Segment Any Events with Language
Authors: Seungjun Lee
Gim Hee Lee
Venue: ICLR 2026 Poster (2026)
Keywords: event sensor
event-based scene understanding
open-vocabulary
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce SEAL, the first Semantic-aware Segment Any Events framework that addresses Open-Vocabulary Event Instance Segmentation.

Abstract: Scene understanding with free-form language has been widely explored within diverse modalities such as images, point clouds, and LiDAR. However, related studies on event sensors are scarce or narrowly centered on semantic-level understanding. We introduce **SEAL**, the first Semantic-aware Segment Any Events framework that addresses Open-Vocabulary Event Instance Segmentation (OV-EIS). Given the visual prompt, our model presents a unified framework to support both event segmentation and open-vocabulary mask classification at multiple levels of granularity, including instance-level and part-level. To enable thorough evaluation on OV-EIS, we curate four benchmarks that cover *label granularity* from coarse to fine class configurations and *semantic granularity* from instance-level to part-level understanding. Extensive experiments show that our SEAL largely outperforms proposed baselines in terms of performance and inference speed with a parameter-efficient architecture. In the Appendix, we further present a simple variant of our SEAL achieving generic spatiotemporal OV-EIS that does not require any visual prompts from users in the inference. The code will be publicly available.

Published as a conference paper at ICLR 2026
SEGMENT ANY EVENTS WITH LANGUAGE
Seungjun Lee, Gim Hee Lee
Department of Computer Science, National University of Singapore seungjun. https: //aandonn github. io/ SEAL
weepjunnnneeeeee lee@u. nus. .us. gimhee. eee@ nus. edu. sg
Streams
OVSAM
SEAL (Ours)
Object, Sentence...
Effective Efficient
SEAL (Ouns)
light
Hybrid
Wrony!
Connor
CS
AF-DA
Over Exposure
AR-CDG
CSs Pied "Sawiing
CK Pisd Tiee
Piompt: Y1 Frmanss deiak
Time (ms)
(a) RGB vs Event
(b) SEAL
(c) Comparison
Figure 1: a) Image-based models are vulnerable to severe image degradation, while event-based models remain robust by leveraging event inputs. b) Our SEAL effectively recognizes both part- and object-level instances on both noun- and sentence-level text queries, e) Our SEAL outperforms existing methods in performance and inference speed with paremeter-efficient architecture.
ABSTRACT
Scene understanding with free-form language has been widely explored within diverse modalities such as images, point clouds, and LiDAR. However, related studies on event sensors are scarce or narrowly centered on semantic-level un- derstanding. We introduce SEAL, the first Semantic-aware Segment Any Events framework that addresses veen-Voabbllayy Event Instance Segmentation (OV-EIS). Given the visual prompt, our model presents a unified framework to support both event segmentation and open-vocabulary mask classification at multiple levels granularity, including instance-level and part-level. To enable thorough evaluation on OV-EIS, we curate four benchmarks that cover label granularity from coarse to fine class configurations and semantic granularity from instance-level to part-level understanding. Extensive experiments show that our SEAL largely outperforms proposed baselines in terms of performance and inference speed with a parameter- efficient architecture, In the Appendix, we further present simple variant of our SEAL achieving generic spatiotemporal OV-EIS that does not require any visual prompts from users in the inference. The code will be publicly available.
INTRODUCTION
Event cameras, also known bio-inspired vision sensors, differ fundamentally from conventional frame-hased cameras by offering compelling advantages including unparalleled temporal resolu- tion (Rebecq al., 2019; Scheerlinck al., 2020), ultra-low latency (Dimitrova al., 2020; Lee & Lee, 2025), wide dynamic range (Gallego et al., 2020; Messikommer et al., 2022b; Schiopu & Bilcu, 2023) and commendable energy efficient (Ramesh et al., 2020). As illustrated in Fig. la, these advantages can mitigate false predictions of image-based models by addressing the inability of Trame-based cameras capture meaningful information in challenging conditions such as low light or overexposure.
Aforementioned competency of event cameras has motivated community to explore robust scene- understanding with events. Especially, event-based segmentation has become key task in event vision (Alonso & Murillo, 2019; Biswas et al., 2024; Hamaguchi et al., 2023; Sun et al., 2022), where the typical challenges of image segmentation (Chen et al., 2018; 2017; Cordts et al., 2016; He et al., 2016; Long et al., 2015) are combined with additional complexities arising from the unique character- istics of event streams (Alonso & Murillo, 2019). Earlier works Event Semantic Segmentation (ESS) has achieved significant success by using either densely annotated event labels (Alonso & Murillo, 2019; Sun al., 2022) employing unsupervised domain adaptation, where image-domain

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Published as a conference paper at ICLR 2026
knowledge is transferred to event data without using the dense event annotations (Gehrig et al., 2020; Sun et al., 2022; Messikommer et al., 2022a). Despite their effectiveness, they show two limitations: 1) Their inference is limited to closed-set vocabulary which restricts their real-world application. 2) They cannot perform instance-level recognition since they focus on semantic segmentation.
Recently, several works have sought to overcome these limitations by distilling the feature space of large image foundation models (Radford et al., 2021; Kirillov et al., 2023) into event- models or by proposing the new benchmarks for instance-level event segmentation (Hamann et al., 2024; 2025). Kong et al. (2024) proposes a novel framework for Open- Vocabulary ESS (OV-ESS) by transferring knowledge from CLIP (Radford et al.. 2021) to an event backbone. However, they are limited to semantic segmentation and thus struggle to distinguish individual object instances. Chen et al. (2024) proposes a Segment Anything model for the event modality that enables instance-level event segmentation. Nonetheless, they lack the ability to recognize the semantics of events since their focus remains on class-agnostic segmentation. Hamann et al. (2024; 2025) proposes novel benchmark for spatiotemporal and instance-level segmentation for events. However, their dataset is restricted to a single class label, which falls short of the real-world requirement to recognize diverse semantics.
In this paper, we propose SEAL: Segment any Events with Any Language to circumvent the aforementioned issues by addressing the Open- Vocabulary Event Instance Segmentation (OV-EIS) task. There three main objectives of our SEAL: 1) Recognize event instances across multiple levels of semantic granularity that include both part-level and object-level proposals. 2) Support free- form language queries that include but are not limited to noun-level and sentence-level expressions shown in Fig. lb. 3) Adopt a parameter-efficient architecture with fast inference to align with the low-power consumption and high temporal resolution nature of event data. To this end, we design Semantic-aware Segment Any Events model by introducing two key components: 1) The Multimodal Hierarchical Semantic Guidance (MHSG), which is novel multimodal learning framework that aims to learn semantic-rich event representations across multiple levels of granularity by exploiting the knowledge of large language models (Radford et al., 2021; Yuan et al., 2024b). 2) To effectively learn the foundational knowledge of MHSG with parameter-effiieent architecture, Multimodal Fusion Network is built on top of the Segment Any Events model (Chen et al., 2024). It comprises three main components: 1) The Backbone Feature Enhancer explicitly integrates the language-derived semantic priors into event-domain features for better alignment between event representations and text features. 2) The Spatial Encoding module incorporates spatial priors derived from the foundation image segmentation model (Kirillov et al., 2023) to obtain a more discriminative event feature space. 3) The Mask Feature Enhancer finally refines both semantic and spatial priors encoded in the learned event features to further enhance the open-vocabulary recognition capabilities.
Since there is existing benchmark with multiple semantics available for EIS, we further propose four benchmarks to evaluate the open-vocabulary capabilities on the diverse settings of: label granularity (coarse fine-grained class annotations) and semantic granularity (instance-level to part- level segmentation) (cf. Fig. 5b). Quantitative results show that our SEAL significantly outperforms the proposed baselines across all evaluation settings and exhibits the highest efficiency in terms of inference time with efficient parameter size (cf. Fig. lc). In summary, our contributions are as follows:
We introduce SEAL, a Semantic-aware Segment Any Events framework, which is capable of generating worn semantic predictions for event masks across the multiple levels of granularity including instance-level and part-level.
To the best of our knowledge, this work represents the first attempt to address OV-EIS that supports free-form of language queries including noun-level and sentence-level expressions.
We propose novel multimodal learning framework named as MHSG, and light-weight multi- modal fusion network to effectively learn open- open-uocabylary capabilities.
We propose four benchmarks to evaluate our SEAL on diverse settings. Quantitative results show that our SEAL achieves the best results in terms of effectiveness and efficiency.
2 RELATED WORKS
Event-based Segmentation. Most literature event segmentation has focused on Event Semantic Segmentation (ESS), which assigns semantic labels to events for enhanced scene understanding. Alonso & Murillo (2019) introduces the first ESS benchmark DDDI7(Binas et al., 2017), followed
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AR-CDG
Hybrid
AF-DA
Evant Stream
Steam
Evant Stramm
Steam
Semantic-
aware
EventSAM
EventSAM
EventSAM
(RGB) SAM
EZVID
E2VID
EZVID
Voral
Pacsn
Spkk
Profbo
Car
CLIP
CLIP
NosA Fodtio
Maxa Fookar
Event
Recondrusted
Reconsti .tte Frame
Recrnotructed
Backbone
Frams
Frome
Classifcation!
Classification
(a) Semantic-aware SAM baseline
(b) Image Crop Baseline
(c) Feature Pooling Baseline
(d) Event Backbone Baseline
Figure 2: Four Types of Baseline under Three Categories. The three categories (AR-CDG, Hybrid, AF-DA) are designed as baseline based on the strategy of transferring inage-domain knowledge to the event domain. Refer to Sec. 3 and Supplementary material Sec. A.3 for further details.
by several methods that aim to improve accuracy while reducing the need for dense event labels. Specifically, approaches such as EvDistill (Wang et al., 202 and DTL (Wang et al., 202 la) leverage image frames aligned with events, while EV-Transfer (Messikommer et al., 2022a) and ESS (Sun et al., 2022) use unsupervised domain adaptation to transfer knowledge from image datasets to events. More recent work such as HALSIE (Biswas et al., 2024) and HMNet (Hamaguchi et al., 2023) explore cross-domain synthesis and memory- -baee encoding to further enhance mask accuracy. Another direction of research investigates the spiking neural networks for energy- energy-efficient ESS (Che et al., 2022; Kim et al., 2022b; Neftei al., 2019; Wu et al., 2018). Recently, EventSAM (Chen et al., 2024) introduces the Segment Anything Model (SAM) in the event modality to address class-agnostic event instance segmentation. In this paper, we extend this direction tackle OV-EIS by designing a novel Semantic-aware Segment Any Events model.
Open- World Events Understanding. Recent advancements in open-world image understand- ing (Zhou et al., 2022; Ghiasi et al., 2022; Liang et al., 2023; Xu et al., 2023; Zhang et al., 2023) using multimodal foundation models (Radford et al., 2021; Jia et al., 2021) have inspired several works to extend this trend into the event modality. Specifically, EventCLIP (Wu et al., 2023) employs an adapter align event features with CLIP (Radford et al., 2021). and EventBind (Zhou et al., 2024) jointly aligns event, image and text features. Ey-LaFOR (Cho et al., 2023) introduces category-guide attraction loss and category-agnosse features and CLIP. Another line of works (Li et al., 2025; Liu et al., 2025) leverage Multi-modal Large Language Model (MLLM) to understand event streams with natural language while they lack thorough exploration of pixel-level understanding such object detection segmentation. To this end, recent work OpenESS (Kong et al., 2024) have addressed generalizable event understanding at pixel-level granularity, termed OV-ESS, by proposing framework. In this paper, we push the boundaries of open-world event understanding from the semantic level the instance level by introducing a new task called OV-EIS. Our annotation- framework enables the model to recognize individual instances across multiple levels of granularity in response to free-form language queries.
repulsion loss to improve the alignment between the event
superpixel-to-eveer representation learning
PRELIMINARIES AND BASELINES.
Event Representations and Images. Event streams are encoded with pixel coordinates (x,y). microsecond-level timestamp and polarity p E 1, +1 that indicates brightness changes. An event E e occurs when the absolute difference in logarithmic brightness between consecutive timestamps exceeds threshold. To process raw events E, with neural network (Gallego al., 2020), they are converted into regular representations n E RCx NxW The input dimension C varies by representation type: spatiotemporal voxel grids (Gehrig et al., 2019; Zihao Zhu et al., 2018; Zhu et al., 2019), frame-like reconstructions (Rebecq et al., 2019), or bio-inspired spikes (Kim et al., 2022b). Spatial alignment and temporal synchronization between events and images Jimy captured by conventional cameras enable the formation of event-image pairs.
Open-Vone Vocabulary Event Segmentation. In contrast to image datasets, large-scale event-image pairs with dense pixel-level annotations are not yet widely available. To address this gap, OpenESS (Kong et al., 2024) introduces an annotation- framework for OV-ESS that learns semantically rich event representations through mision-languee The event backbone is trained to align its feature space with the visual backbone of CLIP using paired event-image inputs. OpenESS also provides text-domain guidance by generating the pseudo
foundation models such as CLIP (Radford et al., 2021).
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semantic labels with leveraging dataset-specific class lists and CLIP zero-shot classification. However, OpenESS has several limitations: 1) It lacks the ability to recognize multiple instances individually since it focuses semantic segmentation. 2) It relies closed-set of predefined class candidates for text-domain guidance, which limits the open-vocabulary capabilities of the model.
Segment Any Events. EventSAM (Chen et al., 2024) is the first to adapt SAM for event streams to achieve EIS. Specifically, EventSAM learns an event backbone compatible with the original SAM mask decoder in an annotation-free manner by aligning its feature space with the SAM image backbone. Despite its effectiveness, EventSAM only generates class-agnostic masks and lacks the ability to recognize the semantics of the obtained proposals.
Baselines. Based on the preliminaries, we introduce four baselines that are grouped into three categories to address OV-EIS. Further details for baselines are in the Supplementary material Sec. A.3.
Lnnotation-Rich Cross-Domain Generalization (AR-CDG): This category focuses on cross- domain generalization by evaluating the direct transfer of pretrained image segmentation models to event streams. As shown in Fig. 2a, the Semantic-aware SAM baseline exemplifies this approach by leveraging B-sed models (Yuan et al., 2024a; Li et al., 2023; Han et al., 2025; Wang et al., 2024; Chen et al., 2023) that are pre-trained on large image datasets with human annotations. To adapt event streams for these image-based models, the event data is first reconstructed into grayscale images using E2VID (Rebecq et al., 2019) and are subsequently used for inference.
Annotation-Free Domain Adaptation (AF-DA): This category focuses on adapting neural net- works to event streams without using dense human annotations since labeling events is inherently challenging. Fig. 2d illustrates the Event backbone baseline in this category, where a pretrained EventSAM acts as class-agnostic mask generator. The event backbone is learned as an open- vocabulary mask classifier via the OpenESS (Kong et al., 2024) framework while text-domain supervision is excluded based on the assumption that -ataset-specific class names should also be inaccessible to better match the annotation setting. During inference, mask proposals from EventSAM are passed to the event backbone, where the CLIP-aligned mask features are pooled for classification.
Hybrid (AR-CDG + AF-DA): This category combines the AF-DA mask generation approach with the AR-CDG mask classification strategy. Specifically, EventSAM first generates masks that are then classified using CLIP (Radford et al., 2021) or its variants (Liang et al., 2023; Ghiasi et al., 2022; Zeng al., 2024; Zhou et al., 2022; Li et al., 2024) which are traitrained on large-scale image datasets with human annotations. We design two baselines in this category:
Image Crop Baseline (Fig. 2b): The E2VIDDreconstmucted frame is cropped using the predicted mask and feature of cropped region is extracted using CLIP or its variant (Liang et al., 2023).
Feature Pooling Baseline (Fig. 2c): Mask features directly pooled from the pixel-wise feature maps of CLIP-based models (Ghiasi et al., 2022; Zeng et al., 2024; Zhou et al., 2022: Li et al., 2024) which are specifically designed for dense prediction. In particular, events are reconstructed into grayscale images prior to classification.
Limitations of Baselines. Although the proposed baselines achieve open vocabulary inference on events to some extent, they exhibit several limitations: 1) The AR-CDG and hybrid categories still suffer from a huge domain gap between image and events despite the events are reconstructed to grayscale frame. This is because the reconstruction process often adds noise and artifacts, especially under fast motion or low event rates (Kong et al., 2024; Chen et al., 2024), driving the domain gap with natural images. 2) All baselines require two distinct backbones for mask generation and classification which degrades parameter and inference efficiency. Our work aims to address these challenges through a unified and efficient framework.
4 OUR METHOD
Overview. The overall architecture of our SEAL illustrated in Fig. 3. Our SEAL falls under the AF-DA category, where only event-image pairs (1ret, 11,")) are used during training without access to dense event labels. During inference, the model takes the event embeddings 15"e as input and generates mask and class predictions based on the visual prompts P provided by the user. We design the Multimodal Hierarchical Semantic Guidance (MHSG) module to learn rich open- vocabulayy event representations across multiple levels of granularity by exploiting the vision-language foundation models (Radford al., 2021; Yuan et al., 2024b) (cf. Sec. 4.1). To
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Training
Inference
Image
Distilationn
CUI
Sq
Ka
EvenitAM Backbone
SAM Mask Decoder
VLN
Whtile
M
Event Streems
RGB imege
Backbonn Feature Enhencer
Points
Mask Feature Enhanoo
S
RolAlig
Precktio
traggh rooks
Spatial Encoding
EvenESA
DOO P
Backbone
Blakkbon Fastlure
Event
Enhancer
Classifictioo!
Maski Fawtrr
Figure Overall framework of SEAL. The MHSG module (Red and Green) provides rich multimodal semantic guidance across multiple levels of granularity including part-level and instance- level. The multimodal fusion network of SEAL (Purple) enhances class prediction from given binary mask by encoding rich semantic and spatial priors to produce CLIP-aligned mask feature.
effectively transfer the foundational knowledge into our SEAL with parameter-efficien architecture, we introduce light-weight Multimodal Fusion Network that consists of three main components: 1) Backbone Feature Enhancer, 2) Spatial Encoding, and 3) Mask Feature Enhancer (ef. Sec. 4.2). The training details are explained in Sec. 4.3.
4.1 MULTIMODAL HIERARCHICAL SEMANTIC GUIDANCE (MHSG)
In this section, introduce MHSG module which serves rich multimodal guidance to our SEAL, learning open-vocabulary event representations across multiple levels of granularity.
Hierarchical Visual Guidance. Learning the rich event representations with multiple granularity levels is challenging due its sparse and asynchronous nature (Kong et al., 2024). To address this challenge, we leverage images ging and SAM (Kirillov et al., 2023) to group pixels into conceptually meaningful regions under hierarchical semantic structure. SAM can generate three different masks for a single-point prompt, where each corresponds to different level of semantic granularity. We use the inherent property of SAM to generate three segmentation maps: Mims, M,,n "Mm" and Momo, These maps capture the hierarchical semantic structure of Jimg at three levels: semantic, instance, and part, respectively.
Specifically, Mri"s = [m.,m,........ _ Ki), where K2 denotes the number of masks in E (8, i,p) level of granularity. These generated masks satisfy: m/ U mf .... U mi f1,2, x W). where H x W denotes the size of pimg, Subsequently, we get the visual features for each mask by pooling pixel-wise CLIP features: Iing Fabs (Júmg) P x X22 Finally, these CLIP
mask features: Mf [v], v?, ... k.) } at multiple levels of granularity ( {s, i, p} serve as hierarchical visual guidance with rich open- open- ceca-uooabulrryy knowledge.
Hierarchical Text Guidance. Although visual guidance already provides rich semantic priors, the integration of text-domain supervision is essential to improve the responsiveness the model to free-form language (Lee al., 2024). To this end, we leverage LLaMA (Touvron et al., 2023)-based MILM (Yuan et al., 2024b) to generate rich captions for each mask by feeding Timg and M. as input. Each caption is passed to the text encoder of CLIP for the construction of the hierarchical text guidance: (
{t,, t? ,t J, (s,
Discussion. Our MHSG differs from OpenESS as follows: 1) OpenESS groups the pixels into coarse part-level instances with single granularity. In contrast, MHSG defines the region into hierarchical semantic structure that enables the model to understand scenes with diverse levels of semantic granularity. Compared to OpenESS, we do not use predefined class candidates given by the dataset to fully model annotation-free setting. Instead, we leverage pretrained MLLM to generate rich captions as supervision. This allows our model to learn from rich and diverse vocabularies.
4.2 MODEL ARCHITECTURE
As illustrated in Fig.3, our SEAL leverages a pretrained EventSAM backbone and SAM mask decoder as a class-agnostic mask generator to predict instance masks. We build our light- Multimodal Fusion Network top of EventSAM backbone mask classifier to achieve parameter-
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efficient design with single backbone. The three main components of our Multimodal Fusion Network are detailed in the following sections.
1) Backbone Feature Enhancer. This module aims to enhance backbone features by explicitly integrating language information into event-domain features. Specifically, backbone features Ieur e R.DxHH2WW W2 are first extracted from the EventSAM backbone by taking event embedding rev as imput. They are then fed to L = 6 layers of the multimodal fusion module, where each layer comprises a self-attention module, a cross-attention module, and a feed-forward network. During training, text domain guidance M E K obtained from Sec. 4.1 are fed into the cross-attention module as keys and values to enable multimodal fusion. During inference, either the class candidates provided by the dataset or user-defined language input is given the fusion module instead. Finally, the language-fused events features fevt IR. xR HP2xxxxWWW.. xW2 obtained here. Tab. 5 shows that this explicit fusion of events and language results in improved performance due to the enhancement of the alignment between event representations and the multimodal feature space of CLIP.
Guided by the binary mask predictions from the mask decoder, we extract features of corresponding regions from the language-fusee fused backbone features i E D2 M2xW; W2 via Rol-Align (He et al., 2017) pooling operation. obtaining the mask features for classification. During training. the image masks Ming from Sec. 4.1 are used to guide pooling over ieur to extract the mask features Stut which is then aligned with multimodal semantic guidance. Despite its effectiveness, the sole reliance on the pooling method to obtain mask features yields two main issues. 1) Dead masks: The masks for small objects, i.e. part-level proposals often disappear when they are downsampled to match the low-resolution of the feature map i This is due to the significant downscaling from the original mask resolution. Consequently, the pooling layer outputs zero vectors which cause unreliable predictions. The naive solution is to upscale i into the original mask resolution before pooling. However, applying Rol-Align on high-resolution feature maps substantially increases inference time, particularly when processing a large number of masks (cf. Fig. 7c). 2) Semantic Conflict: At low feature-map resolution (H2 x W2). the mask pooling operation projects multiple masks with different semantics onto the same region of ieut, This overlap results in an indistinct event feature space (cf. Fig. 4a). To address this issue, we complement the pooled mask features Seut with two additional components: Spatial Encoding and Mask Feature Enhancer. Refer to Sec. A.5 of the Supplementary material for more details about these two issues and the motivation of Spatial Encoding module.
2) Spatial Encoding. As shown in Fig. 4b, this module aims to make event representations more discriminative by encoding the spatial priors into mask features. To this end. we leverage the mask token from the SAM mask decoder that encodes rich spatial cues such as the shape and position of the mask. During training. we calculate the bounding boxes by E IR Ki x4 from the image masks Minms and feed them into the SAM mask decoder as visual prompts along with the event backbone features Ieve The resulting mask tokens G E IR KIXD are then combined with the mask features Seat E RRKxD which can be formulated as M denote the concatenation operation and the projection layer, respectively. During inference, user- provided visual prompts are fed into the SAM mask decoder to obtain the mask token G and the corresponding event mask prediction M The ays is then used to pool Seet from the enhanced backbone features jeet, followed by integration with Gevt for spatial encoding. Note that Sevt corresponds to semantic features obtained from the language-fused feature representations i In comparison. G corresponds to spatial features guided by the mask decoder. Combining these two features enables mutual compensation between semantic and spatial information.
proj(concat
Stxt),, where concat(;,) and proj(-)
3) Mask Feature Enhancer. Semantic and spatial priors encoded in mask features M are further enhanced through cross-attention layer, where the language-fused backbone features ieet (semantic) with positional encodings (spatial) serve as keys and values. To focus attention on relevant areas, the mask features M are forced to aggregate the foreground regions of the predicted mask by applying masked attention (Cheng et al., 2022). During training, we use image masks Ming as attention masks. During inference, the attention masks are instead derived from the predicted event masks Mevt produced by the mask decoder.
4.3 TRAINING
Dataset. We collect event-image pairs by combining the training splits of two popular event- segmentation datasets (Alonso & Murillo, 2019; Sun et al., 2022), named Mixed-24K. It contains

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Published as a conference paper at ICLR 2026
Table 1: Quantitative results in closed-set event instance segmentation. Our SEAL shows superior effectiveness and efficiency in performance and inference time by outperforming all baselines across three benchmarks with parameter-efficient architecture, The colors of 1st column indicates the three categories described in Sec. 3. Best results are in bold, and second best are underscored.
My. 1
A%
APc
58
My
My.
AP
RPa
46%
AT
AP
APL
Trme test
157
2.3
243
222
202
202
11.5
12.4
11.
100.21
314.7
Noo
31.5
26.1
155
14.9
11.3
119
194
201
51
59
16
44
-10
11.1
212,00
533.
2%
of oi 3112)
268
18.8
-58
30.4
214
25.9
26.
224
13.
14.2
Weart
15.
011
11.6
41.
13.
14.
322
12.1
Scansdscana Diilic ciill J011
326
15.5
18.3
13.6
46.0
08.0
180
23.3
28.3
313
2611
314
96
119
13.
113
-53
15.1
11.18
1041
(650)
14411
(63.)
64.31
(6681
141
1680
(48.)
(454)
(46%)
(658)
14860
(481)
(481)
(46.)
(45.)
14511
Table 2: Quantitative results on closed-set event
Table 3: Ablation study on Hierarchical Se-
part segmentation.
mantic Guidance. AP scores are used as metric.
Metloal
Bio
instance
semantic
Point
DDD17-Im:
DSECHT-Is
Point
DSEC-PPrr Box
APra|
AP
APta
APhe
31.2
37.5
13.5
18.2
Pil Oven an 2013)
12.
13.1
15:8
16:1
3322
25.0
32.2
38.3
12.7
16.3
(fleee 3000
5.8
10:1
10.3
11:1
13.9
11:4
31.0
36.9
22.5
11.9
14.4
ceans2 Bltchoo al, 20199
10.2
10.4
18.7
11.5
13.2
12.3
Zapih Oiis 301289
9
103
204
1009
32.0
18.1
21.5
132
18.5
SEAL (Our))
136
166
174
183
269
31.
31.5
37.9
22.4
28.3
12.4
15.4
(40.5)
(48.)
(186)
(62.0)
(43.7)
(-7.4)
32.3
38.2
22,4
28.8
136
18.3
24,032 event-image pairs, and all of the pairs are used during the training with pre-processed MHSG. Refer to Sec. A.4 of supplementary material for more details on the training dataset and MHSG.
Training. Our training process consists of two stages. In stage 1, we follow the original paper (Chen et al., 2024) to train EventSAM on the Mixed-244 24K dataset. In stage 2, we train the huckbone feature maskfeature pretrained EventS including its backbone, mask decoder, and prompt encoder frozen. For training supervision, the image masks Mina from Sec. 4.1 are used as guide where the corresponding mask features Mpat are pooled from SEAL and subsequently aligned with MHSG. Specifically, the mask features are aligned with both image-domain M and text-domain M guidance for each semantic level I E (8, using a cosine similarity loss. We thus formulate the distillation loss
enhancer together with the spatial encoding and mask feature enhancer module. Here, we keep the
function as:
{s.1.p}
{s,h.p 1 (1-cos(M M Ki
Cdistsil
Σ
1 (1 (1-cos(Mi cos(M Mimo))=
1
Σ
(1)
Ki
where cos(s,)) is the cosine similarity function with I.2 feature normalization.
EXPERIMENT
5.1 EXPERIMENTAL SETTINGS
Benchmarks. Since no existing benchmark is available for event instance segmentation, we adapt two widely used ESS datasets (Alonso & Murillo, 2019; Sun et al., 2022) to evaluate our model. Specifically, given the event-image pairs with ground-truth semantic maps, we generate ground-truth instance masks from images using SAM while the semanntc label for each instance is obtained from the corresponding semantic map given by the datasets human annotators. Finally, construct four benchmarks to evaluate OV-EIS on diverse settings. 1) The DDDI7-Ins is a benchmark with 6 semantic classes derived from DDD17-Seg (Alonso & Murillo, 2019) dataset. It consists of 3,890 testing events acquired by DAVIS346B with spatial size of 352 x 200, along with synchronized gray-scale frames provided by a DAVIS camera. The DSEC11-Ims benchmark is derived from DSEC-Semantic (Sun et al., 2022) dataset, which contains three testing sequences comprising 2,809 events at a spatial resolution of 640 440. The class label for each instance obtained from the semantic map parsed into 11 semantic classes. 3) The DSECI9-Ins is a ame-grained annotated version of DSECI/ -Ins with 19 semantic classes while sharing the same scene. 4) The DSEC-Part benchmark is designed to assess event part segmentation using the testing sequences of DSEC-Semantie (Sun et al., 2022). We choose Vehicle and Building as base categories to obtain leve- masks since they have multiple distinguishable components. We define five part-level classes for vehicle while four classes for building. The class label for each mask is manually annotated to ensure an accurate evaluation since the original dataset does not provide ground-truth part-level semantic maps. It should be noted that our benchmarks cover diverse evaluation settings including label granularity and semantic granularity (ef. Fig. 5b). Specifically, evaluation on DSECI / and DSEC19-Ins
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simulates the label granularity from 11 classes to 19 classes while evaluation on DSECII-In and DSEC-part captures semantic granularity from instance-level to part-level segmentation. Please refer to the Sec. A.1 of the supplementary material for more details about the benchmark configurations.
Metrics. We employ a commonly used instance segmentation metric: Average Precision (AP). AP scores are evaluated at mask overlap thresholds of 50% and 25%, and averaged over the range of [0.5: 0.95 0.05]. The prediction confidence score is set to 1.0 in our experiments.
Implementation Details. We resize input images and event frames to 512x512 and use ViT- B (Dosovitskiy et al., 2020) based EventSAM (Chen et al., 2024) as the mask generator. Our SEAL trained for 15,000 iterations with Adam (Kingma & Ba, 2014) using batch size of 8, and an initial learning rate of 2e-4 decayed by 0.9 at the 3rd epoch. All experiments are conducted on a single NVIDIA RTX 6000 Ada.
Baselines. Four types of baselines are illustrated in Fig. 2. We choose OVSAM (Yuan et al., 2024a) as the Semantic-awan SAM Baseline in Fig. 2a since the code is publicly available. In Fig. 2b, the original CLIP (Radford et al., 202 1) and OVSeg (Liang et al., 2023) are adopted as mask classifier for the Image Crop Baseline, where OVSeg is fine-tuned variant of CLIP. For Feature Pooling Baseline shown in Fig. 2c, we choose MaskCLIP (Zhou et al., 2022) as the structure-modified variant of CLIP and, OpenSeg (Ghiasi et al., 2022), MaskCLIP++ (Zeng et al., 2024) and Adaperr (Li et al., 2024) as the fine-tuned variants of CLIP to serve as mask classifier. For the Event Backbone Baseline in Fig. 2d, we form 2 frame 2 recoon and 2rame2spikl event representations, where E2VID (Rebecq al., 2019), ResNet-50 (He al., 2016) and Spiking- DeepLab (Kim et al., 2022b) are adopted as event backbone, respectively. The event backbone is trained using only event-image pairs from Mixed-24K be consistent with AF-DA setting.
based on the use of
Evaluation with Prompts. Following (Yuan et al., 2024a; Li et al., 2023), we evaluate our SEAL in the interactive segmentation setting using visual prompts such as boxes or points sampled from ground truth masks. For point prompts, three points are sampled using the furthest point sampling (Qi al., 2017). In the supplementary material, we further show the effective and efficient variant of our SEAL, named SEAL++, that supports prompt-free OV-EIS by combining the spatiotemporal object detection models for events (Gehrig & Scaramuzza, 2023; Zhang et al., 2025) into our framework.
5.2 EXPERIMENTAL RESULTS
Closed-Set Event Instance Segmentation. We quantitatively evaluate our approach on the closed- vocabulary event instance segmentation task on DDD17-Ins, DSECII-Ins and DSECII-Ins. As shown in Tab. 1. our SEAL achieves clear improvements over all basclines on DDD17-Ins and DSEC/I-Ins. Specifically, we surpass the second-best results with box prompts by 3.2% and 3.4% AP, respectively. OVSAM in the AR-CDG category shows unsatisfactory performance due to the huge domain gap between images and frame-like reconstructed event frames In the Hybrid category, OpenSeg (Ghiasi et al., 2022) and MaskCLIP++ (Zeng et al., 2024) outperform OVSAM by leveraging higher-quality event masks generated by an event-adapted mask generator (Chen et al., 2024). However, domain gap remains between images and reconstructed events during mask classification leading to sub-optimal performance. We also observe that the Feature Pooling Baselines (Ghiasi al., 2022; Zhou al., 2022; Zeng al., 2024; Li et al., 2024) achieves better performance than the Image Crop Baselines (Radford et al., 2021; Liang et al., 2023) since mask classifiers for Feature Pooling Baselines are explicitly designed for dense prediction tasks during pretraining. The baselines in the AF-DA category addressed the domain gap between events and images through domain adaptation. However, their performance remains unsatisfactory due the absence of text-domain guidance and explicit spatial priors. Furthermore, a semantic conflict occurs when CLIP features from high-resolution masks are distilled into lesortienol event feature maps due to the severe downscaling. Hence, multiple instances with distinct semantics are often projected onto the same local region which ultimately degrading performance.
Our SEAL achieves the best performance by learning discriminative event representations through the rich MHSG supervision and an effective model architecture that leverages both semantic and spatial priors. The performance gap between our SEAL and the second-best results (Zeng et al., 2024) narrows on the DSEC/9-Imt benchmark with more fine- -meigraieed classes. This shows the advantage of annotation-rice training with large-scale images leveraged by image-pretraince baselines. Despite the absence of direct human annotations, our SEAL still achieves the best performance surpassing competitors by 0.7%. 2.3%, and 2.1 % on AP, AP50, and AP25 scores, respectively. We also compare
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Table 4:
Ablation study on Multimodal
Table 5: Ablation study on Model Architec-
Guidance. AP scores are used as metric.
ture. AP scores are used as metric.
VG
TG
Point
DDD17-Ins
Box
DSECIL-Jss Point
Box
DSEC19-Ims Box
Point
Pasion
sR.
MPR.
Point
DIDD17-00 des
Box
DSBCIT-Is
Punn
Bux
DSEC///Jur
Point
Box
Poirs
Box
10.7
12.2
30.3
37.0
19.9
24.0
10.5
12.9
12,6
31.6
21.3
13.0
52.0
38,1
21.5
27.1
11.3
14.5
12.4
15.2
32.3
38.2
22.4
28.8
11.7
14.8
32.3
38.2
23.4
25.8
11.7
14.8
13,6
18.3
Dead masks
Semantic Conflict
Distinct Feature Space
(a) wo Spatial Encoding & Mask Feature Enhancer
(h) Spatial Encoding & Mask Feature Enhancer
Figure 4: Event feature space visualized by UMAP (Mclnnes al., 2018). Event representation learned without SE and MFE exhibits dead masks (Red box) and semanlic conflict (Purple box) with indistinct feature space. Our model addresses these issues by encoding spatial prior via SE and MFE to give more discriminative feature space (Green box). Refer to Sec. 5.3 for further details.
the efficiency on both inference time and parameter size in the gray-colored column of Tab. 1. Our SEAL shows significantly fewer parameters and the shortest inference time, which highlights the efficiency of its single-backbone architecture. The small model size of our SEAL aligns with practical requirements since event cameras are often deployed on edge devices with low-power constraints (Ramesh et al., 2020). The practicality of our SEAL is further demonstrated by its fast model inference that enables efficient exploitation the asynchronous high temporal resolution of events. More qualitative results provided in the supplementary material Sec. B.5.
Closed-Set Event Part Segmentation. We also evaluate our approach on the event part segmentation on DSEC-Part benchmark. For comparison, adopt VLPart (Sun et al., 2023), an image-pretrained open vocabulary part segmentation model as a mask classifier within the feature pooling baseline. Event backbone baseline is further adopted for comparison. Our SEAL achieves higher AP scores than the baselines, demonstrating the effectiveness of MHSG.
5.3 ABLATION STUDY
Hierarchical Semantic Guidance. In Tab. 3, we analyze the hierarchical semantic guidance of MHSG across part, instance and semantic levels of granularity in DDD17-Ins, DSECII-Ins and DSEC-Part benchmarks. We have following observations. 1) We observe performance degradation on the part segmentation benchmark (DSEC-Par) when part-level guidance excluded (2nd, 3rd, and 5th rows). 2) Performance instance segmentation benchmarks (DDD17-Ins and DSECI IIns) falls into sub-optimal when guidance from higher level of granularity such as instance-level or semantic-level removed (Ist row). 3) Our SEAL consistently contributes the best performance across diverse benchmarks including both part and instance segmentation only when the guidance from all levels of semantic granularity is combined (4th, 5th rows vs 6th row). This result shows the effectiveness of our hicrarchical semantic guidance.
Multimodal Semantic Guidance. In Tab. 4, we analyze the multimodal guidance of MHSG on the DDDI7-Ins, DSECI1-Ime and DSEC/9-It benchmarks. Using both of Visual-domal Guidance (VG) and Text-domain Guidance (TG) yields the best performance in all the benchmarks (3rd row).
Model Architecture. In Tab. 5, we analyze the model components, Our SEAL shows unsatisfactory performance when Spatial Encoding (SE) and Mask Feature Enhancer (MFE) are removed (Ist row). To investigate the effects of SE and MFE, we visualize the learned event feature space using UMAP (McInnes et al., 2018). Specifically, we collect the predicted mask features from the DSEC- Part benchmark and visualize them in 2D space, where each feature is color-coded according to its ground-trui label. Fig. 4a shows the visualization results of SEAL trained without SE and MFE. As mentioned in Sec. 4.2, masks with small size often disappear due to the severe downscaling to feature map size. This results in zero vectors from the Rol-Align layer which we referred to as dead masks. These dead masks shown in Red box, where multiple masks with different semantics

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(colors) are clustered together since they are all mapped to values. Additionally, the Purple box illustrates the indistinct feature space caused by semantic conflict, where the green and blue dots lack clear separations. We address these issues by making the event feature space more discriminative with the encoding of spatial prior via the SE and MFE modules. As shown in Fig. 4b, our SEAL with ME and SFE shows more distinct feature space, where green and blue dots clearly separated (Green box). Dead masks are also not observed. Additionally, Tab. 5 shows that combining SE (2nd row) or MFE (3rd row) yields performance improvement compared to the model without either module (Ist row). Using both ME and SFE achieve the best performance across all benchmarks including part and instance segmentation (Ist-3rd rows 5th row). Similarly, the fusion of text knowledge in the backbone feature enhancer also leads to better performance (4th row vs 5th row) with better alignment between events and text.
6 CONCLUSION
In this study, we address the Open- Open-Vocabulary Event Instance Segmentation (OV-EIS) problem, an important yet largely overlooked research challenge. We first propose four types of simple baselines that enables OV-EIS by combining the existing open vocabulary components. Then, we discuss the limitations of our proposed baselines which further motivate us to design SEAL, the novel framework to address OV-EIS that understands the event streams with free-form language across multiple levels of granularity. Extensive experiments with our proposed EIS benchmarks show that our SEAL outperforms all of the baselines in terms performance and inference speed with parameter-efficient architecture. We believe that SEAL can serve as strong starting point for open-world, fine-grained event understanding,
This research project is supported by the National Research Foundation, Singapore, under its NRF Investigatorship Programme (Award ID. NRF-NRFI09- 0008), and the Tier 1 grant T1-25IRES2305 from the Singapore Ministry of Education.
Acknowledgment.
ETHICS STATEMENT
This research complies with the ICLR Code of Ethics. In this section, we discuss the following ethical considerations.
Data and Privacy: : The datasets used in this research are publicly available (Sun et al., 2022; Alonso Murillo, 2019).
Potential Harmful Applications: The proposed method for open- vocabulcyy events instance segmentation could be used sensitive domains such as surveillance and public-space crowd monitoring. We urge future users to deploy the model responsibly, with appropriate safeguards to mitigate potential misuse.
We confirm that all experiments were conducted in accordance with best practices in research ethics, and all results are reported with full transparency and accuracy.
Research Integrity:
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our work, we state specific implementation details in Sec. 5.1 and Supplementary material Sec. A that cover the specifications for our proposed benchmarks, baselines, MHSG module and the model architecture of SEAL. The code will be made publicly available.
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Segment Any Events with Language
Supplementary Material
TABLE OF CONTENTS
Implementation Details
17
A.1 Benchmarks
17
A.2 Preliminaries
19
A.3 Baselines
20
A.4 MHSG
22
A.5 SEAL
23
Additional Experiment Results.
24
B.1
Cross-dataset Evaluation
24
B.2
Prompt-free OV-EIS
24
B.3
Efficiency
26
B.4
Quantitative Results
27
B.5
Qualitative Results
27
C Limitation..
28
A
IMPLEMENTATION DETAILS
In the following sections, we present additional details facilitate the implementation and repro- ducibility of our proposed SEAL framework. In Sec. A.I, we outline the specific configurations and data processing pipeline of our proposed four EIS benchmarks. In Sec. A.2, we supplement the pre- liminaries of our work by detailing the two pioneering works in event-based scene understanding with image foundation model. Based on the preliminaries, we present the implementation details of our proposed baselines under three categories in Sec. A.3. Finally, provide the design specifications of MHSG and our proposed SEAL framework in Sec. A.4 and Sec. A.5, respectively.
A.1 BENCHMARKS
In this study, we use two popular event-segmeenattion datasets: DSEC-Semantic (Sun et al., 2022) and DDD17-Seg (Alonso & Murillo, 2019) construct four EIS benchmarks that simulate diverse evaluation settings for open-vocabulary scene understanding. Fig. 5a illustrates the whole processing pipeline of the proposed benchmarks two stages. 1) We first generate class-agnostie masks by leveraging the sellossemmetttiinn capabilities of SAM (Kirillov et al., 2023). SAM can generate three different masks with different granularity for single point where we denote semantic-level, instance-level and part-level. All levels of granularity are used to construct the EIS benchmarks where DSEC11-Ins, DSEC19-Ime and DDD17-Ins leverage semantic-level and instance-level masks while DSEC-Part exploits part-level masks. To better separate each granularity in the masks generation, we use the customized SAM proposed by Qin et al. (2024). 2) Semantic labels are subsequently assigned AAM-generated masks by parsing the ground-trute truth semantic maps given by the original datasets (Alonso & Murillo, 2019; Sun al., 2022) or employing human annotators.
In the following, supplement the additional details of two existing ESS datasets (Sun et al., 2022; Alonso Murillo, 2019) and the specific configurations our proposed EIS benchmarks (cf. Tab. 6). Furthermore, Fig. 6 illustrates the class distributions of each EIS benchmark.
DDD17-Seg (Alonso Murillo, 2019) is the first ESS benchmark built on top of the DDD17 (Bi- et al., 2017) dataset. DDD17 (Binas al., 2017) provides the large-scale event-image pairs
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Semantic Labeling
Granularity
dgn poln
DBEC11-00 D8EC19-in
SAM
0001174ax
100L
00017-ins
RGB Image
Instance-ovel
Naps
Semantic Gramulartty
DBEC
DBEGIT OOEST9 Ira
DSEO Purt
Part Ionel Mapio
Human Annokaor
(a) Data Processing Pipeline
(b) Evaluation Setting
Figure 5: (a) We first generate class-agnostic masks using SAM, and then assign semantic labels to each mask proposal either from ground-truth semantic maps or through human annotation. (b) Our four EIS benchmarks simulate label granularity from coarse to fine-grained class configurations and semantic granularity from instance-level to part-level understanding.
acquired by a DAVIS346B over several hours of driving scenes. Semantic maps for the correspond- ing event-image pairs are additionally generated by DDD17-Seg by using pretrained semantic segmentation model. In total, DDD17-Seg comprises 15,950 training event-image pairs from 8 driving sequences and 3,890 testing samples from a single driving sequence. Each sample has 352 x 200 spatial resolution, where each pixel is mapped to one of the six semantic classes, including flat, t, construct. tnysky, ob object, vegetat t on, human and vehicle. We further de- rive DDD17-Ins by adding the instance-level masks to DDDI7-Seg. For evaluation, the background class excluded to align with the instance segmentation setting where the "flat" class is removed.
DSEC-Semantic (Sun et al., 2022) extends the DSEC (Gehrig et al., 2021) dataset by providing additional semantic maps to its extensive collection of event-image pairs. The DSEC dataset itself stereo driving benchmark that includes recordings from two monochrome event cameras and two global-shutter color cameras. Specifically, Prophesee Gen3. IM sensors are used for events, and FLIR BlackFly S USB3 cameras are adopted for RGB. Based on this rich set of event-image pairs, DSEC-Semantic provides pixel-wise class annotations across 8 training sequences and 3 testing sequences. In total, the dataset comprises 8,082 training samples and 2,809 testing samples, where each sample has 640 x 440 spatial resolution. Each pixel is annotated by two types of semantic labels with a different number of classes, one with 11 classes and another with 19 classes:
11 classes: background, building fence, person, pole, road, sidewalk vegetation, car, wall, traffic sign.
19 classes:
road, sidewalk, building, wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus. train, motorcycle, bicycle.
Based on DSEC-Semantic, we propose three EIS benchmarks by adding either instance-level masks or part-level masks. Specifically, DSECII-Ins is derived by assigning 11 classes to instance masks, while DSEC19-Ins adopts 19 classes to annotate the object proposals. Similarly DDD17-Ins, back- ground classes are excluded in the evaluation to match the instance segmentation setting. Specifically. "background", "road", dewalllkk and "wall" 111 classes are excluded for the DSEC1 I-Ins benchmark, while DSEC19-Ins additionally removes "sky" class as part of the background category.
One of the main objectives of this study to enable events semantic recognition across multiple levels of granularity, encompassing both instance-level and part-level objects. To this end, introduce a new benchmark for event part segmentation, named DSECII-Part. Given the part-level mask proposals from SAM, we employ human annotators to manually label the part objects in the testing sequences of DSEC-Semantic. We first select several instance- classes that have multiple distinguishable components and carefully design their pert-level configurations according their semantic structure. Specifically, "bui lding" and "vehicle cle" are selected as base classes where five part-level classes are defined for "vehicle" and four classes are assigned to "bui lding":
vehicle: wheel, window, light, side mirror, license plate
building: wi windww, roof, door, terrace
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Table 6: Data configurations of the four EIS benchmarks.
Benchmarks
DDDI7-Ius
DSECITIIa
DSEC19-Ins
DSEC-Part
Seq
dirl
13__
14_c
15_a
13__a
14_c
15_a
13_a
14_c
15_a
# Frames & Events
3,454
378
1,190
1,238
378
1,190
1,238
378
1,190
1,238
# Mask nums
48,303
6,462
24.572
27,866
6,399
23,543
26,681
3,240
13,144
15.193
Resolution
352 200
640
440
# Classes
classes
Classes
Classes
Classes
12.2%
31.2%
34% 23%
20.0%
32.25
(a) DDDI7-Ins
(b) DSECI 1-Ins
DSEC19-Ins
Figure 6: Class distributions for three EIS benchmarks.
Discussion. Our four EIS benchmarks are carefully designed to simulate the diverse evaluation settings, including label granularity and semantic granularity (cf. Fig. 5b). Evaluation on DDD17- Ins, DSECI I-nns, and DSECI9-Ins models the label granularity where the number of classes are incremental from 5 to 14 within similar driving scenes. Specifically, "object" class in DDD17-Ins is subdivided into finer-grained categories in DDD11-Ins such as "traffic s sign", "pole", "fence", etc. Similarly, DSEC19-Ims further separates "car" class in DSEC11- into more diverse semantics, including "ous", "truck", "train", etc. This enables evaluation of the model response to diverse class configurations by varying the levels of label granularity from coarse to fine. Furthermore, consider semantic granularity by evaluating and pert-level benchmarks. This enables a thorough assessment of its semantic understanding with multiple granularities.
SEAL on both instance-level
Visualization. In Fig. 9-10, we present additional visualizations of our proposed EIS benchmarks.
A.2 PRELIMINARIES
Our SEAL is closely related to two pioneering works (Kong al., 2024; Chen al., 2024). These approaches transfer the foundational knowledge (Kirillov et al., 2023; Radford et al., 2021) of RGB- based models to event backbones, which enhances the generalizability and robustness of event-based scene understanding. In the following, we outline the additional details of these prior works as preliminaries.
OpenESS (Kong et al., 2024) proposes the first annotation- free framework to achieve open- vocabulary event semantic segmentation. Their framework only requires event-image pairs without any semantic labels, and therefore relaxes the inherent challenges of annotating asynchronous events. Given by the event-image pairs (Ieut, _ám=)), OpenESS aligns the event backbone Feet with CLIP (Radford et al., 2021) through multimodal semantic guidance. It can be formulated as:
Lioss = Laistin (7eet), Feclip (rim))) + (revt) (Tel),
where Felip denotes the CLIP image encoder that maps an RGB image Jimy into pixel-wise CLIP features. The event representation Jevt is processed by an event backbone Feevt and subsequently aligned with the CLIP feature space via the distillation objective term Cdistau. addition, frevt is further guided by semantic supervision in the text domain Tllip Given by the closed-set of predefined class candidates, OpenESS generates pixel-wise class labels of event-image pairs by leveraging the multiple vision foundation models. Specifically. OpenESS first generates superpixels in the image domain using SAM and then assigns pseudo-labels to the resulting pixel groups by using CLIP as zero-shot classifier. The pixel-wise labels subsequently mapped to the text representations Telip
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Table 7: Comparisons of four types of proposed baselines. Mask classifiers AR-CDG and Hybrid category are pretrained on large-scale image datasets (1 (118K-13B) with rich human annotations and are directly applied to events. In contrast, baselines in AF-DA category are adapted to events domain by using relatively small set of event-image pairs (8K-15K) in annotation- frree manner.
Canegay
Type
Baseline
(Sigrnentor Classifie)
Configuration
Troining Datasets
Data Size
Domalin
Amtotations
AR-CDG
Semumiic-swaa SAM Busclize
OVSAM Cluas ei cl., 2014
OVSAM
SA-13 (15) (Kiilleo et al, 2023)
33K
Image
(Lun 2014)
118K
CLIP
EvaiSAM
DDD17-Say
DSFC--Smmnie (Sin
14K SK
Event-fnnge
(Radfond et 2021)
Image Buaclinz
CLIP
WIT (Roalfnnd all 2021)
138
Insage
OVSex ot 3021)
EvautSM
DDD17- (Alirao Marilln, 20199)
DSEC Semartic (Sun et 2022)
ISK.
aK
Evest-
OV5eg
COCO (Lin 2014)
118
Imsaee
MarKCIPP (Zhoo 20122)
FwatSAM
DDD17 Seg (Alonso Marillo, 20199.
USEU Semartic (Sin et al, 2022)
1SK.
8K
Event-lmage
MaskULIIP
WTT (Radfont 2021)
138
FM
DUD17- (Nlonso DSEC-Sematic
20119)
15K.
Event-fmags
OpenSia
Peopre-Chop Boaelinz
OpenSeg
Localinod Sarraiiee (Pont-Tluct oi, 2000)
COCO (Lis dl., 2014),
115K 632K
MasCTLIP (Zong 2024)
EveutSAM
DDD17 Seg (Aianso& Marillo, 2019).
USEEC Senc--eeatticc (Sun et 2002)
15K.
8K
Event Image
Mas
(Lin al. 2014)
118
Iiuus
MadcoAiigpier alictal,
Fw
10D17--Sgg (Alonso Marillo, 20119)
DSEC SEEC-Senatic (San et al, 3022)
15K.
8K
Event-faage
Mask-Adupter
00C0 (luin or 2019
11KK
Imay
Franell Olic et al, 201.5)
FwwiSAM
RoNNe159
AF-DA
Phent backhbachhhoe Boseline
(Rtbaag or at., 20199)
Erane
EvaiSAM
ELVID
1DD17-Seg CA lnnsn Marilin, 20199 DSPU--Smmntic (Sunict stall, 2007
1SK. SK
Exent-inag:
Lsanczapiha
FowntAA
(Kim el al., 301269
Spiking,DostLall
through the CLIP text encoder for additional supervision the paired events with balancing weight a. Kindly refer to Kong et al. (2024) for more details.
EventSAM (Chen et al., 2024) proposes the first Segmant Any Events framework to achieve instance-level event segmentation. Similarly to OpenESS, the event backbone Fresam is aligned with the feature space of the SAM image backbone using only event-image pairs (Tevt fing), )) can be formulated as L = Ldistillation (Feet (Te"t,, " (zeem(Tmma) backbone of the original SAM (Kirillov et al., 2023). During inference, EventSAM can predict instance objects M in the event domain by utilizing the SAM mask decoder Msaa as follows:
where Fsam denotes the image
M = M=M M (Feet (reut), paam
where Paom the SAM prompt encoder and represents visual prompts such as points or boxes. Kindly refer to Chen et al. (2024) for more details.
A.3 BASELINES
Based on the preliminaries, we introduce four baselines that are grouped into three categories to address OV-EIS. We specify the implementation details for our proposed baselines in the following. Tab. 7 further presents a detailed comparison of the proposed baselines in terms of training setting.
AR-CDG category leverages the rich semantic knowledge derived from the large-scale image datasets to understand the event-streams as cross-domain generalization. Specifically, SAM-based open- vocabulary models (Yuan et al., 2024a; Li et al., 2023; Han al., 2025; Wang et al., 2024; Chen et al., 2023) which are pre-trained on richly annotated image datasets are directly applied to event streams to achieve OV-EIS. To mitigate the inherent domain gap between images and events, we reconstruct events into gray-scale images using E2VID (Rebecq et al., 2019), which are then employed for inference. It can be formulated as:
M, M,C = M(Re2vid P),
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where (.) denotes E2VID operation and M represents the unified model for segmentation and mask classification. In the implementation, OVSAM (Yuan et al., 2024a) is adopted as AR-CDG baseline since it is fully open-soureed. Given the visual prompt P, mask predictions M and semantics labels C' are directly obtained from OVSAM via unified framework.
AF-DA category focuses on adapting neural networks event streams without relying on the dense event annotations where it is named as Aunotation-Free Domain Adaptation in this study. It is comprised of two-stage framework: 1) Class-agnostic mask proposal module first generates the instance masks in the events domain. 2) Obtained instance proposals are subsequently classified by the open- ppen-vocabulary classifier. The whole pipeline can be formulated as:
M = Mmaak (1e0t, P), M), (ob( E HxWxD
(5)
pols
(6)
where Mmask and M denotes the mask proposal module and the mask classifier, respectively. Given the event representation rewt, classifier M produces per-pixel features which are subse-
quently pooled by the binary mask obtained from Misssk, Specifically, Rol-Align (He al., 2017) is used for the pool(-.) operation. The resulting pooled mask feature fels is then employed for the mask classification conditioned on user-provided text prompts.
For implementation, we adopt EventSAM (Chen et al., 2024) as a class-agnostic event segmentor which pretrained by following the original paper. Mask classifier is implemented with three different types of event backbones inspired by OpenESS (Kong et al., 2024), where each corresponds distinct type of event representation:
2voxe1 converts raw events e into regular voxel grids that are compatible with convolu- tional neural networks since encodes both spatial and temporal information. Given the predefined number of events or time window, the voxel grids can be formulated as:
Teot Σ p, - x) - ma It; =). -B-11 to oyEcr
AT
where AT is the time window and B is the number of temporal bins. to denotes the timestamp of the first event in the current time window, and 5() is the Kronecker delta function. In the implementation, we follow Sun et al. (2022) to produce voxelized event representations. We set B and use 25 ms of time window for the DSEC-based benchmarks, and a 15 ms window is employed for the DDD17 I-bbad benchmarks. E2VID (Rebecq et al., 2019) is adopted as backbone for this type where it outputs per-pixel event embeddings ront E IR XxxxX x with feature dimension D. In the implementation, we add additional ResNet blocks on top of the E2VID backbone. During training, we initialize the E2VID backbone with the official pretrained weights. We then keep it frozen while optimizing only the newly added ResNet blocks. This training scheme can be seen as linear probing which improves training stability and convergence speed.
frame2recon adopts event reconstructions via E2VID as the pre- pre-processing step for events. The image-based model ResNet-50 (He et al., 2016) is used for the backbone, where ingests events-reconstruuted frames as input. E2VID operation can be described as follows:
z rec = ge2vid (IEet (I=11 A == ==1,...,,
(8)
rree_ = De2vid (
(9)
where ge2vid and De2vid denotes the encoder and decoder of E2VID. The recurrent network adopted to leverage information from previous events for the reconstruction of the gray-scale images with high fidelity. However, it should be noted that E2VID operation often introduces unwanted artifacts loses details due to fast motions or low event rates.
frame2spike represents the spiking neural network, where it first converts events into spikes and processes them a with spiking network. We follow Kim et al. (2022b) to implement spiking- based classifier where the Spiking-DeepLab (Kim et al., 2022b) is employed as backbone. We adopt rate coding as the spike encoding strategy (Kim et al., 2022b; Kong et al., 2024). Specifically, at each time step, random value sampled from the range [smin: Smaz] is assigned to every pixel where 8 Smin and smaz correspond to the lower and upper bounds of pixel intensity. A spike with amplitude is generated by the Poisson spike generator if the sampled value exceeds the pixel
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Table 8: Configurations of MHSG module. We collect approximately 24K number of event-image pairs from two widely-used ESS datasets (Alonso & Murillo, 2019; Sun et al., 2022). All of the pairs are processed to generate multimodal semantic guidance.
2013)
2019))
Sey
00.4
01
02.
04.
08
06.
(0.3
08.
dir0
dirk
dird
dirS
drr
Framos& Exrris
955
680
234
100
1752.
1522
1462
786
5540
1320
6945
647
18641
12,200
3,506
14,412
30,647
23,156
27,401
13,062
66,333
13,977
82,432
6,578
Mask
Instunce Jevel
56.773
31,218
14.291
41.322
55,119
66,529
96,091
30,375
205.352
37,055
226,818
10
22,351
Rosnutton
540 x
440
552 200
intensity. Otherwise, no spike is emitted. Spikes accumulated within a predefined temporal window are then aggregated into a frame gevt, which is subsequently used as input to the spiking semantic segmentation network.
All types of classifier are trained by following Eq. 2 (Kong et al., 2024), where the foundational knowledge of CLIP is distilled to an event neural network. However, semantic supervision in the text domain Tekp in Eq. 2 is excluded. In this work, annotation-free refers to training without access to any auman-annotated semantic information. Since T is constructed from the closed set of class candidates provided by the dataset, it contradicts the condition of annotation-free and is therefore removed. Note that the mask proposal module and mask classifier are trained separately using the event-image pairs collected from the two ESS datasets (Alonso & Murillo, 2019; Sun et al., 2022).
Hybrid combines the AF-DA mask generation approach with the AR-CDG mask classification strategy. It follows the two-stage framework of the AF-DA category (cf. Eq. 5-6) while the mask classifier M is replaced with pretrained RGB-based models which fall into the AR-CDG category. To match the input modality of events with the RGB models, event representation is first reconstructed to gray-scale frame via E2VID which is then passed into the mask classifier M
This category is comprised of two types of baseline:
Image Crop Baseline crops the reeottreconttruuted frame using the predicted binary mask, and then classifies the resulting image patch with a mask classifier. It can be formulated as:
pals M (crop (Teet), M));
(10)
where crop(..)) denotes the cropping operation. We employ CLIP ViT-L/14@336px (Radford et al., 2022) and ViT-L/14 based OVSeg (Liang et al., 2023) an open-vocabulary classifier. It should be noted that OVSeg represents the fine-tuned variant of CLIP, where additional Iearnable
parameters are introduced to the original CLIP for the transfer of its image classification capabilities to the mask classification task.
Feature Pooling Baseline adopts the pretrained models that are specifically designed to conduct per-pixel classification or mask classification. Given the predicted binary mask M and event- reconstructed frame 22id (I the mask classifier M pools the CLIP-aligned mask feature fals by using Rol-Align additional pooling network. We employ MaskCLIP (Zhou et al., 2022), OpenSeg (Ghiasi et al., 2022), MaskCLIP++ (Zeng et al., 2024) and Mask-Adapter (I.i et al., 2024) as open-vocabulary mask classifier for this baseline. Given the gray-scale frame reconstructed from events, MaskCLIP and OpenSeg generate per-pixel CLIP features which are then pooled by binary mask via the Rol-Align operation. Conversely, MaskCLIP++ and Mask-Adpater employ additional maak--pooingg layers which are specially designed to achieve the mask classification task. These pooling layers consist of several learnable modules which are pretrained with large-scat scale annotated datasets (Lin et al., 2014). Note that MaskCLIP represents the structure- modified variant of CLIP where only the structure of CLIP modified without any fine-tuning. In contrast, OpenSeg, MaskCLIP++ and Mask-Adpater denote fine-tuned variants of clip where rich semantic knowledge of CLIP is transferred to pixel-level understanding task through additional training.
A.4 MHSG
In this study, we propose MHSG which is a novel multimodal learning framework that enables our SEAL to learn semantic-rich event representations across multiple levels of granularity. In the following, provide additional details and visualizations for the proposed MHSG framework.
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Time (ne))
Diseppoart
Contict
beaae MAD 3R)
Postar Moo D2x
wmnge 612x S1D
Sigpnetation (riza S1z)
Gegrevaation
(112 4
Numser
(a) Dead Masks
(b) Semantic Conflict
(c) Rol-Align Ablation
Figure 7: (a) Dead Masks: Small size of masks often disappear in the pooling operation due the severe downscaling. (b) Semantic Conflict: Multiple masks with different semantics are projected into same feature patches, which leads indiscriminative events representation. (c) Rol-Align operation in high-resolution feature maps substantially increases inference time (Red), especially when the large number of masks are given.
Motivation. Our MHSG is closely related to superpixel-driven contrastive learning (Hénaff et al., 2021; Sautier et al., 2022) where superpixels group the pixels into conceptually meaningful regions and transfer the corresponding semanties into other modalities, i.e., point-cloud (Lee et al., 2024; Wang et al., 2025b), sensory data (Guan et al., 2024; Wang et al., 2025a), medical images (Wang et al., 2022) etc. This superpixel-driven approach can facilitate the events representation learning, which is inherently challenging due to the sparse and asynchronous nature of events, For example, Kong et al. (2024) converts raw events into frame-like representations such as voxel grids or gray-scale images and then groups the pixels into higher granularity, mitigating the sparsity of events. MHSG extends this strategy to enable hierarchical event representation learning by aggregating events into multiple levels of granularity including semantic-level, instance-level and part-level.
Implementation Details. MHSG is comprised of two types of guidance: visual guidance and text guidance. We use OpenSeg (Ghiasi et al., 2022) to extract visual features from the predicted masks, and Osprey (Yuan et al., 2024b) used to generate mask captions that serve as text guidance. Osprey supports two aoptionn-gneratioon modes, namely short and long. We generate both short and long captions where short captions are used as guidance and long captions are given to the backbone feature enhancer for langauge fusion during the training. To generate the masks at multiple levels of granularity, we use a custom SAM proposed by Qin et al. (2024). We curate 24,032 event-image pairs from the two widely-used ESS datasets (Alonso & Murillo, 2019; Sun al., 2022) which named as Mixed-24K where all of the pairs are used to generate MHSG. Tab. 8 presents the specific configurations of MHSG.
Visualization. We visualize the generated masks and captions of MHSG module in Fig. 11-12.
A.5 SEAL
Here, we provide the architectural details of our SEAL and supplement the motivation of the Spatial Encoding module with additional ablation.
Model Architecture. Given the voxelized events feef with 512 x 512 spatial resolution, EventSAM backbone with ViT-B (Dosovitskiy et al., 2020) based architecture outputs 32x downscaled event embeddings. Events tokens 1sst are fed into the Backbone Feature Enhancer which consists of 6 layers of multimodal fusion module. Each fusion module comprises a self-attention layer, cross-attention layer, and an MI.P layer. The text-domain representations are given to the cross- attention layer as keys and values, and I serve as queries, fusing the language prior explicitly. Following Kirillov et al. (2023); Li et al. (2022), the self-attention layers in the first five fusion modules adopt 14 x 14 windowed attention, while the standard global attention is adopted in the last self- self-attention layer to allow token-wiss global fusion. A Spatial Encoding Module with concat(-.)) operation is followed by MLP layers to reduce the feature dimension. Finally, a single cross-attention layer with masked attention (Cheng et al., 2022) is used as a Mask Feature Enhancer to further refine both semantic and spatial priors.
Motivation of Spatial Encoding, Given the event representation Test E HxW backbone feature enhancer outputs language-fusee feature maps j E IR & x W. which are 32 times downscaled from the original resolution. Rol-Align is then applied to the feature maps, pooling the feature of the predicted mask M E IR H in high resolution. Despite its effectiveness, sole reliance of the pooling
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Table 9: Quantitative results in cross-dataset evaluation.
DonI7-tax DSEC I-us
DSECIT
DDDIT-Jas
Method
Puin
Box
Puint
Box
AP
APtn
APs:
AP
APaa
APss
AP
AlPnn
APs:
AP
APtn
APss
Frane2-wcor (Ho et 2016)
14.9
15.3
15.7
15.5
16.5
16.8
23.0
25.4
27.1
25.0
28.6
29.7
tranc2voxси (Rebeeg al, 2019)
15.9
17.8
19.1
17.3
20.3
21.1
24.3
25,4
111
28.1
15.1
26.8
ZraneZapike (Kim al,, 20220)
16.3
18.6
20.1
17.6
21.3
22.2
23.2
26.8
29.0
26.0
31.4
52.8
SEAL (Ours)
20.4
26,6
30.8
24.4
33.7
36.2
30,1
39.4
44.2
36.2
48,4
51.7
(+4.))
(+8.0)
(+10.7)
(+6.)
(+12.4)
(+14.0)
(-5.8)
(+11.0)
(+13.)
(+7.0)
(+13.3)
(+149)
layer to obtain mask features results in two critical issues as mentioned in Sec. 4.2. First, small sized masks often disappear when we match the resolution of binary mask into low-resolution of the feature map in the pooling operation, consequently resulting in zero vectors. We name this problem Dead Masks, and illustrates it in Fig. 7a. Second, as we illustrate in Fig. 7b, multiple masks with different semantics (Red and Blue) are often projected into the same feature patches (Green) due to severe downscaling. We refer to this issue as Semantic Conflict which yields indistinguishable mask features across distinct semantics (ef. Fig. 4a). The naive solution is to upscale the resolution of the feature map to the mask resolution prior to the pooling operation. However, applying Rol-Align on high-resolution feature maps substantially increases the inference time, particularly when processing a large number of masks. In Fig. 7c, we report the inference time of the Rol-Align operation at different spatial resolutions across varying numbers of masks. Specifically, we estimate the runtime of the pooling operation at mask resolution (512 x 512) and feature map resolution (32 x 32), with the channel dimensionality fixed 768. We run the experiment NVIDIA RTX 6000 Ada. Given the 10 number of masks, high-resolution pooling requires 4.0 ml inference time, which is roughly 80 times longer than low-resolution pooling time (0.05ms). The pooling time increases drastically to 264.06ms in high resolution when the number of masks increases to 1,000. However, the low resoluttnt pooling time remains largely unchanged (< 0.4ms) on different mask numbers. Overall, we conclude that pooling in low resolution is essential to achieve real-time inference. To mitigate the Dead Masks and Semantic Conflict issues without upscaling the feature maps, we introduce the Spatial Encoding module to complement the mask feature with geometry information derived from SAM mask decoder. The 2nd row of Tab. 5 and Fig. 4b shows that spatial encoding leads to performance improvement by yielding the more discriminative event feature space.
B ADDITIONAL EXPERIMENT RESULTS
In the following, we present additional experimental results to supplement the effectiveness and efficiency of our framework. In Sec. B.I, we evaluate the cross-dataset generalizability of our framework compared to the baselines. In Sec. B.2, we present SEAL++ which is the variant of our SEAL to support generic spatiotemporal EIS that does not take the visual prompts from the users. In Sec. B.3, we further exhibit the breakdown comparison in runtime and parameter size between our framework and the proposed baseline. Finally, in Sec. B.4 and Sec. B.5, we show additional qualitative results with class-wise EIS performance and quantitative segmentation results of our fremwork across noun -level and sentence-level text conditions.
B. -ROSS-DATASET EVALUATION
Tab. 9 analyzes cross-dataset adaptation of our SEAL against event backbone baselines. All methods are trained on event-image pairs from the DDDI7-Ins dataset and evaluated on the DSECI1-Ins benchmark, and vice versa. Our SEAL shows significantly better semantic generalizability compared to baselines trained in the same setting on both of DSEC/ (SEC/I-Ins I-Ins and DDD/7-Ins benchmarks.
B.2 PROMPT-FREE OV-EIS
Here, we present the additional variant of our SEAL, named as SEAL++, to support the prompt-free spatiotemporal OV-EIS by combining the event-based object detection model into framework.
Model Architecture. Our overall model architecture is illustrated in Fig. 8. We attach the spatiotem- poral object detection branch (Red) on top of pretrained backbone of our SEAL. Specifically, follow DEOE (Zhang et al., 2025), a agnnstic event- based detector that leverages the RVT architecture (Gehrig & Scaramuzza, 2023). Our object detection branch consists of single layer of
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Decoder BEAL
Person
Tree
Ciject resoo head
Building
EvenSAM Backbone
Car
Traffle
Event Streams
Bounding Box
Mask
Classificaiin
1
1
Figure 8: Overall architecture of our SEAL+
Table 10: CAOD results on DSEC-Detection (Gehrig & Scaramuzza, 2024) dataset.
Method
AUC
AR.o
ARs)
ARso
AR100
AR300
Time (ms)
DEOE (Zhang et al., 2025)
41.35
50.0
52.0
52.5
53.3
53.8
6.16
SEAL++ (Ours)
43.32
52.9
$4.5
55,0
55.3
55.6
11.61
(+1.97)
(+2.9)
(+2.5)
(+2.5)
(+2.0)
RVT and FPN (Lin et al., 2017) that are followed by two distinct detection heads: 1) Disentangled Obj. Head and 2) Dual Regressor Head. The RVT block ingests event features I extracted from the backbone at the current timestamp t. together with the recurrent event embeddings I event features I, which
propagated from the previous timestamp t 1. It outputs temporal then upsampled by the FPN layer and processed by two detection heads, and finally obtaining the alass-agnostic bounding boxes for the detected proposals. Here, the final bounding boxes are acquired by combining the objectness scores from Disentangled Obj. Head and potential bounding boxes derived from Dual Regressor Head. Refer to the original paper (Zhang et al., 2025) for more specific details about the detection module.
Discussion. RVT (Gehrig & Scaramuzza, 2023) and DEOE (Zhang et al., 2025) use 4 layers of the RVT block to interpret spatiotemporal events. However, since our SEAL has a strong backbone which is distilled from the image segmentation foundation model (Kirillov et al., 2023), we empirically find that adopting single layer of the recurrent network is enough to generate high quality bounding boxes when it is built on top of our backbone (cf. Tab. 10). This design choice endows our SEAL with prompt-free EIS capabilities without sacrificing the real-time inference and parameter-efficient architecture.
Training. Building the detection module on top of our pretrained SEAL,, we train only the detection branch while keeping all other components frozen. We follow the DEOE (Zhang al., 2025) frame- work to train the detection branch where the objective terms consist of IoU loss, object confidence loss and spatial consistency regularization term. To secure training efficiency and convergence speed, we set the sequence length as 5 and batch size as 6. The maximum learning rate is set to 2 x 10-4 and optimization performed using the Adam optimizer (Kingma & Ba, 2014) for 300,000 iterations. Refer to the DEOE paper (Zhang et al., 2025) for more details in training and objective terms.
Inference. Given the event representation fevt the SAM mask decoder, our SEAL and the object detection branch share common backbone. Given the event features outputted from the backbone of SEAL, the detection branch first generates class-agnostic bounding boxes. These bounding boxes are then used as box prompts for mask generator and mask classifier to get the segmentation masks and class labels.
Dataset. We train the detection branch of our SEAL++ on the training split of DSEC- Detection (Gehrig & Scaramuzza, 2024) with only using the ground-truth bounding boxes. DSEC- Detection is built on top of DSEC (Gehrig et al., 2021) by annotating the bounding boxes with the pretrained multiobject tracking module QDTrack (Fischer al., 2023; Pang et al., 2021). contains 8 semantic classes which are: pedestrians, rider, cars, bus, truck, bicycle, motorcycle and train.
Experimental Settings. We conduct experiments in two-settings: 1) Class-Agnostic Object Detection (CAOD) and 2) closed-set generic EIS with semantic classes. In the CAOD setting, we compare our SEAL+ with DEOE which is pretrained on DSEC-Detection. On the evaluation of the closed-set generic EIS. SEAL+ is compared with the composition of DEOE and proposed baselines in Sec. 3, where the DEOE serves as the class-agnostic detection module and the baselines exploited as
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Table 11: Quantitative results on generic EIS in DSEC-Detection (Gehrig & Scaramuzza, 2024).
Method
AP
AP50
APrs
AP large
AP Pmediuurs
AP
Time (ms)
DEOE OVSAM (Yuan et al., 2024a)
11.7
17.4
11.9
11.3
11.1
0.7
361.2
DEOE CLIP (Radford et al, 2021)
12.1
18.2
12.8
12.9
13.0
1.2
335.59
DEDE OVSeg (Jia et al., 2021)
12.4
18.7
14.3
12.7
13.1
1.4
299.02
DEOE MaskCLIP (Zhou et al., 2022)
12.2
18.3
14.2
12.4
12.7
1.1
302.17
DEOE OpenSeg (Ghiasi et al., 2022)
16.1
21.3
18.2
17.2
16.9
3.2
17
DEOE MaskCLIP++ (Zeng al., 2024)
16.3
21.8
18.5
17.6
17.0
3.4
400.77
DEOE Mask-Adapter (li et al,, 2024)
15.6
21.3
17.9
16.9
16.5
2.2
293.65
DEOE frame2recon (He et al., 2016)
14.8
20,9
16.2
15.1
14.5
1.7
284.51
DEOF frame2voxe1 (Rebecq et al., 2019)
14.4
20.4
15.8
14.7
14.4
1.8
94.35
DEOF Frame2spark (Kim al., 20226)
14.2
DEOE SEAL
16.4
18.8
17.4
3.4
28.44
SEAL+ (Ours)
17.8
23.6
19.2
18.5
18.2
3.5
24.12
(+1.4)
(+1.5)
(+0.4)
(+0.7)
(+0.8)
(+0.1)
mask generator and mask classifier. Furthermore, we adopt the naive integration of DEOE and our SEAL as additional comparison, where DEOE and SEAL serve as individual module without sharing the backbone. All evaluations are conducted on three testing sequences DSEC-Detection dataset comprising zurich city 13 zurich. city 14 and zur i ch city ________ Since DSEC-Detection does not provide segmentation mask events to evaluate EIS, we annotate the ground-truth masks using the paired RGB images and SAM (Kirillov al., 2023). Ground-truth bounding boxes from the dataset are used as prompts for mask generation.
Metrics. In the CAOD setting, we adopt Average Recall (AR) and area under the curve (AUC) the main metrics following the existing CAOD works (Saito et al., 2022; Kim et al., 2022a; Zhang et al., 2025; Wang al., 2021c). For the generic EIS evaluation, adopt Average Precision (AP) from RVT (Gehrig & Scaramuzza, 2023) as the main metric.
Class-Agnostic Object Detection (CAOD). Tab. 10 presents the CAOD results on DSEC-Detection dataset. Our SEAL++ significantly outperforms DEOE in all metrics by leveraging a stronger backbone endowed with foundational priors from SAM. The tradeoff between performance and inference speed is also observed, where DEOE shows slightly faster inference speed with lightweight backbone. However, the runtime of SEAL+ still exhibits real-time speed with only spending 11 11.61ms ms to generate box proposals. Note that operations for mask generation and mask classification are excluded from the runtime estimation since they are not necessary for CAOD.
Closed-set generic EIS. Tab. 11 shows the quantitative results on the closed-set generic EIS in the DSEC-Detection dataset. We make the following observations: 1) Our SEAL++ shows superior performance compared to the naive combination of DEOE and SEAL (DEOE SEAL) since our SEAL++ can provide better quality of bounding boxes compared to DEOE as shown Tab. 10. 2) The performance gap between DEOE SEAL and the baselines in the Hybrid category is less pronounced than in the evaluations on our EIS benchmarks reported in the main paper. We empirically find that SEAL struggles to distinguish "car" class from other semanties within the "vehicle" category when they are provided as joint text conditions. This is likely from class imbalance in the training sequences, where "car" class dominates the vehicles in the scene while other vehicle types are rare. Since most of the classes in our model frequently produces false positives for the "car" class by mislabeling other vehicles as cars. In contrast, the Hybrid baselines exhibit robust recognition under the vehicle-dominatee label space with narrowing performance gap since they use mask classifiers trained on large-scale annotated datasets. 3) By adopting the single-backbone architecture design, our SEAL++ achieves the fastest inference time with parameter-efficien architecture 24. 1mms to process single timestamp.
dataset are fine-grained subclasses within the "vehicle" category,
B.3 EFFICIENCY
We supplement the breakdown comparisons in runtime and parameters size between our SEAL and all of the proposed baselines. Specifically, Tab. 12 shows the runtime breakdown along the model configurations. We observe that events reconstruction via E2VID (Rebecq et al., 2019) is the main bottleneck of the baselines in the ARCDG and Hybrid categories due to the recurrent reconstruction process. This underscores the importance of excluding the reconstruction operation to enable the real-time events understanding. Our SEAL shows the fastest runtime by only taking 6. 161s in the mask classification step. In Tab. 13, further report detailed parameter-size breakdown
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Table 12: Runtime breakdown with model configurations.
Configurations
(uss)
Method
Frame Reconstruction
Mark Generator
Mask Claseifer
Fran: Redonstruction
Mise Generatoe
Mask Classifer
Total (ms)
(Yiun sl., 20044)
CLIP (Radford 2021) 2021)
CLIP
60.56
329.43
OV-Seg (1 (linn et 2023)
DV-Seg
23.97
202
MasKCLIP (2hou al, 2022)
EIVID (Rebeeg
MisKCLIP
27.12
296.01
OpenSeg (Chimsi al, 2007)
OgenSeg
158. 12
427.00
MasACLIPPs (Zoug
EvemSAM
MasACLIPea
125.72
Adapter
(Chen al, 2014)
Adapter
Vsame2ltecor st,, 2016)
Zeare2lle
Vosee (Rchoog ci 2019)
Te Teaee:VVoxe
72.07
Pranc2 (Kim et al., 20226)
Fianc 25kkk
306.52
322.64
SEAT (Ours)
SRAI.
5.16
22.38
Table 13: Parameters size breakdown with model configurations.
Model Configuration
Param Sire (M)
Recontiuction
Classiteer
Reconstraction
Generator
OVSAM (Yian 2024a)
OVSAM
304
314.7
(Radiidd 2021)
CLIP
427.9
$29.9
Seg (Liang 2003)
(2hon al., 2022)
D2VID (Rebeoq al., 2019)
10.7
OpenSez (Ghlnd et al., 2022)
OpenSeg
126.4
228.4
MaskCT IP4e (Zing et all22024) 2024)
EvemSAM
MaskC7 IP44
91.3
199.7
901.7
Aask-Adapeer et al. 2024)
Misk-Adapter
214.7
316.7
Psamez Recon (Tlo al. 2016)
Fsame2Recon
38.7
141.7
Vsans22oxe (Rebeeq
2vosel
109.1
Psame25pike
(Ours)
29.1
highlight the parameter efficiency of our framework. Most parameters our SEAL reside in the ViT-B backbone where the mask classifier adopts lightweight design with 7.8M parameters.
B.4 QUANTITATIVE RESULTS
We present the detailed quantitative comparisons with per-class EIS results across DSEC-Part, DDD17-Ins, DSEC11-/m and DSEC19-/ns Tab. 14-17.
B.5 QUALITATIVE RESULTS
In this section, we present diverse visualization results of our SEAL and SEAL++ across diverse settings, showing their capabilities to understand the multiple levels of granularity in response to free-from language.
Visualization results of SEAL with box prompts. Fig. 13 shows the OV-EIS capabilities of our SEAL based the box prompts. We give multiple bounding boxes that cover all of the objects in the current frame and ask our SEAL to classify the correct instance masks accroding to the given free-form language query. Following Qin al. (2024), we attach predefined canonical phrases which are "object"" "things", "stuff" and "texture" to the text query, making the single language query to classification setting among number of classes: free-form language query canonical phrases. Our visualization results demonstrate that our SEAL recognize the diverse instances in response to both noun-level and sentence-level queries.
Visualization results of SEAL with point prompts. In Fig. 14, give single point prompt to our SEAL and ask to classify the generated mask in the multiple levels of granularity. Since the mask generator of SEAL follows the architecture of SAM, it generates three masks with different granularity according to the single point prompt. We use two of them for the visualization. the coarsest and the finest. For the classification,, use DSEC1 I-/ns class setting for the coarse mask
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Table 14: The per-class segmentation results (AP) on DSEC-Part benchmark.
car wheel
car window
car side mirror
car licesse plate
building window
bailding door
huilding termic
cur wheel
car window
light
car side mirror
car liccass plate
building window
bailding roef
building door
building terrace
building
Method
con
box
VLPart (Sun al,, 2023)
8.0
13.3
3.9
0.2
2.7
71.2
4.5
74
4.9
12.8
16.8
4.1
0.2
4.7
74.3
9.6
15.6
6.8
SEAL (Ours)
and DSEC-Part class setting for the fine mask. Fig. 14 shows that our SEAL can recognize the objects both instance-level and part-level granularities.
Video visualization of SEAL++. We further provide video demo of our SEAL++ to demonstrate the spatiotemporal OV-EIS. Video contains visualizations of 1) Class- Agnosticc Object Detection (CAOD) in events, 2) OV-EIS with free-form language and 3) generic OV-EIS with closed-set of classes. For the generic OV-EIS, we use two-classes setting where consists of vehicle and people.
C LIMITATION
While our method has demonstrated ppen-vocabulary capabilities across diverse linguistic forms and multiple levels of granularity, its recognition still suffers from the inherent challenges posed by the asynchronous nature of events data. Specifically, we empirically find that our SEAL and SEAL++ often fail to detect the objects in region with low event rates. For example, objects far from the camera occupy a minimal footprint in the camera frame, thereby producing highly sparse event streams. This case is easily happened in the driving scenes with straight roads such DSEC (Gehrig et al., 2021) dataset where they exhibit single dominant forward vanishing point. Objects (e.g., cars) near the vanishing point or far from the camera position generate few events due to the minimal apparent motion, leading to missed detections.
Furthermore, model is trained widely-used outdoor scenes such DSEC (Gehrig al., 2021) and DDD17 (Binas et al., 2017), thereby showing the poor recognition capabilities the indoor scene due the huge domain gap. However, our MHSG module and training framework of SEAL can be readily extended to other datasets without requiring the additional human annotations. Hence, exploiting more diverse events dataset such as VisEvent (Wang et al., 2023) and COESOT (Tang et al., 2022) will improve the generalizability of our method.
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Table 15: The per-class segmentation results (AP) DDD17-Ins benchmark.
obyiee
mature
vehiels
object
nature
banann
vehicle
Method
point
box
(Yuan et al., 2024s)
52.0
23.0
52.6
0.9
20.5
(Radlord 2021)
21.4
25.6
OVSeg (Jia al. 2021)
51.5
0.6
19.5
0.9
33.0
52.1
0.6
20.0
1.2
33.6
MaskCLIP (Zhou et al,, 2022)
51.8
0.6
20.2
0.7
24.2
52.3
0.8
21.0
0.8
25.1
OpenSeg (Ghinsi al., 2022)
55.0
0.9
28.0
20.3
44.8
63.6
0.9
35.7
23.4
51.3
MaskCI.IP++ (Zeng al., 2024)
Mask Adapter (Li al., 2024)
56.8
0.6
24.5
20.4
34.0
61.1
0.6
30.8
21.4
35.9
Trame2recon (He et al. 2016)
54.8
0.7
27.1
16.6
45.1
65.2
0.7
34.0
21.8
52.2
Trame2voxe 1 (Reboog ct al., 2019)
55.3
0.6
27.5
8.4
46.3
65.6
0.7
35.8
11.1
54.8
frameZapark ol., 2022h)
24.6
0.6
29.8
1.1
Table 16: The per-class segmentation results (AP) on DSEC11-Ins benchmark.
Method
building
Fence
person
pole
vegetation
car
Iaffie sign
building
Tence
parson
pole
vegetation
car
trafiic
point
box
OVSAM (Yuan al., 2024g)
22.7
3.3
8.7
6.5
39.9
25.4
10.5
35.4
3.9
13.3
7.1
41.1
40.6
14.0
CLIIP (Radford et al., 2021)
25.7
3.5
2.6
6.9
39.9
25.4
4.2
36.1
3.7
2.8
7.0
40.5
30.2
OVSeg (Jin 2021)
30.4
3.5
2.7
40.5
24.2
39.1
3.9
2.8
42.7
24.6
MaskCLIP (Zhou al., 2022)
21.8
3.3
2.6
6.3
38.5
20.9
3.9
21.9
3.4
2.7
6.4
39.5
21.0
OpenSeg (Ghiasi et al., 2022)
25.1
3.7
15.6
6.2
43.0
40.2
9.2
31.6
4.3
17.9
6.3
46.7
47.8
10.8
MaskCLIP+ (Zan et 2024)
25.3
4.0
35.0
5.5
12.9
11.0
Mask-Adapter (1. al, 2024)
27.5
3.7
12.1
7.2
41.9
27.3
6.6
36.8
4.9
14.9
7.3
46.3
31.2
frame2reccn (He al., 2016)
25.1
4.2
10.1
43.3
30.0
7.3
32.0
5.2
10.1
6.6
49.7
35.8
freme2voxel (Rebocq al., 2019)
26.0
3.7
8.7
6.6
42.7
32.2
6.2
33.5
4.1
8.7
6.9
49.8
38.4
7.6
frpme2apork (Kim 2022b)
24.5
3.5
34.9
SEAL (Ours)
27.1
5.0
15.6
35.2
7.5
13.1
Table 17: The per-class segmentation results (AP) on DSEC19-Ins benchmark.
OVSAM co 2004
257
13
35
1.3
32
37.
11.3
02
245
2.6
0D
0.3
11
347
42
33
18
12.
357
13.0
162
34.
63
27
07
24
CLI (iateder 3020)
25.1
3.6
25
34
43
37.
27
02
257
33
33
65
345
411
73
64
353
2.5
142
25.3
39
45
41
10
Maonal 2000)
360
MuACUIP coae
153
MokcliPee Slegge 2014)
106
12.
915
60
18.
33
15%
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364
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74
154
156
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300
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350
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67
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923
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15
411
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307
43
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323
214
29

---

Published as a conference paper at ICLR 2026
LWI
(a) DDD17-Ins
(b) DSECII-Ins
(c) DSECI9-Ins
Figure 9: Visualizations for our proposed EIS benchmarks
30

---

Published as a conference paper at ICLR 2026
RGB Image
Car: Window
Car: License plate
Events
Car: Wheel
Car: Light
RGB Image
Car: Window
Car: License plate
Events
Car: Wheel
Car: Light
00
000
RGB Image
Budiling: Roof
0
Events
Building: Terrace
Building: Window
RGB and Events
(b) Part-level masks with class labels
Figure 10: Visualizations for our DSEC-Part benchmark. All semantic labels are annotated by human.
31

---

Published as a conference paper at ICLR 2026
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with
blue entaviar.
sendadee de strek, Shani lira StRiESE
lonaaa boated the appur, pwokooeed
msnd maak the manerit
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strwet
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lanaal lenoed the right bacandre
our from the right the stuat
ou from rigtt
purke
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Liniiaa right de image there selliiigg with The voue stantee reur rre andi oboeet to the viewer
lonacl Jasssteaat atuandd right
prediiing cler wrts leer
WEBE Naliin
(a) RGB image
(b) semantic-level
(c) instance-level
Figure 11: Visualizations of mask captions in semantic and instance level generated by MHSG module.
32

---

Published as a conference paper at ICLR 2026
lonecal
model strel booeddi
Bul part the merte wilting insnrctioon
slent
widhher reflcctie.
stands right huddtap,
RGB
R0DRE hasciaht
There
louited
mowart mmpp
Soaneaa berk
lo
Image
SHORT
2oamn Natconal
bailting
Eowaaa right coker Horre Sibaec
EEWOE waiting teserch
demoar
neur'a Masilan and blick shis, mover ant suggart
and bluck appain oi parted powsibl
RGB Image
Bitk
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EOWC There langtaill
perse wathing towarde sie street
davve sgaene boicngg Sruee, obberiing
Figure 12: Visualizations of mask captions in part-level generated by MHSG module.
33

---

Published as a conference paper at ICLR 2026
RGB
RGB Image
Home sweet home.
Fove nature.
8GB
Mice weathert
Sidewolk
RGB Image
Walking
Ride a bicycle.
light
RGB Image
Throwing Sway the gartgg.
Apartment.
RG8
Moving
Figure 13: Qualitative results of our SEAL with box prompts.
34

---

Published as a conference paper at ICLR 2026
part
8GB Image
Car instance
Window Part
pare
RGB Image
Car musoanc
light- Port
Building
RGB Image
Building iutence
Window - Part
(a) RGB Image
(b) Instance-leved
(c) Part-level
Figure 14: Qualitative results of our SEAL with point prompts.
35


Reviews:
[{"review_id": "wdBZdA1yPf", "reviewer": "Reviewer_AXhG", "summary": "The paper proposes the SEAL architecture, an open-vocabulary event segmentation architecture based on EventSAM that can segment events on the instance and part level using single nouns or whole sentences as prompts.\n\nThe paper adapts EventSAM, an event encoder aligned with the feature space of the Segment Anything Model (SAM). A Multimodal Hierarchical Semantic Guidance (MHSG) module uses SAM to produce 3 levels of masks, which are then used to pool CLIP features and generate captions for each masked region using a pretrained vision-language model (LLaMA). The multilevel captions are used as conditioning to train a feature enhancer network that takes in event features from EventSAM. The enhanced features are pooled with a region-of-interest pooling from the EventSAM masks and are further enhanced using mask tokens from the SAM decoder. The pooled CLIP features are used to test the alignment of the language-enhanced event encodings.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strong performance against baselines", "weaknesses": "- Limited novelty: the method is essentially a merger of several pretrained foundation models and their data in a clever way.\n\n - Limited ablations: qualitative ablations on the Spatial Encoding or Mask Feature Enhancer are missing or not well explained; I did not understand Tables 4 and 5.", "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": "XVwrQ4T4nd", "reviewer": "Reviewer_PR6s", "summary": "This work introduces a multimodal, hierarchical semantic method  to align event-based segmentation with open-vocabulary language queries. It supports both instance-level and part-level mask generation. The proposed method uses three types of prompt-driven masks generated by SAM for each image (semantic, instance, part). The features are supervised by pooled CLIP embeddings and LLM-generated captions. The multimodal fusion network, with a single backbone, combines language and vision with explicit spatial encoding per mask, addressing previous inefficiencies and semantic conflicts in feature pooling observed in all AR-CDG, Hybrid, and AF-DA baselines.", "questions": "* For DSEC-Part, are part-level mask labels created\n\"by hand\" (since event data is too impoverished for mask proposals alone to identify parts)? If so, doesn’t this somewhat undermine the claim of annotation-free scaling?\n* Artefacts seem to be mitigated compared to the baselines. Even so, couldn’t reconstructing events to images or mapping event data to traditional vision models still introduce artefacts or domain discrepancy, especially under high-speed or low-event-rate scenarios?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 2, "contribution": 3, "strengths": "This work is shown to outperform MaskCLIP and other leading open-vocabulary baselines by 3.4 AP on DSEC11-Ins and 3.2 AP on DDD17-Ins. Inference is 5-18x faster with fewer than 1/5th the parameters. This is a good contribution towards practical application.\n\nSecondly, unlike prior methods, this work’s two-stage mask feature enhancement and spatial encoding overcome the \"dead mask\" issue, where small-event region masks are mapped to zero vectors; UMAP visualisations show tight semantic separation after adding these modules.", "weaknesses": "* DSEC19-Ins is a highly fine-grained dataset: on it, the improvement over MaskCLIP narrows to just 0.7 AP. This, it seems to me that, even with annotation-free training, suggests that the distilled representations are less robust when class granularity exceeds the capacity of available MHSG cues.\n* I think that the main variant of the method benefits from GT-derived visual prompts for mask proposals; although a supplementary \"prompt-free\" variant exists, the claim of real-world flexibility is less convincing without broader prompt-agnostic validation.", "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": "HUNwQ2KQTq", "reviewer": "Reviewer_55Qv", "summary": "This paper introduce a framework to segment events using language. \nIt contains Multimodal Hierarchical Semantic Guidance and Multimodal Fusion Network.\nAnd further propose four benchmarks to evaluate the results.", "questions": "Please refer to the weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper introduce a \"Segment Any Events\" framework, which can  generate open-world semantic predictions for event masks.\n* This paper  attempt to address OV-EIS that supports free-form of language queries.\n* This paper propose four benchmarks for evaluation.", "weaknesses": "* The architecture seems rather complex. Could the authors provide a clearer motivation for the inclusion of these modules? Are all of these modules necessary? Is there a simpler approach that could achieve the same results? Besides, it appears that the method is transferring concepts from open-vocabulary image segmentation techniques, such as OpenSeg, MaskCLIP, MaskCLIP++, and OVSeg to the event modality. Could the authors clarify this adaptation and its justification?\n\n* The paper claims to handle open-vocabulary, but is there a benchmark to demonstrate this capability? For example, can the model generalize to unseen classes, or handle user-defined text queries effectively?\n\n* MHSG uses SAM masks and CLIP features on images as supervision signals for the event modality, but there may have significant modality differences between events and images. How accurately can image masks correspond to event regions in this context?\n\n* The part-level experiments seems conducted solely on the DSEC-Part dataset, which contains very few categories, with a small sample size and severe class imbalance. The model’s performance at a finer granularity (e.g., material, action, state) has not been evaluated. \n\n* Additionally, the paper does not analyze conflicts or consistency between different granularities, for example, how the model handles the situation when a wheel is simultaneously recognized as both a “car” and a “wheel”.\n\nI would be happy to revise my score if the author addresses these points.", "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": "zngDpmT4xB", "reviewer": "Reviewer_5RxY", "summary": "Authors propose the first text-guided instance segmentation framework for event streams, achieving open-vocabulary segmentation, and introduce four benchmark levels to validate the effectiveness of the proposed model.", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.  Proposes SEAL, the first open-vocabulary instance segmentation framework for event streams, achieving effective instance-level segmentation.\n2.  Introduces a decoupled training–inference strategy, featuring a complex training framework and a lightweight inference framework.\n3.  Constructs a scarce open-vocabulary segmentation dataset and designs four benchmark levels for training and evaluation.", "weaknesses": "1. Incomplete related work analysis. In the Open-World Events Understanding section (line 126), the authors discuss event-based vision foundation models such as EventBind and EventCLIP, but fail to include analyses of event-based vision MLLMs such as EventGPT and EventVL, which also fall within this domain.\n2. Dataset availability concerns. For open-vocabulary segmentation in event streams, the authors propose a complex training framework. However, the scarcity of open-vocabulary segmentation datasets in this domain remains a key limitation. The authors should clearly state whether their dataset will be publicly released.\n3. Fairness of experimental analysis. In the main experiments, several RGB-based models are used for comparison. It is unclear whether these models were fine-tuned on the proposed dataset (with event-image representations). If not, the comparison is unfair, and results after fine-tuning should be reported.\n4. Unclear experimental presentation. The authors introduce four benchmark levels for open-vocabulary segmentation, which vary significantly in word-level and semantic-level difficulty. In Table 1, the evaluation for each benchmark level should be clearly separated and 5. Dataset quality issues. For semantic-level open-vocabulary segmentation, the authors mainly rely on the DSEC dataset, where all scenes are road environments. These scenes are highly repetitive, and event streams lack color and texture information, leading to low semantic diversity and a risk of overfitting. The authors should clarify how they mitigate or address these issues.", "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)