Name
Abstract | ||
|---|---|---|
Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action confidence in long and untrimmed real-world videos. In this paper, we propose an efficient and unified framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classification and action completeness regression for densely distributed proposals. In particular, the DBG consists of two modules: Temporal boundary classification (TBC) and Action-aware completeness regression (ACR). The TBC aims to provide two temporal boundary confidence maps by low-level two-stream features, while the ACR is designed to generate an action completeness score map by high-level action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to encode RGB and optical flow information, which helps to capture discriminative boundary and actionness features. Extensive experiments on popular benchmarks ActivityNet-1.3 and THUMOS14 demonstrate the superiority of DBG over the state-of-the-art proposal generator (e.g., MGG and BMN). Our code will be made available upon publication. |
Year | Venue | DocType |
|---|---|---|
2020 | AAAI | Conference |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuming Lin | 1 | 9 | 4.14 |
| Jian Li | 2 | 1 | 1.70 |
| Yabiao Wang | 3 | 21 | 7.05 |
| Ying Tai | 4 | 213 | 25.74 |
| Donghao Luo | 5 | 3 | 2.42 |
| Zhipeng Cui | 6 | 1 | 0.35 |
| Chengjie Wang | 7 | 43 | 19.03 |
| Jilin Li | 8 | 48 | 8.94 |
| Feiyue Huang | 9 | 226 | 41.86 |
| Rongrong Ji | 10 | 3616 | 189.98 |