World, Americas, Special Report

[ms-marvelesold]

F with a ResNet-50 backbone without bells and whistles, which exceeds the previous state-of-the-art performance by 8.4 points. F of 62.4 among all existing methods. F metric can be further boosted to 63.3 by adopting a simple post-process technique. Moreover, we show the impressive results of 55.0 mAP and 43.7 mAP on A2D-Sentences and JHMDB-Sentences respectively, which significantly outperforms the previous methods by a large margin. Referring video object segmentation (R-VOS) aims to segment the target object in a video given a natural language description. This emerging topic has raised great attention in the research community and is expected to benefit many applications in a friendly and interactive way, e.g., video editing and video surveillance. Therefore, the model should have a strong ability to infer which object is referred and to perform accurate segmentation. To accomplish this task, the existing methods can be mainly categorized into two groups: (1) Bottom-up methods.




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