Weakly-supervised video re-localization with multiscale attention model
Journal
AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
Pages
11077-11084
Date Issued
2020
Author(s)
Abstract
Video re-localization aims to localize a sub-sequence, called target segment, in an untrimmed reference video that is similar to a given query video. In this work, we propose an attention-based model to accomplish this task in a weakly supervised setting. Namely, we derive our CNN-based model without using the annotated locations of the target segments in reference videos. Our model contains three modules. First, it employs a pre-trained C3D network for feature extraction. Second, we design an attention mechanism to extract multiscale temporal features, which are then used to estimate the similarity between the query video and a reference video.Third, a localization layer detects where the target segment is in the reference video by determining whether each frame in the reference video is consistent with the query video. The resultant CNN model is derived based on the proposed coattention loss which discriminatively separates the target segment from the reference video. This loss maximizes the similarity between the query video and the target segment while minimizing the similarity between the target segment and the rest of the reference video. Our model can be modified to fully supervised re-localization. Our method is evaluated on a public dataset and achieves the state-of-the-art performance under both weakly supervised and fully supervised settings. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Other Subjects
Feature extraction; Attention mechanisms; Attention model; CNN models; Public dataset; Query video; Re-localization; State-of-the-art performance; Temporal features; Artificial intelligence
Type
conference paper