Until recently, the Video Instance Segmentation (VIS) community operated under the common belief that offline methods are generally superior to a frame by frame online processing. However, the recent success of online methods questions this belief, in particular, for challenging and long video sequences. We understand this work as a rebuttal of those recent observations and an appeal to the community to focus on dedicated near-online VIS approaches. To support our argument, we present a detailed analysis on different processing paradigms and the new end-to-end trainable NOVIS (Near-Online Video Instance Segmentation) method. Our transformer-based model directly predicts spatio-temporal mask volumes for clips of frames and performs instance tracking between clips via overlap embeddings. NOVIS represents the first near-online VIS approach which avoids any handcrafted tracking heuristics. We outperform all existing VIS methods by large margins and provide new state-of-the-art results on both YouTube-VIS (2019/2021) and the OVIS benchmarks.

近期的研究表明，针对具有挑战性和长视频序列而言，传统的离线方法不一定优于逐帧的在线处理。本研究提出了新的面向近在线视频实例分割（NOVIS）方法，并通过详细分析不同处理范式和新的端到端可训练模型，证明了该方法的优越性，并在YouTube-VIS（2019/2021）和OVIS基准测试中取得了新的最先进的结果。

NOVIS：面向端到端近在线视频实例分割的案例