In this work, for the first time, we propose a proposal-free Temporal Action detection model with Global Segmentation mask (TAGS). Our core idea is to learn a global segmentation mask of each action instance jointly at the full video length. The TAGS model differs significantly from the ...
Abstract 全景分割的目的是在单一训练体系结构中统一实例分割和语义分割。论文中提出了一个快速和稳健的LiDAR点云全景分割框架(Panoptic-PolarNet)。 文中使用了极坐标鸟瞰图(BEV)表示在单个推理网络中学习语义分割和类无关的实例聚类,这样能够避免城市街道场景中实例之间
为了解决现有TAD方法将定位误差累计到分类模块的误差传播问题和需要大量数据训练的问题,结合SSL,提出了proposal-free temporal mask learning和伪标签pseudo-label结合的方案。 总体模型 图2 SPOT总体模型 总体模型包含两个部分:video snippet embeding和TAD head Propose a new parallel classifcation and localization...
In this work, we propose a Proposal-Free Network (PFN) to address the instance-level object segmentation problem, which outputs the numbers of instances of different categories and the pixel-level information on 1) the coordinates of the instance bounding box each pixel belongs to, and 2) the...
While previous works have tackled this task by a propose-and-rank approach, we introduce a more efficient, end-to-end trainable, and {\em proposal-free approach} that relies on three key components: a dynamic filter to transfer language information to the visual domain, a new loss function...
Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG.In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, ...
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First single-stage proposal-free framework for Semi-Supervised Temporal Action Detection (SS-TAD) task. Being single-stage, it does not suffers from the notorius Proposal Error Propagation problem. Proposed a novel pre-text task for Action Detection based on the notion of Random Foreground. A ...
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