual Conference on Robot Learning, pp. 1-16, 2017.
11

---

Published as a conference paper at ICLR 2026
Pier Luigi Dovesi, Matteo Poggi, Lorenzo Andraghetti, Miquel Marti, Hedvig Kjellström, Alessandro Pieropan, and Stefano Mattoccia. Real-time semantic stereo matching. In 2020 IEEE international conference on robotics and automation (ICRA), pp. 10780-10787. IEEE, 2020.
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Danillidis, al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-18)) 2020.
Mathias Gchrig, Willem Aarents, Daniel Gehrig, and Davide Scaramuzza. Dsec: A stereo event camera dataset for driving scenarios. IEEE Robotics and Automaatoo Letters, 6(3):4947-9954,
2021a.
Mathias Gehrig, Mario Millhäusler, Daniel Gehrig, and Davide Scaramuzza. E-raft: Dense optical flow from event cameras. In 2021 International Conference on 3D Vision (3DV), pp. 197-206. IEEE, 2021b.
Mathias Gchrig, Manasi Muglikar, and Davide Scaramuzza. Dense continuous-ting optical flow from event cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7): 4736-4746, 2024.
Yanming Guo, Yu Liu, Theodoros Georgiou, and Michael S Lew. A review semantic segmentation using deep neural networks. International journal of multimedia information retrieval, 7:87-93, 2018.
Daniel Gehrig Javier Hidalgo-Carrio and Davide Scaramuzza. Learning monocular dense depth from events. IEEE International Conference on 3D Vision,(3DV), 2020. URL http::/rpg..fff wwch.ch/dcce 3DV20 Hidalgo.pol pdf.
Diederik Kingma. Adam: A method for stochastic optimization. arXiv preprint arXiv: wrXiv:1412.6980. 2014.
Liulei Li, Tianfei Zhou, Wenguan Wang, Jianwu Li, and Yi Yang. Deep hierarchical semantic segmentation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp.
2022.
Yongrui Ma, Shi Guo. Yutian Chen, Tianfan Xue, and Jinwei Gu. Timelens-xl xl: Real-time event-based video frame interpolation with large motion. In European Conference on Computer Vision, pp. 178- 194. Springer, 2024.
Yujian Mo, Yan Wu, Xinneng Yang, Feilin Liu, and Yujun Liao. Review the state-of-the-art technolo- gies of semantic segmentation based on deep learning. Neurocomputing, 493:626 646, 2022.
Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 5437-5446, 2020.
Nikhila Ravi, Valentin Gabeur, Yuan- Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichtenhofer. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.0077 00714, 2024. URL https: /ttp://aavvvv///aa//eaeeeeaeaawa org/ alos///////////////////////// 2408 00714.
Abhinav Shrivastava, Abhinav Kumar Gupta, and Ross B. Girshick. Training region-based object detectors with online hard example mining. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 761-769, 2016. https:/ //tp//apppp./// semanticschot
org/Corpus TD: 2843566.
Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pp. 402-419. Springer, 2020.
Jichiang Tsai, Che-Cheng Chang, and Tzu Li. Autonomous driving control based on the technique of semantic segmentation. Sensors, 23(2):895, 2023.
12

---

Published as a conference paper at ICLR 2026
Zhexiong Wan, Yuchao Dai, and Yuxin Mao. Learning dense and continuous optical flow from an event camera. IEEE Transactions on Image Processing, 31:7237-7251, 2022.
Ziming Wang, Ziling Wang, Huaning Li, Lang Qin, Runhao Jiang, De Ma, and Huajin Tang. Eas-snn: End-to-end adaptive sampling and representation for event-based detection with recurrent spiking neural networks. arXiv preprint 2rXiv:2403.12555 2024.
Yilun Wu, Federico Paredes-Vallés, and Guido CHE De Croon. Lightweight event-based optical flow estimation via iterative deblurring. In 2024 IEEE International Conference on Robotics and Automation (ICRA), pp. 14708-14715, IEEE, 2024.
Ruihao Xia, Chaoqiang Zhao, Meng Zheng, Ziyan Wu, Qiyu Sun, and Yang Tang. Cmda: Cross- modality domain adaptation for nighttime semantic segmentation. In Proceedings of the IEEF/CVF International Conference Computer Vision, pp. 21572-21585 2023.
Bochen Xie, Yongjian Deng, Zhanpeng Shao, and Youfu Li. Eisnet: multi-modal fusion network for semantic segmentation with events and images. IEEE Transactions on Multimedia, 2024.
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances neural information processing systems, 34:12077- 2021.
Changqian Yu, Jingbo Wang. Chao Peng, Changxin Gao, Gang Yu, and Nong Sang. Bisenet: Bilateral segmentation network for real-time semantic segmentation. In Proceedings of the European conference on computer vision (ECCV), pp. 325-341, 2018.
Jiaming Zhang, Ruiping Liu, Hao Shi, Kailun Yang. Simon Reiß, Kunyu Peng, Haodong Fu, Kaiwei Wang, and Rainer Stiefelhagen. Delivering arbitrary-modd modal semantic segmentation. In Proceedings of the IEFF/CVF Conference on Computer Vision and Pattern Recognition, pp. 1147, 2023.
Alex Zihao Zhu, Dinesh Thakur, Tolga Özaslan, Bernd Pfrommer, Vijay Kumar, and Kostas Daniilidis. The multivehicle stereo event camera dataset: An event camera dataset for 3d perception. IEEE Robotics and Automation Letters, 203) 2018.
Alex Zihao Zhu, Ziyun Wang, Kaung Khant, and Kostas Danilidis. Eventgan: Leveraging large scale image datasets for event cameras. In 2021 IEEE international conference on computational photography (ICCP), pp. 1-11. IEEE, 2021.
Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei. Deep feature flow for video recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2349-2358, 2017.
13

---

Published as a conference paper at ICLR 2026
A BENCHMARK INFORMATION
Here the detailed information on each dataset.
The real-world DSEC dataset Gehrig et al. (2021a) provides 11 classes of segmentation pseudo-labels at a low frequency of 20 Hz, alongside a high-resolution event stream. This restricts our primary evaluation to temporal gap of 8t 50 ms.
DSEC.
M3ED. The M3ED dataset Chaney et al. (2023) is used evaluate robustness to diverse dynamics, providing RGB frames at a 25 Hz frequency. We specifically test on its challenging Drone and Quadruped splits, which feature rapid ego-motion.
DSEC-Nighh. The DSEC-Night benchmark Xia al. (2023) is an evaluation-only set of 150 manually annotated nighttime labels. It serves as a rigorous zero-shot test for generalization to extremely low-light conditions.
SHF-DSEC. To overcome the 20 Hz limitation of DSEC for evaluating anytime performance, we introduce our synthetic SHF-DSEC dataset, built with the CARLA simulator Dosovitskiy et al. (2017). This dataset provides dense, ground-truth segmentation maps for 11 classes, synchronized with RGB frames and event streams, all at a high frequency of 100 Hz. The training set (1,260 samples) is drawn from CARLA towns 01-05, while the evaluate domain generalization. Event streams are simulated based on logarithmic intensity changes. The 100 Hz ground truth is crucial, as allows us to rigorously evaluate our model's "anytime" capability at much finer temporal intervals, such as òt = 10 ms.
(180 samples) uses town 10 to
B DATA GENERATION IN SHF-DSEC
The SHF-DSEC dataset is sampled at 10 ms intervals, comprising total of 16,200 samples per sequence. It integrates data from three synchronized sensors: an RGB camera, an event camera based on the configuration by Hidalgo et al. Javier Hidalgo-Carrio & Scaramuzza (2020), and segmentation camera adhering to the DSEC setup by Gehrig et al. Gehrig et al. (2021a). All sensors operate resolution of 480 640 with a field of view of 57.5%, ensuring consistent and high-quality inputs for dynamic scene segmentation tasks. As detailed Table 6, the dataset is specifically designed to enhance segmentation variety and robustness for event-based methods, featuring six distinct simulated environments generated using the CARLA simulator. These environments reflect diverse urban and natural settings, with vehicle and pedestrian configurations inspired by Aliminati al. Aliminati et al. (2024).
The SHF-DSEC dataset synthesized within the CARLA simulator, rendering high-fidelity scene frames under varying lighting and motion conditions. To faithfully emulate real sensors, we configured the simulation with a rigorous rather than linear interpolation, accurately captures rapid aliasing. An event
fixed time-step (1000 Hz). This high-frequency physical sampling,
dynamics without temporal (a. y, t. pol) triggered at pixel (ar, y) and timestamp when the change in logarithmic intensity L.xx. y, i) exceeds predefined threshold. Specifically, event occurs if [T<x,, y, t) L(x,, y, /. ōt)l = pol C, where C 0.3 the contrast threshold, denotes the time elapsed since the last event that pixel, and pol (+1,-1) represents the polarity indicating brightness increase or decrease, respectively. This mechanism generates a realistic event stream that effectively captures dynamic scene changes.
The dataset encompasses 11 annotation classes for segmentation: background, building, fence, person, pole, road, sidewalk, vegetation, car, road lines, and traffic sign. Notably, the "wall" class from the original DSEC dataset was replaced with "road lines" due to the visual similarity between simulated walls and buildings in CARLA, which poses challenges for accurate differentiation.
14

---

Published as a conference paper at ICLR 2026
Table SHF-DSEC Dataset Structure
Map
Description
Sequences
Usage
Town01
Small town featuring a river and bridges Small town with a mix of residential and commercial buildings
Training Training
Town02
2
Town03
Larger urban setting with roundabout and multiple junctions
2
Training
Town04
Mountainous town with infinite highway Grid-based town with cross-junctions, a bridge, and multi-lane directions Downtown area with skyscrapers, residen- tial buildings, and ocean promenade
Training Training
Town05
TownIOHD_Op Opt
1
Testing
C EXPERIMENTAL SETUP
C.I TRAINING AND IMPLEMENTATION DETAILS
Training Strategy. Our entire framework is trained end-to-end using the OhemCrossEntropy loss Shrivastava et al. (2016) to mitigate class imbalance. key aspect of training is that supervision is applied only at the discrete RGB frame timestamps t At, where ground truth Seg, + ar available. To achieve this, the feature Fi+5t. which has been propagated an intermediate time (typically the midpoint, 51 = Al/2), is warped second time to Fitar before being passed to the final segmentation decoder. Specifically, this second warp utilizes new motion field M which estimated by feeding event slice Ex and E++6t-+4+ the flow estimator. This ensures that the entire propagation chain is differentiable and aligned with the available supervision.
Hyperparameters. We use the AdamW optimizer Kingma (2014) with a learning rate of 1e and weight decay of 5e 5e- 33 A polynomial decay schedule is employed for the learning rate, with 10-epoch warm-up phase followed by decay with a power of 0.95. All models were trained on two NVIDIA RTX 4090 GPUs for 200 epochs until convergence, using total batch size of 4.
Anytime Inference. At test time, our framework's "anytime" capability is realized. By providing the relevant event slice At for any target time öt, our model can compute the corresponding motion field and propagate features on the fly. This enables dense segmentation at arbitrary temporal resolutions without any modification or retraining of the model.
C.2 EXPERIMENTAL SETUP
To rigorously validate framework, conducted extensive evaluations the real-world DSEC and M3ED datasets, our synthetic SHF-DSEC dataset, and the DSEC-Night benchmark. For fair comparison, all methods leverage the same Segformer-B2 backbone and are trained to convergence on their respective datasets, with the exception of the zero-shot DSEC-Night evaluation. We established four categories of external baselines: the HFR Upper Bound (an ideal system with I++5t), the LFR Lower Bound (a naive approach using only I,), Interpolation Baselines (e.g.. TLX + Segformer), and Multi-Modal Fusion Baselines (e.g.. CMNeXt).
Our framework employs the SegFormer model with the MiT-B2 backbone Xie et al. (2021), which utilizes a hierarchical Transformer encoder and lightweight ML.P decoder to generate dense semantic predictions. The model is initialized with weights pre-trained on ImageNet and then fine-tuned on the DSEC and SHF-DSEC datasets. To ensure fair and consistent comparison, all baseline methods, including, TimeLens-XL Ma et al. (2024), EISNet Xie et al. (2024), and CMNeXt Zhang et al. (2023), were also re- re-trained from scratch on both datasets until convergence. We made a specific adaptation for TimeLens- XL due its architectural constraints, which require input dimensions to be multiples of 32. For this baseline, we applied center cropping to our standard 440 640 input, resulting in a resolution of 384 x 608. Accordingly, its evaluation performed by comparing predictions against
15

---

Published as a conference paper at ICLR 2026
ground truth segmentation maps cropped to the same resolution, ensuring unbiased assessment across all methods.
D IMPRACTICALITY OF THE RGB-BASI HFR SYSTEM
Table 7: Event camera vs. high-speed RGB camera.
Event Camera
High-Speed RGB
Camera Type
Prophesee EVK4 HD
Phantom MTX-7510
Resolution
1280x720
1280x640
Max FPS
>
94K
Price (USD)
$5K
$150K
Power (W)
1.5
>325
Dynamic Range (dB)
>12
51
We believe that our proposed problem and solution hold high practical value. Below, we compare our approach with the combination of a high-speed RGB camera and SegFormer terms of hardware overhead.
As shown Table 7, event cameras feature extremely high temporal resolution (Prophesee, 1280x720, > >M FPS) at low price (S5KUSD), small power consumption (1.5W), and large dynamic range (> 120db.. In comparison, a high-speed RGB camera (Phantom MTX-7510, 1280x640, 94K FPS) has high price ($150kUSD), large power consumption (>325W), and small dynamic range (51db).
E COMPUTATIONAL EFFICIENCY ANALYSIS
We analyze the computational efficiency LiFR-Seg on an NVIDIA RTX 3090 at 440 x 640 resolution. The core efficiency of our paradigm stems from amortization: the heavy Image Encoder executed only once per keyframe (I,), while subsequent predictions (t + 8t) utilize lightweight propagation modules. Theoretically, for N propagated frames, the cost saving is proportional to N (Cencoder- moodules). In our practical implementation, we utilize a lightweight variant (LiFR- Seg-Lite) with RAFF-small backbone and 1/16 flow resolution, which maintains competitive 73.49 mloU (vs. 73.82 mloU for the full version) while ensuring minimal computational overhead.
Table 8: Computational cost comparison RTX 3090. LiFR-Seg-Liie achieves the lowest amortized FLOPs while maintaining real-time speeds.
Avg Cost (N = 1)
Avg Cost (N = 10)
Method
Params (M)
GFLOPs
FPS
GFLOPs
FPS
HFR Upper Bound
25.8
42.04
72.8
42.04
72.8
LFR + Fusion (EISNet)
34.5
72.73
34.7
72.73
34.7
LFR + Fusion (CMNeXt)
58.7
68.12
29.1
68.12
29.1
LFR + Interp. (TLX)
33.2
200.77
29.5
248.51
26.7
Ours (LiFR-Seg-LLtte)
30.7
40.43
65.6
38.89
60.3
As shown in Table 8, our amortized cost (40.43 GFLOPs) is lower than the HPR baseline (42.04 GFLOPs) and significantly outperforms fusion (>68 GFLOPs) and interpolation (>200 GFLOPs) methods. Although our latency (65.6 FPS) is slightly higher than HFR due to the memory-bandwiddh bottleneck of correlation lookups, it remains well within real-time requirements. Furthermore, this memory-bound characteristic suggests strong scaling potential modern banddahhdwithh hardware (e.g., A100 or RTX 40-series), highlighting its viability for high-speed deployment in autonomous systems.
16

---

Published as a conference paper at ICLR 2026
F LIMITATIONS
Despite these promising results, our current approach has several limitations. While LiFR-Seg proves robust high-speed ego-motion (as seen on M3ED), our current evaluation includes limited datasets featuring high-speed object dynamics (e.g., extreme localized motion blur, or highly non-linear motion). Challenges such as complex non-linear deformations (e.g., sports) or severe source-frame motion blur represent a valuable frontier for future propagation-base research. To overcome these challenges, our immediate next step will involve constructing new real-world and synthetic datasets explicitly designed to incorporate high-speed object motion scenarios. Developing these datasets will enable comprehensive validation of our approach under more demanding and realistic conditions, expanding its applicability to critical domains such autonomous driving. sports analyties, and drone navigation. Furthermore, integrating our anytime segmentation framework with advanced, specialized hardware platforms optimized for event-driven computation could significantly enhance real-time processing efficiency. This combined hardware-softwar co-design would be particularly beneficial for resource-constrained edge devices, enabling robust, high- segmentation in practical, real weal-world scenarios.
G BROADER IMPACTS
To enhance our model, we plan to adapt it for streaming inputs in an online fashion. By utilizing the optical flow obtained from the previous time step initialization for the next time step's flow estimation, we can further improve computational efficiency. Additionally, we aim to extend our research into the broader domain of video-based dynamic segmentation. Video segmentation introduces challenges such as variable frame rates, diverse lighting conditions, and persistent occlu- sions. We are confident that our framework can be enhanced address these complexities, thereby expanding its real-world applications and significantly advancing the state-of-the-art in dynamic semantic segmentation.
H DECLARATION OF LLM USAGE
Large Language Models (LLMs) were used to assist in language editing, grammar refinement, and improving the overall clarity and readability of the manuscript. However, all scientific ideas, methodologies, experimental designs, data analysis, and conclusions presented in this work are entirely the product of the authors" independent research and intellectual effort. The authors have carefully reviewed, revised, and approved all content and take full responsibility for the accuracy, integrity, and authenticity of the work.
17


Reviews:
[{"review_id": "iXBVbpnVzg", "reviewer": "Reviewer_AC8G", "summary": "This paper presents LiFR-Seg, a framework for Anytime Interframe Semantic Segmentation, where the objective is to predict dense semantic maps at arbitrary timestamps using only a past RGB frame and a stream of asynchronous event data. The proposed approach estimates an event-driven uncertainty-aware motion field, propagates features through uncertainty-guided Softmax Splatting, and ensures temporal coherence via a temporal memory attention module. The authors also introduce SHF-DSEC, a synthetic benchmark built on CARLA that enables fine-grained evaluation of anytime performance at 100 Hz. Extensive experiments across four datasets (DSEC, SHF-DSEC, M3ED, and DSEC-Night) show that LiFR-Seg achieves performance nearly identical to a high-frame-rate (HFR) upper bound and maintains robustness in highly dynamic and low-light scenarios", "questions": "- Could you clarify how the temporal memory bank is updated over time (e.g., recurrently after each $\\delta t$ or at selected keyframes)?\n\n- How is the uncertainty map trained, does it rely solely on end-to-end supervision, or is any auxiliary loss used?\n\n- What is the computational cost (runtime, memory usage, or FLOPs) compared to fusion-based baselines such as CMNeXt or EISNet?\n\n- Has the SHF-DSEC dataset been validated for realism or bias? How well does a model trained on SHF-DSEC transfer to real DSEC data?\n\n- How sensitive is the system to event noise or missing events, and does the uncertainty weighting mitigate this effect?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper tackles a well-defined and practically important problem at the interface of event-based sensing and dense semantic segmentation. The authors clearly articulate the “Anytime Interframe Segmentation” task, distinguishing it from standard video propagation or multi-modal fusion. The method is technically solid, combining event-driven motion estimation, uncertainty-weighted feature propagation, and temporal memory in a cohesive framework that respects causal and anytime constraints.", "weaknesses": "While the overall contribution is convincing, the conceptual novelty is limited. LiFR-Seg builds on well-known components, RAFT-style optical flow, Softmax Splatting, and memory-based refinement, and integrates them effectively rather than introducing a fundamentally new algorithmic idea. The contribution is thus primarily at the systems and task-definition level.\n\nSome implementation details remain underspecified. The design and update mechanism of the temporal memory module are described conceptually but not concretely, leaving ambiguity about how features are written to or retrieved from memory. The uncertainty map, though central to the proposed “uncertainty-aware” warping, is not visualized or quantitatively analyzed, so its empirical behavior remains unclear. In addition, while the new SHF-DSEC dataset is useful, the paper does not analyze its realism or demonstrate transfer from synthetic to real data. Finally, runtime and computational cost comparisons are missing despite claims of efficiency, which limits the evaluation of practical deployment potential.\n\nDespite these issues, the paper is technically sound, and empirically strong. The combination of causal design, uncertainty modeling, and temporal coherence results in a high-quality, well-rounded study that advances event-driven semantic segmentation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9mPY47m9PX", "reviewer": "Reviewer_c5rU", "summary": "The authors propose a new task, Anytime Interframe Semantic Segmentation, which offers a practical, causal, and predictive formulation for real-world autonomous systems. Their approach centers on propagating deep features rather than images or segmentation maps, a strategy that is empirically validated (Table 3). The uncertainty-guided propagation mechanism (Eq. 4) effectively handles the noise and sparsity inherent in event-based motion. Quantitative results closely match the high-frame-rate upper bound on DSEC (0.09% mIoU gap) and surpass it on DSEC-Night. Evaluation across multiple real and synthetic datasets, baselines, and ablations, particularly in warping, flow robustness, and memory, demonstrates the method’s efficacy and supports its design rationale.", "questions": "Could the authors please provide a detailed comparison of the computational cost (e.g., FLOPs and inference latency in ms) between: (a) the proposed LiFR-Seg, (b) the HFR Upper Bound (SegFormer-B2), and (c) the LFR + Interpolation baseline (TLX + SegFormer)? This is crucial for understanding the true trade-offs of the proposed \"efficient paradigm.\"\n\nIn Appendix C.1, the training strategy mentions that $F_{t+\\delta t}$ is warped a second time to $F_{t+\\Delta t}$ to be compared with the ground truth $Seg_{t+\\Delta t}$. How is this second warp performed? Is a new motion field $M_{t+\\delta t \\rightarrow t+\\Delta t}$ estimated? If so, how, and what are its inputs?\n\nCould the authors please clarify the statement in Appendix E regarding the \"lack of evaluation on datasets featuring high-speed dynamics\"? How does this reconcile with the use of the M3ED dataset, which is presented as high-speed? What specific \"high-speed\" phenomena are not covered?\n\nThe paper convincingly demonstrates that feature-warping is superior to image-warping/interpolation. Why, fundamentally, does the (reconstruction $\\rightarrow$ segmentation) pipeline fail? Is it because the optimization objective of image reconstruction (e.S., PSNR) is misaligned with the goal of semantic-level consistency, even if the reconstruction looks plausible to a human?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The LiFR-Seg framework presents a practical, causal, and predictive formulation for real-world autonomous systems. Its core idea, propagating deep features instead of images or segmentation maps, is empirically validated as shown in Table 3. The uncertainty-guided propagation, described in Equation 4, effectively addresses the inherent noise and sparsity of event-based motion. Quantitative results closely match the high-frame-rate upper bound on DSEC with a 0.09 percent mIoU gap and exceed it on DSEC-Night. Evaluation across multiple real and synthetic datasets, baselines, and ablations, particularly in the areas of warping, flow robustness, and memory, demonstrates the method’s efficacy and supports its design choices. The new SHF-DSEC dataset, which includes 100Hz ground-truth segmentation, although tailored to this setting, may also serve as a useful resource for future research in event-based semantic segmentation.", "weaknesses": "The paper argues for being an \"efficient paradigm\" (Abstract, Appendix D), but this argument is based entirely on hardware (cost, power, bandwidth). It provides no analysis of computational cost (e.g., FLOPs, inference latency). The proposed LiFR-Seg framework involves running a feature encoder, a flow network, a ScoreNet, a splatting operation, and a temporal memory module. This is almost certainly more computationally expensive than the HFR baseline (which just runs a SegFormer). This is a critical trade-off for practical deployment that is not discussed. \"Efficiency\" in terms of hardware is traded for what appears to be higher computational load.\n\nThe limitations section (Appendix E) states that a weakness is the \"lack of evaluation on datasets featuring high-speed dynamics.\" This is confusing, as the M3ED dataset is repeatedly described in the main paper as featuring \"high-speed scenarios\" and \"highly dynamic\" motion. The authors should clarify this apparent contradiction.\n\nThe paper correctly ablates against \"Image Warping\" and \"Interpolation\" (TLX+Seg) and shows they are inferior. However, the reason for their failure (especially on SHF-DSEC) is only \"conjectured\" to be \"interpolation artifacts\" (Sec 5.1).  A deeper analysis would be valuable. Is the problem that reconstruction models are optimized for perceptual losses (PSNR/LPIPS) which are not aligned with downstream semantic consistency? This is a key point that could be strengthened.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8zCmpoR7z8", "reviewer": "Reviewer_PFAT", "summary": "This paper presents \"LiFR-Seg,\" a novel framework for Anytime Interframe Semantic Segmentation, addressing the perceptual gaps in low-frame-rate (LFR) camera systems by predicting dense semantic maps at any time $ t + \\delta t $ using a single past RGB frame $ I_t $ and an event stream $ E_{t-\\Delta t \\rightarrow t+\\delta t} $. The method introduces an uncertainty-aware motion field estimated via a RAFT-like FlowNet and ScoreNet, uncertainty-guided feature propagation with softmax splatting, and a temporal memory attention module for consistency.\n\nEvaluated on the DSEC dataset, SHF-DSEC (a new 100 Hz synthetic benchmark), M3ED, and DSEC-Night, the experiments aim to demonstrate LiFR-Seg's robustness and effectiveness across diverse scenarios, including standard driving conditions, high-frequency synthetic environments, high-dynamic motion, and low-light settings. The paper contrasts its causal, anytime-capable design with non-causal interpolation and non-anytime fusion baselines (e.g., CMNeXt), highlighting its ability to bridge perceptual gaps inherent in LFR systems, though it lacks efficiency metrics critical for real-time applications like autonomous driving.", "questions": "1. What is the design rationale behind the multiple modules in LiFR-Seg (e.g., uncertainty-aware motion field, guided propagation, and temporal memory), and how do their outputs complement each other? \n2. Additionally, is the fusion of event data (sparse motion cues) and RGB data (dense semantics) sufficiently effective, or could alternative fusion strategies (e.g., earlier joint embedding) improve performance in sparse-event scenarios?\n3. How does the framework mitigate the impact of noise and sparsity in event data during fusion", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper introduces a novel task, \"Anytime Interframe Semantic Segmentation,\" which addresses the critical \"perceptual gap\" in low-frame-rate (LFR) systems by enabling dense semantic segmentation at any arbitrary time\n2. LiFR-Seg proposes a robust framework that leverages the complementary strengths of RGB cameras (dense semantic context) and event cameras (high-temporal-resolution motion cues). The method’s core components—uncertainty-aware motion field estimation (Section 3.2), uncertainty-guided feature propagation (Section 3.3), and temporal memory for long-term consistency (Section 3.4)—are technically sophisticated and well-integrated, as depicted in Figure 2.\n3. Unlike non-causal interpolation methods or non-anytime fusion approaches, LiFR-Seg is explicitly designed to be both causal (relying only on past data) and capable of anytime prediction, as emphasized in Section 3.1 and Figure 3.\n4. The introduction of the SHF-DSEC synthetic dataset with 100 Hz temporal resolution (Section 4) provides a valuable resource for high-frequency evaluation", "weaknesses": "1. The Eq.(3) employs a compact function composition that obscures the specific roles and data flow between $\\phi_{\\text{joint}}$, $F_{\\text{SED}}$, and $\\phi_{\\text{out}}$. A stepwise breakdown would improve readability, and its correspondence to components in Figure 2(b).\n2. The author claims that the pipeline is causal, however, the $E_{t+\\delta t \\rightarrow t+\\Delta t}$ is input the 'Flow estimator' in Fig.2, making it confusing.\n3. The experiments lack detailed ablations for all proposed modules, such as the uncertainty-aware motion field and ScoreNet, with no direct comparison (e.g., with vs. without confidence modulation).\n4. Critical implementation details, such as the use of OhemCrossEntropy loss for training (Appendix C.1), are relegated to appendices rather than the main text, which hinders quick understanding of the optimization strategy and reproducibility.\n5. The paper does not provide a direct comparison of the LiFR-Seg model’s computational efficiency (e.g., inference time, frames per second [FPS], or resource usage) with other settings or baseline models. The real-time processing is important for the application like atonomous driving.\n6. As acknowledged in Appendix E, the benchmarks lack datasets with extreme high-speed dynamics (e.g., severe motion blur or frequent occlusions), which is a crucial application scenarios for usage of event camera.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "27OwNfAYlw", "reviewer": "Reviewer_xGY9", "summary": "The paper proposes a novel framework for Anytime Semantic Segmentation to address the challenges of high-temporal-resolution segmentation in dynamic environments by integrating RGB images and event data. Leveraging the complementary strengths of RGB frames (spatially dense but temporally sparse) and event streams (temporally dense but spatially sparse), their method proposes uncertainty-aware optical flow estimation, learnable feature warping, and a memory mechanism to align and enhance features for segmentation at arbitrary time points. The framework is evaluated on the DSEC and a newly introduced synthetic dataset, SHF-DSEC, demonstrating better performance and robustness. However, they didn't elaborate on training time and how fast their model run compared to SOTA methods.", "questions": "The optical flow estimator, E-Raft, is pretrained on DSEC and SHF-DSEC using their corresponding ground truth optical flow. This setup may lead to unfair evaluation because the model could overfit to the ground truth flow of the corresponding dataset. It is unclear whether the trained model can generalize to unseen data.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "•\tThe design of the framework is relatively well explained, incorporating uncertainty-aware optical flow estimation, learnable feature warping to achieve precise feature alignment across modalities\n•\tThe proposed synthetic dataset (SHF-DSEC) is a valuable contribution to the field for it provides a high-temporal-resolution segmentation dataset", "weaknesses": "•\tDidn’t elaborate on training time and number of parameters of the proposed model against baselines they compare with\n•\tDid they explain how they generate event streams in SHF-DSEC\n•\tNo report on how fast their model runs compared to other schemes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)