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
Learning joint feature adaptation for zero-shot recognition
Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances. In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly we propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration we design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement. We test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%
TagBook: A Semantic Video Representation without Supervision for Event Detection
We consider the problem of event detection in video for scenarios where only
few, or even zero examples are available for training. For this challenging
setting, the prevailing solutions in the literature rely on a semantic video
representation obtained from thousands of pre-trained concept detectors.
Different from existing work, we propose a new semantic video representation
that is based on freely available social tagged videos only, without the need
for training any intermediate concept detectors. We introduce a simple
algorithm that propagates tags from a video's nearest neighbors, similar in
spirit to the ones used for image retrieval, but redesign it for video event
detection by including video source set refinement and varying the video tag
assignment. We call our approach TagBook and study its construction,
descriptiveness and detection performance on the TRECVID 2013 and 2014
multimedia event detection datasets and the Columbia Consumer Video dataset.
Despite its simple nature, the proposed TagBook video representation is
remarkably effective for few-example and zero-example event detection, even
outperforming very recent state-of-the-art alternatives building on supervised
representations.Comment: accepted for publication as a regular paper in the IEEE Transactions
on Multimedi