13 research outputs found
Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
Event detection in unconstrained videos is conceived as a content-based video
retrieval with two modalities: textual and visual. Given a text describing a
novel event, the goal is to rank related videos accordingly. This task is
zero-exemplar, no video examples are given to the novel event.
Related works train a bank of concept detectors on external data sources.
These detectors predict confidence scores for test videos, which are ranked and
retrieved accordingly. In contrast, we learn a joint space in which the visual
and textual representations are embedded. The space casts a novel event as a
probability of pre-defined events. Also, it learns to measure the distance
between an event and its related videos.
Our model is trained end-to-end on publicly available EventNet. When applied
to TRECVID Multimedia Event Detection dataset, it outperforms the
state-of-the-art by a considerable margin.Comment: IEEE CVPR 201
Are All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-Based Video Retrieval
In this paper we tackle the cross-modal video retrieval problem and, more
specifically, we focus on text-to-video retrieval. We investigate how to
optimally combine multiple diverse textual and visual features into feature
pairs that lead to generating multiple joint feature spaces, which encode
text-video pairs into comparable representations. To learn these
representations our proposed network architecture is trained by following a
multiple space learning procedure. Moreover, at the retrieval stage, we
introduce additional softmax operations for revising the inferred query-video
similarities. Extensive experiments in several setups based on three
large-scale datasets (IACC.3, V3C1, and MSR-VTT) lead to conclusions on how to
best combine text-visual features and document the performance of the proposed
network. Source code is made publicly available at:
https://github.com/bmezaris/TextToVideoRetrieval-TtimesVComment: Accepted for publication; to be included in Proc. ECCV Workshops
2022. The version posted here is the "submitted manuscript" versio