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Gestures play an important role in our daily communications. However, recognizing and retrieving gestures in-the-wild is a challenging task which is not explored thoroughly in literature. In this paper, we explore the problem of identifying and retrieving gestures in a large-scale video dataset provided by the computer vision community and based on queries recorded in-the-wild. Our proposed pipeline, I3DEF, is based on the extraction of spatio-temporal features from intermediate layers of andoi:10.1145/3372278.3390723 dblp:conf/mir/ParianRSD20 fatcat:2jsdhqhg7jazbjfdpu54zfifq4