1,806 research outputs found
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The role of HG in the analysis of temporal iteration and interaural correlation
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
Automatic analysis of facilitated taste-liking
This paper focuses on: (i) Automatic recognition of taste-liking
from facial videos by comparatively training and evaluating models
with engineered features and state-of-the-art deep learning
architectures, and (ii) analysing the classification results along the
aspects of facilitator type, and the gender, ethnicity, and personality
of the participants. To this aim, a new beverage tasting dataset
acquired under different conditions (human vs. robot facilitator
and priming vs. non-priming facilitation) is utilised. The experimental
results show that: (i) The deep spatiotemporal architectures
provide better classification results than the engineered feature
models; (ii) the classification results for all three classes of liking,
neutral and disliking reach F1 scores in the range of 71%-91%; (iii)
the personality-aware network that fuses participants’ personality
information with that of facial reaction features provides improved
classification performance; and (iv) classification results vary across
participant gender, but not across facilitator type and participant
ethnicity.EPSR
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Differentiating between networks associated with linguistic processing: A magnetoencephalographic investigation
Many models of reading differentiate between linguistic processing pathways. The studies reported within this thesis demonstrate how magnetoencephalography (MEG), together with the analysis technique Synthetic Aperture Magnetometry (SAM) can be used to investigate spectral power changes associated with different processing routes, with millisecond resolution. To isolate different linguistic processes, two approaches have been taken. In the first study task manipulations were used to focus attention on either the semantic or phonological nature of a stimulus. Results suggest a task-specific dissociation between the spatial-temporal dynamics of frontal (inferior frontal and BAIO) and temporal (BA20) / parietal (BA39/40) effects, which has implications for theories regarding the interaction between frontal “executive” systems and semantic / phonological “stores”. This approach was extended by investigating task-specific effects associated with the priming of the two tasks, prior to stimulus processing demands. Similiarities were observed between these effects and those observed during stimulus processing in terms of location, latency and frequency band, suggesting that task priming may allow investigation into the neural correlates of semantic and phonological processes in the absence of confounds associated with task demands and the choice of stimuli. The second approach involved the use of stimulus manipulations, within the context of a lexical decision task. Different types of word and nonword vary in terms of their semantic and phonological nature, and therefore in the extent to which they engage semantic and phonological processes. Differential effects were observed between the stimulus conditions, and interpreted in terms of differing degrees of semantic and phonological processing. As each of these approaches have methodological advantages and disadvantages, the results of the stimulus manipulation study are compared to those of the task manipulation study. Therefore, using three different approaches (task manipulations, stimulus manipulations, and task priming), MEG has been used to differentiate between networks associated with linguistic processing, within the domains of space, time and frequency
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
A Connectionist Theory of Phenomenal Experience
When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as
many of them have been doing recently, there are two fundamentally distinct approaches available. Either
consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or
it is to be explained in terms of the computational processes defined over these vehicles. We call versions of
these two approaches vehicle and process theories of consciousness, respectively. However, while there may
be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because
of the influence exerted, on the one hand, by a large body of research which purports to show that the
explicit representation of information in the brain and conscious experience are dissociable, and on the
other, by the classical computational theory of mind – the theory that takes human cognition to be a species
of symbol manipulation. But two recent developments in cognitive science combine to suggest that a
reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the
experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer
reasonable to assume that the dissociability of conscious experience and explicit representation has been
adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in
cognitive science as it once was. It now has a lively competitor in the form of connectionism; and
connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory
of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It
takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit
representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some
common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways
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