8,299 research outputs found
Learnable PINs: Cross-Modal Embeddings for Person Identity
We propose and investigate an identity sensitive joint embedding of face and
voice. Such an embedding enables cross-modal retrieval from voice to face and
from face to voice. We make the following four contributions: first, we show
that the embedding can be learnt from videos of talking faces, without
requiring any identity labels, using a form of cross-modal self-supervision;
second, we develop a curriculum learning schedule for hard negative mining
targeted to this task, that is essential for learning to proceed successfully;
third, we demonstrate and evaluate cross-modal retrieval for identities unseen
and unheard during training over a number of scenarios and establish a
benchmark for this novel task; finally, we show an application of using the
joint embedding for automatically retrieving and labelling characters in TV
dramas.Comment: To appear in ECCV 201
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware
retinal transform (DART), that encodes the structural context using log-polar
grids for event cameras. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
- …