In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-ofthe-art performance on the publicly available FCVID and YLI-MED datasets 1 .
This paper presents a fully-automatic method that combines video concept detection and textual query analysis in order to solve the problem of ad-hoc video search. We present a set of NLP steps that cleverly analyse different parts of the query in order to convert it to related semantic concepts, we propose a new method for transforming concept-based keyframe and query representations into a common semantic embedding space, and we show that our proposed combination of concept-based representations with their corresponding semantic embeddings results to improved video search accuracy. Our experiments in the TRECVID AVS 2016 and the Video Search 2008 datasets show the effectiveness of the proposed method compared to other similar approaches.
In this paper, the problem of unlabeled video retrieval using textual queries is addressed. We present an extended dual encoding network which makes use of more than one encodings of the visual and textual content, as well as two different attention mechanisms. The latter serve the purpose of highlighting temporal locations in every modality that can contribute more to effective retrieval. The different encodings of the visual and textual inputs, along with early/late fusion strategies, are examined for further improving performance. Experimental evaluations and comparisons with state-of-the-art methods document the merit of the proposed network.
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