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Event-SAMA: Language-Grounded Video Object Segmentation on Neuromorphic Event Streams

Paper recorded by Signals 4 on 2026-10-29 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.CL · 自然语言处理 · first seen 2026-10-30

Abstract

Event cameras offer microsecond temporal resolution, high dynamic range and no motion blur, but referring video object segmentation (RVOS) lacks event-stream datasets and models. We introduce Event-SAMA-239K, a synthetic-event conversion of the SAMA-239K grounded video-chat corpus and all additional SAMA training data: 30,650 video clips and 392,162 images. Optical-flow, FILM or saccade upsampling

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#77 most recent of 628 cs.CL papers we have recorded · ↑ newer: From the Transformer to a Formal Semantics of Coordinated Transformati · ↓ older: Incentivizing Self-Learning in Large Language Models via Reinforcement

Cite this page: Event-SAMA: Language-Grounded Video Object Segmentation on Neuromorphic Event Streams: the #77 most recent of 628 cs.CL papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/event-sama-language-grounded-video-object-segmentation-on-neuromorphic-event-str.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json

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