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DA-VLAD: DISCRIMINATIVE ACTION VECTOR OF LOCALLY AGGREGATED DESCRIPTORS FOR ACTION RECOGNITION

Citation Author(s):
Fiza Murtaza, Muhammad Haroon Yousaf, Sergio A Velastin
Submitted by:
Sergio Velastin
Last updated:
4 October 2018 - 11:16am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Sergio A Velastin
Paper Code:
1283
 

In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1\% and 80.1\% respectively.

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