349 citations · 763 across the 12 of their papers we have counts for
6 papers · 1 filter
Efficient Diverse Ensemble for Discriminative Co-Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Ensemble discriminative tracking utilizes a committee of classifiers, to label data samples, which are in turn, used for retraining the tracker to localize the target using the col…
Neural Sequence Model Training via -divergence Minimization
Sotetsu Koyamada, Yuta Kikuchi, Atsunori Kanemura +2
We propose a new neural sequence model training method in which the objective function is defined by -divergence. We demonstrate that the objective function generalizes the maxi…
Active Collaborative Ensemble Tracking
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba +1
A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize t…
Efficient Version-Space Reduction for Visual Tracking
Kourosh Meshgi, Shigeyuki Oba, Shin Ishii
Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is u…
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama +1
We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial l…
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba +1
Adaptive tracking-by-detection approaches are popular for tracking arbitrary objects. They treat the tracking problem as a classification task and use online learning techniques to…