PDAN: Pyramid Dilated Attention Network for Action Detection

5 Jan 2021  Β·  Rui Dai, Srijan Das, Luca Minciullo, Lorenzo Garattoni, Gianpiero Francesca, Francois Bremond Β·

Handling long and complex temporal information is an important challenge for action detection tasks. This challenge is further aggravated by densely distributed actions in untrimmed videos. Previous action detection methods fail in selecting the key temporal information in long videos. To this end, we introduce the Dilated Attention Layer (DAL). Compared to the previous temporal convolution layer, DAL allocates attentional weights to local frames in the kernel, which enables it to learn better local representation across time. Furthermore, we introduce Pyramid Dilated Attention Network (PDAN) which is built upon DAL. With the help of multiple DALs with different dilation rates, PDAN can model short-term and long-term temporal relations simultaneously by focusing on local segments at the level of low and high temporal receptive fields. This property enables PDAN to handle complex temporal relations between different action instances in long untrimmed videos. To corroborate the effectiveness and robustness of our method, we evaluate it on three densely annotated, multi-label datasets: MultiTHUMOS, Charades, and Toyota Smarthome Untrimmed (TSU) dataset. PDAN is able to outperform previous state-of-the-art methods on all these datasets.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Action Detection Charades PDAN (RGB+Flow) mAP 26.5 # 5
Temporal Action Localization MultiTHUMOS PDAN Average mAP 17.3 # 5
Action Detection Multi-THUMOS PDAN mAP 47.6 # 3
Action Detection TSU PDAN Frame-mAP 32.7 # 1