23,344 research outputs found
Good Features to Correlate for Visual Tracking
During the recent years, correlation filters have shown dominant and
spectacular results for visual object tracking. The types of the features that
are employed in these family of trackers significantly affect the performance
of visual tracking. The ultimate goal is to utilize robust features invariant
to any kind of appearance change of the object, while predicting the object
location as properly as in the case of no appearance change. As the deep
learning based methods have emerged, the study of learning features for
specific tasks has accelerated. For instance, discriminative visual tracking
methods based on deep architectures have been studied with promising
performance. Nevertheless, correlation filter based (CFB) trackers confine
themselves to use the pre-trained networks which are trained for object
classification problem. To this end, in this manuscript the problem of learning
deep fully convolutional features for the CFB visual tracking is formulated. In
order to learn the proposed model, a novel and efficient backpropagation
algorithm is presented based on the loss function of the network. The proposed
learning framework enables the network model to be flexible for a custom
design. Moreover, it alleviates the dependency on the network trained for
classification. Extensive performance analysis shows the efficacy of the
proposed custom design in the CFB tracking framework. By fine-tuning the
convolutional parts of a state-of-the-art network and integrating this model to
a CFB tracker, which is the top performing one of VOT2016, 18% increase is
achieved in terms of expected average overlap, and tracking failures are
decreased by 25%, while maintaining the superiority over the state-of-the-art
methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin
Building Combined Classifiers
This chapter covers different approaches that may be taken when building an
ensemble method, through studying specific examples of each approach from research
conducted by the authors. A method called Negative Correlation Learning illustrates a
decision level combination approach with individual classifiers trained co-operatively. The
Model level combination paradigm is illustrated via a tree combination method. Finally,
another variant of the decision level paradigm, with individuals trained independently
instead of co-operatively, is discussed as applied to churn prediction in the
telecommunications industry
Linear model for fast background subtraction in oligonucleotide microarrays
One important preprocessing step in the analysis of microarray data is
background subtraction. In high-density oligonucleotide arrays this is
recognized as a crucial step for the global performance of the data analysis
from raw intensities to expression values.
We propose here an algorithm for background estimation based on a model in
which the cost function is quadratic in a set of fitting parameters such that
minimization can be performed through linear algebra. The model incorporates
two effects: 1) Correlated intensities between neighboring features in the chip
and 2) sequence-dependent affinities for non-specific hybridization fitted by
an extended nearest-neighbor model.
The algorithm has been tested on 360 GeneChips from publicly available data
of recent expression experiments. The algorithm is fast and accurate. Strong
correlations between the fitted values for different experiments as well as
between the free-energy parameters and their counterparts in aqueous solution
indicate that the model captures a significant part of the underlying physical
chemistry.Comment: 21 pages, 5 figure
Contextual Knowledge Learning For Dialogue Generation
Incorporating conversational context and knowledge into dialogue generation
models has been essential for improving the quality of the generated responses.
The context, comprising utterances from previous dialogue exchanges, is used as
a source of content for response generation and as a means of selecting
external knowledge. However, to avoid introducing irrelevant content, it is key
to enable fine-grained scoring of context and knowledge. In this paper, we
present a novel approach to context and knowledge weighting as an integral part
of model training. We guide the model training through a Contextual Knowledge
Learning (CKL) process which involves Latent Vectors for context and knowledge,
respectively. CKL Latent Vectors capture the relationship between context,
knowledge, and responses through weak supervision and enable differential
weighting of context utterances and knowledge sentences during the training
process. Experiments with two standard datasets and human evaluation
demonstrate that CKL leads to a significant improvement compared with the
performance of six strong baseline models and shows robustness with regard to
reduced sizes of training sets.Comment: 9 pages, 4 figures, 6 tables. Accepted as a full paper in the main
conference by ACL 202
From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits
Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852
Using image morphing for memory-efficient impostor rendering on GPU
Real-time rendering of large animated crowds consisting thousands of virtual humans is important for several applications including simulations, games and interactive walkthroughs; but cannot be performed using complex polygonal models at interactive frame rates. For that reason, several methods using large numbers of pre-computed image-based representations, which are called as impostors, have been proposed. These methods take the advantage of existing programmable graphics hardware to compensate the computational expense while maintaining the visual fidelity. Making the number of different virtual humans, which can be rendered in real-time, not restricted anymore by the required computational power but by the texture memory consumed for the variety and discretization of their animations. In this work, we proposed an alternative method that reduces the memory consumption by generating compelling intermediate textures using image-morphing techniques. In order to demonstrate the preserved perceptual quality of animations, where half of the key-frames were rendered using the proposed methodology, we have implemented the system using the graphical processing unit and obtained promising results at interactive frame rates
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