A Fine Granularity Object-Level Representation for Event Detection and Recounting

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
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Original languageEnglish
Pages (from-to)1450-1463
Journal / PublicationIEEE Transactions on Multimedia
Issue number6
Online published3 Dec 2018
Publication statusPublished - Jun 2019


Multimedia events such as "birthday party" usually involve the complex interaction between human and objects. Unlike actions and sports, these events rarely contain unique motion patterns to be vividly explored for recognition. To encode rich objects in the events, a common practice is to tag an individual video frame with object labels, represented as a vector signifying probabilities of object appearances. These vectors are then pooled across frames to obtain a video-level representation. The current practices suffer from two deficiencies due to the direct employment of deep convolutional neural network (DCNN) and standard feature pooling techniques. First, the use of max-pooling and softmax layers in DCNN overemphasize the primary object or scene in a frame, producing a sparse vector that overlooks the existence of secondary or small-size objects. Second, feature pooling by max or average operator over sparse vectors makes the video-level feature unpredictable in modeling the object composition of an event. To address these problems, this paper proposes a new video representation, named Object-VLAD, which treats each object equally and encodes them into a vector for multimedia event detection. Furthermore, the vector can be flexibly decoded to identify evidences such as key objects to recount the reason why a video is retrieved for an event of interest. Experiments conducted on MED13 and MED14 datasets verify the merit of Object-VLAD by consistently outperforming several state-of-the-arts in both event detection and recounting.

Research Area(s)

  • Multimedia event detection and recounting, object Encoding, search result reasoning