OpenESS: Event-Based Semantic Scene Understanding with Open Vocabularies
Lingdong Kong, Youquan Liu, Lai Xing Ng, Benoit R. Cottereau, Wei Tsang Ooi
摘要
Zero-Shot Semantic Segmentation "driveable" (Adjective) "walkable" (Adjective) "car" (Fine-Grained) "manmade" (Coarse) "flat" (Coarse) "barrier" (Fine-Grained) Back Build Road Car Pole Veg Wall Figure 1. Open-vocabulary event-based semantic segmentation (OpenESS). Our framework is capable of performing zero-shot semantic segmentation of event data streams with open vocabularies. Given raw events and text prompts as inputs, OpenESS outputs semantically coherent open-world predictions across various adjective, fine-grained, and coarse categories. The last three columns show the languageguided attention maps where regions of a high similarity score to the given text prompts are highlighted. Best viewed in colors.
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引用它的顶会 Paper15
- FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational FrequenciesDongyue Lu, Lingdong Kong, Gim Hee Lee, Camille Simon Chane 等NeurIPS 2025 · 被引用 13 次
- Talk2Event: Grounded Understanding of Dynamic Scenes from Event CamerasLingdong Kong, Dongyue Lu, Alan Liang, Rong Li 等NeurIPS 2025 · 被引用 7 次
- EventFlash: Towards Efficient MLLMs for Event-Based VisionShaoyu Liu, Jianing Li, Guanghui Zhao, Yunjian Zhang 等ICLR 2026 · 被引用 5 次
- Segment Any Events with LanguageSeungjun Lee, Gim Hee LeeICLR 2026 · 被引用 3 次
- Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network ApproachHebei Li, Yansong Peng, Jiahui Yuan, Peixi Wu 等AAAI 2025 · 被引用 3 次
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