23,442 research outputs found
Predicting Out-of-View Feature Points for Model-Based Camera Pose Estimation
In this work we present a novel framework that uses deep learning to predict
object feature points that are out-of-view in the input image. This system was
developed with the application of model-based tracking in mind, particularly in
the case of autonomous inspection robots, where only partial views of the
object are available. Out-of-view prediction is enabled by applying scaling to
the feature point labels during network training. This is combined with a
recurrent neural network architecture designed to provide the final prediction
layers with rich feature information from across the spatial extent of the
input image. To show the versatility of these out-of-view predictions, we
describe how to integrate them in both a particle filter tracker and an
optimisation based tracker. To evaluate our work we compared our framework with
one that predicts only points inside the image. We show that as the amount of
the object in view decreases, being able to predict outside the image bounds
adds robustness to the final pose estimation.Comment: Submitted to IROS 201
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of
sequential recommendation. Existing neural sequential recommenders typically
adopt a generative way trained with Maximum Likelihood Estimation (MLE). When
context information (called factor) is involved, it is difficult to analyze
when and how each individual factor would affect the final recommendation
performance. For this purpose, we take a new perspective and introduce
adversarial learning to sequential recommendation. In this paper, we present a
Multi-Factor Generative Adversarial Network (MFGAN) for explicitly modeling the
effect of context information on sequential recommendation. Specifically, our
proposed MFGAN has two kinds of modules: a Transformer-based generator taking
user behavior sequences as input to recommend the possible next items, and
multiple factor-specific discriminators to evaluate the generated sub-sequence
from the perspectives of different factors. To learn the parameters, we adopt
the classic policy gradient method, and utilize the reward signal of
discriminators for guiding the learning of the generator. Our framework is
flexible to incorporate multiple kinds of factor information, and is able to
trace how each factor contributes to the recommendation decision over time.
Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed model over the state-of-the-art methods, in terms
of effectiveness and interpretability
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