Unified Embedding and Metric Learning for Zero-Exemplar Event Detection
arXiv:1705.02148
Abstract
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.
IEEE CVPR 2017
References in corpus (7)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Sequence to Sequence Learning with Neural Networks
- Two-Stream Convolutional Networks for Action Recognition in Videos
- Exploiting Similarities among Languages for Machine Translation
- Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques
- Rank Pooling for Action Recognition
- Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks