1,299 research outputs found
Move Forward and Tell: A Progressive Generator of Video Descriptions
We present an efficient framework that can generate a coherent paragraph to
describe a given video. Previous works on video captioning usually focus on
video clips. They typically treat an entire video as a whole and generate the
caption conditioned on a single embedding. On the contrary, we consider videos
with rich temporal structures and aim to generate paragraph descriptions that
can preserve the story flow while being coherent and concise. Towards this
goal, we propose a new approach, which produces a descriptive paragraph by
assembling temporally localized descriptions. Given a video, it selects a
sequence of distinctive clips and generates sentences thereon in a coherent
manner. Particularly, the selection of clips and the production of sentences
are done jointly and progressively driven by a recurrent network -- what to
describe next depends on what have been said before. Here, the recurrent
network is learned via self-critical sequence training with both sentence-level
and paragraph-level rewards. On the ActivityNet Captions dataset, our method
demonstrated the capability of generating high-quality paragraph descriptions
for videos. Compared to those by other methods, the descriptions produced by
our method are often more relevant, more coherent, and more concise.Comment: Accepted by ECCV 201
Evaluation of wind tunnel performance testings of an advanced 45 deg swept 8-bladed propeller at Mach numbers from 0.45 to 0.85
The increased emphasis of fuel conservation in the world and the rapid increase in the cost of jet fuel has stimulated a series of studies of both conventional and unconventional propulsion systems for commercial aircraft. The results of these studies indicate that a fuel saving of 15 to 30 percent may be realized by the use of an advanced high-speed turboprop (Prop-Fan) compared to aircraft equipped with high bypass turbofan engines of equivalent technology. The Prop-Fan propulsion system is being investigated as part of the NASA Aircraft Energy Efficient Program. This effort includes the wind tunnel testing of a series of 8 and 10-blade Prop-Fan models incorporate swept blades. Test results indicate efficiency levels near the goal of 80 percent at Mach 0.8 cruise and an altitude of 10.67 km (35,000 ft). Each successive swept model has shown improved efficiency relative to the straight blade model. The fourth model, with 45 deg swept blades reported herein, shows a net efficiency of 78.2 at the design point with a power loading of 301 kW/sq meter and a tip speed of 243.8 m/sec (800 ft/sec.)
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition approaches remains low. We propose a model for action segmentation
which combines low-level spatiotemporal features with a high-level segmental
classifier. Our spatiotemporal CNN is comprised of a spatial component that
uses convolutional filters to capture information about objects and their
relationships, and a temporal component that uses large 1D convolutional
filters to capture information about how object relationships change across
time. These features are used in tandem with a semi-Markov model that models
transitions from one action to another. We introduce an efficient constrained
segmental inference algorithm for this model that is orders of magnitude faster
than the current approach. We highlight the effectiveness of our Segmental
Spatiotemporal CNN on cooking and surgical action datasets for which we observe
substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important
mathematical error and made the section on segmental inference cleare
Conditional Image-Text Embedding Networks
This paper presents an approach for grounding phrases in images which jointly
learns multiple text-conditioned embeddings in a single end-to-end model. In
order to differentiate text phrases into semantically distinct subspaces, we
propose a concept weight branch that automatically assigns phrases to
embeddings, whereas prior works predefine such assignments. Our proposed
solution simplifies the representation requirements for individual embeddings
and allows the underrepresented concepts to take advantage of the shared
representations before feeding them into concept-specific layers. Comprehensive
experiments verify the effectiveness of our approach across three phrase
grounding datasets, Flickr30K Entities, ReferIt Game, and Visual Genome, where
we obtain a (resp.) 4%, 3%, and 4% improvement in grounding performance over a
strong region-phrase embedding baseline.Comment: ECCV 2018 accepted pape
Learning Visual Question Answering by Bootstrapping Hard Attention
Attention mechanisms in biological perception are thought to select subsets
of perceptual information for more sophisticated processing which would be
prohibitive to perform on all sensory inputs. In computer vision, however,
there has been relatively little exploration of hard attention, where some
information is selectively ignored, in spite of the success of soft attention,
where information is re-weighted and aggregated, but never filtered out. Here,
we introduce a new approach for hard attention and find it achieves very
competitive performance on a recently-released visual question answering
datasets, equalling and in some cases surpassing similar soft attention
architectures while entirely ignoring some features. Even though the hard
attention mechanism is thought to be non-differentiable, we found that the
feature magnitudes correlate with semantic relevance, and provide a useful
signal for our mechanism's attentional selection criterion. Because hard
attention selects important features of the input information, it can also be
more efficient than analogous soft attention mechanisms. This is especially
important for recent approaches that use non-local pairwise operations, whereby
computational and memory costs are quadratic in the size of the set of
features.Comment: ECCV 201
Single microtubules and small networks become significantly stiffer on short time-scales upon mechanical stimulation
The transfer of mechanical signals through cells is a complex phenomenon. To
uncover a new mechanotransduction pathway, we study the frequency-dependent
transport of mechanical stimuli by single microtubules and small networks in a
bottom-up approach using optically trapped beads as anchor points. We
interconnected microtubules to linear and triangular geometries to perform
micro-rheology by defined oscillations of the beads relative to each other. We
found a substantial stiffening of single filaments above a characteristic
transition frequency of 1-30 Hz depending on the filament's molecular
composition. Below this frequency, filament elasticity only depends on its
contour and persistence length. Interestingly, this elastic behavior is
transferable to small networks, where we found the surprising effect that
linear two filament connections act as transistor-like, angle dependent
momentum filters, whereas triangular networks act as stabilizing elements.
These observations implicate that cells can tune mechanical signals by temporal
and spatial filtering stronger and more flexibly than expected
The exit velocity of a compressed air cannon
The use of compressed air cannons in an undergraduate lab provides a way to
illustrate the cooperation of diverse physics concepts, such as conservation of
momentum, the work-kinetic energy theorem, expansion of gas, air drag, and
elementary Newtonian mechanics. However, recent proposals have disagreed as to
whether the expansion of the gas in the cannon should be modeled as an
adiabatic or an isothermal process. We built an air cannon that utilized a
diaphragm valve to release our pressurized gas and found that neither model
accurately predicted the exit velocity of our projectile. We present a new
model, based on the flow of air through the valve, that is in much better
agreement with our data
An optically actuated surface scanning probe
We demonstrate the use of an extended, optically trapped probe that is capable of imaging surface topography with nanometre precision, whilst applying ultra-low, femto-Newton sized forces. This degree of precision and sensitivity is acquired through three distinct strategies. First, the probe itself is shaped in such a way as to soften the trap along the sensing axis and stiffen it in transverse directions. Next, these characteristics are enhanced by selectively position clamping independent motions of the probe. Finally, force clamping is used to refine the surface contact response. Detailed analyses are presented for each of these mechanisms. To test our sensor, we scan it laterally over a calibration sample consisting of a series of graduated steps, and demonstrate a height resolution of ∼ 11 nm. Using equipartition theory, we estimate that an average force of only ∼ 140 fN is exerted on the sample during the scan, making this technique ideal for the investigation of delicate biological samples
Chimeric carrier proteins for targeted delivery of tumor antigens to professional antigen presenting cells
Ontogeny of ependymoglial cells lining the third ventricle in mice.
During hypothalamic development, the germinative neuroepithelium gives birth to diverse neural cells that regulate numerous physiological functions in adulthood.
Here, we studied the ontogeny of ependymal cells in the mouse mediobasal hypothalamus using the BrdU approach and publicly available single-cell RNAseq datasets.
We observed that while typical ependymal cells are mainly produced at E13, tanycyte birth depends on time and subtypes and lasts up to P8. Typical ependymocytes and β tanycytes are the first to arise at the top and bottom of the dorsoventral axis around E13, whereas α tanycytes emerge later in development, generating an outside-in dorsoventral gradient along the third ventricle. Additionally, α tanycyte generation displayed a rostral-to-caudal pattern. Finally, tanycytes mature progressively until they reach transcriptional maturity between P4 and P14.
Altogether, this data shows that ependyma generation differs in time and distribution, highlighting the heterogeneity of the third ventricle
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