text stringlengths 27 153 | label stringlengths 27 153 | id int64 0 40k |
|---|---|---|
point. box the The an values and objectness each outputs bounding network for offset map | The network outputs an objectness map and the bounding box offset values for each point. | 35,200 |
KITTI the evaluation the dataset quantitative evaluations help We with server. carried of out | We carried out quantitative evaluations with the help of the KITTI dataset evaluation server. | 35,201 |
extract and spatial both for pose network features frames. we From the features image | From the spatial network we extract image features and pose features for both frames. | 35,202 |
serve model that (TFF). Temporal temporal Fields These for as Flow input our predicts features | These features serve as input for our temporal model that predicts Temporal Flow Fields (TFF). | 35,203 |
formulate representation similarity novel to joints. of This allows detected a measure | This novel representation allows to formulate a similarity measure of detected joints. | 35,204 |
as driving, crucial estimation are autonomous pose technologies. self-localization/camera applications scene For and parsing such | For applications such as autonomous driving, self-localization/camera pose estimation and scene parsing are crucial technologies. | 35,205 |
propose problems In paper, tackle two this these to framework a simultaneously. unified we | In this paper, we propose a unified framework to tackle these two problems simultaneously. | 35,206 |
semantically-labeled. Both point are and the images the clouds | Both the point clouds and the images are semantically-labeled. | 35,207 |
sensors. Each has highly from video frame motion ground truth pose accurate | Each video frame has ground truth pose from highly accurate motion sensors. | 35,208 |
studies of proposed various effectiveness ablation performed, the the Finally, are system. demonstrate which | Finally, various ablation studies are performed, which demonstrate the effectiveness of the proposed system. | 35,209 |
learning alter behavioral is perception. evidence and physiological that category can This | This is behavioral and physiological evidence that category learning can alter perception. | 35,210 |
net this a We effect. neural model sketch for | We sketch a neural net model for this effect. | 35,211 |
of training be Deep to large learning effective. data requires amounts | Deep learning requires large amounts of training data to be effective. | 35,212 |
iteratively on training, currently predicted the based we errors of clicks add During the segmentation. | During training, we iteratively add clicks based on the errors of the currently predicted segmentation. | 35,213 |
data these and an domain-specific expensive laborious to is Labeling names task. color learn | Labeling data to learn these domain-specific color names is an expensive and laborious task. | 35,214 |
article learn aim labeled color from weakly data. names Therefore, to in we this | Therefore, in this article we aim to learn color names from weakly labeled data. | 35,215 |
we For branch naming network. an color attention purpose, the this add to | For this purpose, we add an attention branch to the color naming network. | 35,216 |
attention pixel-wise used The predictions modulate the to of branch the color is network. naming | The attention branch is used to modulate the pixel-wise color naming predictions of the network. | 35,217 |
the illustrate experiments, relevant branch we the that attention regions. identifies In correctly | In experiments, we illustrate that the attention branch correctly identifies the relevant regions. | 35,218 |
apply of model to self-consistency splices. detecting We task localizing this image and the | We apply this self-consistency model to the task of detecting and localizing image splices. | 35,219 |
optimization experiences Thereby, techniques. important parameter report detailed initialization we concerning and | Thereby, we report important experiences concerning detailed parameter initialization and optimization techniques. | 35,220 |
very they flexible constraints Inequality exact because prior not assume knowledge. are do | Inequality constraints are very flexible because they do not assume exact prior knowledge. | 35,221 |
evaluation conditions. non-studio This us allows present qualitative to in and extension quantitative | This extension allows us to present quantitative and qualitative evaluation in non-studio conditions. | 35,222 |
idea issues address challenging simple This style transfer can methods. existing the effectively in | This simple idea can effectively address the challenging issues in existing style transfer methods. | 35,223 |
we approach. loss, also progressive a on proposed present optimization the feature-domain Based | Based on the proposed loss, we also present a progressive feature-domain optimization approach. | 35,224 |
deadline increases The as bets very rapidly comes betting near. for the of amount | The amount of bets increases very rapidly as the deadline for betting comes near. | 35,225 |
bets gives the bettor which value Each on largest of the expectation benefit. a horse | Each bettor bets on a horse which gives the largest expectation value of the benefit. | 35,226 |
alternative be Using simulations, biased. we approximate-Bayesian (ABC) computation one to on find method based | Using simulations, we find one alternative method based on approximate-Bayesian computation (ABC) to be biased. | 35,227 |
we for to argument, methods them. acquiring improve need and Yet, of apply models | Yet, to improve and apply models of argument, we need methods for acquiring them. | 35,228 |
out fallen of post-processing more In works has favour. however, recent CRF | In more recent works however, CRF post-processing has fallen out of favour. | 35,229 |
can CRFs All backpropagation. optimized using of parameters convolutional easily the be | All parameters of the convolutional CRFs can easily be optimized using backpropagation. | 35,230 |
implementation further research CRF our publicly make facilitating we available. To | To facilitating further CRF research we make our implementation publicly available. | 35,231 |
learning new \emph{e.g. very with a are Humans little of capable concept fine-grained supervision, | Humans are capable of learning a new fine-grained concept with very little supervision, \emph{e.g. | 35,232 |
is collecting challenging. datasets annotated such However, | However, collecting such annotated datasets is challenging. | 35,233 |
complete existing entries. new We databases database by propose to generating | We propose to complete existing databases by generating new database entries. | 35,234 |
(GAN) the generative training algorithm process. networks adopts training adversarial Our | Our training algorithm adopts the generative adversarial networks (GAN) training process. | 35,235 |
facial Robust localization partially are remains challenging landmark faces occluded. task when a | Robust facial landmark localization remains a challenging task when faces are partially occluded. | 35,236 |
on of We dataset RICPR COFW. the evaluate challenging | We evaluate RICPR on the challenging dataset of COFW. | 35,237 |
weights the then fine We propagation. role tune using player back | We then fine tune the player role weights using back propagation. | 35,238 |
and The the interpretability hierarchical of ensures representation. integrity the the group architecture | The hierarchical architecture ensures the interpretability and the integrity of the group representation. | 35,239 |
RMS error. values show of results an improvement of terms Our in prediction | Our results show an improvement in terms of RMS values of prediction error. | 35,240 |
counting paper, In model this to a introduce (PMC). we novel solve algorithm projected | In this paper, we introduce a novel algorithm to solve projected model counting (PMC). | 35,241 |
primal the small of algorithm treewidth input of instance. exploits Our graph the | Our algorithm exploits small treewidth of the primal graph of the input instance. | 35,242 |
deep recently they been incorporated As architectures. such, into have | As such, they have recently been incorporated into deep architectures. | 35,243 |
the kernel boost method MvCCDA. performance develop We a of to further MvCCDA of | We develop a kernel method of MvCCDA to further boost the performance of MvCCDA. | 35,244 |
kernel optimization analysis for MvCCDA extension, and completeness. presented Beyond are of complexity also | Beyond kernel extension, optimization and complexity analysis of MvCCDA are also presented for completeness. | 35,245 |
years. recent analysis become under has the Medical image topic spotlight a in | Medical image analysis has become a topic under the spotlight in recent years. | 35,246 |
medical in of machine a concerning There the is research image usage learning. significant progress | There is a significant progress in medical image research concerning the usage of machine learning. | 35,247 |
and answers questions awaiting still problems and solutions, numerous there However, are respectively. | However, there are still numerous questions and problems awaiting answers and solutions, respectively. | 35,248 |
physical subject. extremely is an optical of relevant of layer the networks Supervision | Supervision of the physical layer of optical networks is an extremely relevant subject. | 35,249 |
in are complexity, Characteristics simulated as time, processing specificity, such and environments. analysed sensitivity, | Characteristics such as sensitivity, specificity, processing time, and complexity, are analysed in simulated environments. | 35,250 |
for an There explain is their to demand algorithms outcomes. increasing | There is an increasing demand for algorithms to explain their outcomes. | 35,251 |
is So produced no far, ranking explains rankings method there the algorithm. a that by | So far, there is no method that explains the rankings produced by a ranking algorithm. | 35,252 |
in generations. network problem deep a focus neural We on training of the | We focus on the problem of training a deep neural network in generations. | 35,253 |
of approach. also Model the and extraction ensemble feature our transfer verify effectiveness | Model ensemble and transfer feature extraction also verify the effectiveness of our approach. | 35,254 |
Carlo algorithm a on Monte inference powerful manifolds. Bayesian Geodesic non-Euclidean is (gMC) for | Geodesic Monte Carlo (gMC) is a powerful algorithm for Bayesian inference on non-Euclidean manifolds. | 35,255 |
object are applications. one Human class interesting numerous faces with | Human faces are one interesting object class with numerous applications. | 35,256 |
problem images deblurring In by this exploiting we the address of structures. face facial paper, | In this paper, we address the problem of deblurring face images by exploiting facial structures. | 35,257 |
affect can the Inter-pathologist test diagnostic accuracy. results in variability | Inter-pathologist variability in the test results can affect diagnostic accuracy. | 35,258 |
in have Multi-label manifested learning machine problems various applications. themselves learning | Multi-label learning problems have manifested themselves in various machine learning applications. | 35,259 |
challenges. framework space Recently, transformation these targeting proposed a label been (LST) has | Recently, a label space transformation (LST) framework has been proposed targeting these challenges. | 35,260 |
(DLST) paper, transformation this In distribution-based we model. propose space label a | In this paper, we propose a distribution-based label space transformation (DLST) model. | 35,261 |
the Consequently, using performance. classifier better codes trained dense latent multi-label yields | Consequently, multi-label classifier trained using the dense latent codes yields better performance. | 35,262 |
The the information of to enables fill out leverage DLST correlations. additional about distribution label | The leverage of distribution enables DLST to fill out additional information about the label correlations. | 35,263 |
employed to ML-KNN the label Then is transformed vector the code. original latent recover from | Then ML-KNN is employed to recover the original label vector from the transformed latent code. | 35,264 |
human the paper, associations faces we In voices. and study this between | In this paper, we study the associations between human faces and voices. | 35,265 |
outcome which increases poor leads rates. mortality treatment This the to | This leads to poor treatment outcome which increases the mortality rates. | 35,266 |
Tehran tested Center. Service Emergency system as in The been has prototype designed a | The designed system has been tested as a prototype in Tehran Emergency Service Center. | 35,267 |
to in changes This leads contact patterns. | This leads to changes in contact patterns. | 35,268 |
population demonstrate negative the at level. models potential to consequences the use network We epidemic | We use epidemic network models to demonstrate the potential negative consequences at the population level. | 35,269 |
through take models. structure of account the several into network population social the We | We take into account the social structure of the population through several network models. | 35,270 |
infectious evolves, the epidemic their themselves distance susceptible individuals contacts. from As may | As the epidemic evolves, susceptible individuals may distance themselves from their infectious contacts. | 35,271 |
replace social ties. Some lost seeking individuals by connections their new | Some individuals replace their lost social connections by seeking new ties. | 35,272 |
real-world in same can negative effect Moreover, arise networks. the | Moreover, the same negative effect can arise in real-world networks. | 35,273 |
importance findings the measures analysis careful models. preventive in epidemic of of highlight These | These findings highlight the importance of careful analysis of preventive measures in epidemic models. | 35,274 |
tasks. related among generalization sharing knowledge improves learning by Multi-task performance | Multi-task learning improves generalization performance by sharing knowledge among related tasks. | 35,275 |
segmentation detection proposed using architecture evaluated We a combination two and on our of datasets. | We evaluated our proposed architecture on a combination of detection and segmentation using two datasets. | 35,276 |
how demonstrate wild with limited Experiments birds CNN from representations general our datasets. learns | Experiments with wild birds demonstrate how our CNN learns general representations from limited datasets. | 35,277 |
finding constraint to a is problems. solutions QGS satisfaction systematic approach of | QGS is a systematic approach to finding solutions of constraint satisfaction problems. | 35,278 |
provide QGS better shows with Comparison that significantly classification. neural network trained networks classifiers other | Comparison with other neural network classifiers shows that QGS trained networks provide significantly better classification. | 35,279 |
generated. material Gene genetic is duplication which through is a new mechanism major | Gene duplication is a major mechanism through which new genetic material is generated. | 35,280 |
tasks is an important computer different and vision for Texture cue applications. | Texture is an important cue for different computer vision tasks and applications. | 35,281 |
efficient yet Pattern the (LBP) of one considered is descriptors. Local Binary best texture | Local Binary Pattern (LBP) is considered one of the best yet efficient texture descriptors. | 35,282 |
noise. sensitivity notable limitations, However, mostly some to has the LBP | However, LBP has some notable limitations, mostly the sensitivity to noise. | 35,283 |
distributions models. are in building a block Gaussian used generative key commonly as many | Gaussian distributions are commonly used as a key building block in many generative models. | 35,284 |
However, well their networks. explored deep applicability been has not in | However, their applicability has not been well explored in deep networks. | 35,285 |
multivariate can model the functions be data. dependence copula to structure used of Empirical | Empirical copula functions can be used to model the dependence structure of multivariate data. | 35,286 |
algorithm numerically benefits the computational theoretically and The and error approximation of is assessed. | The computational benefits and approximation error of the algorithm is theoretically and numerically assessed. | 35,287 |
we for network (DR-ResNet) based counting. deeply-recursive crowd propose a Consequently, blocks on ResNet | Consequently, we propose a deeply-recursive network (DR-ResNet) based on ResNet blocks for crowd counting. | 35,288 |
Beijing data video-monitoring from Besides, we dataset a bus the new generate station. of | Besides, we generate a new dataset from the video-monitoring data of Beijing bus station. | 35,289 |
MEnet several public desirable that Tests performance. show benchmarks has on achieved | Tests on several public benchmarks show that MEnet has achieved desirable performance. | 35,290 |
data tera-voxel current brain interest. neuroanatomy much of light-microscopic is Whole sets using | Whole brain neuroanatomy using tera-voxel light-microscopic data sets is of much current interest. | 35,291 |
tasks. hence They for facial important analysis various are | They are hence important for various facial analysis tasks. | 35,292 |
utilize the shape and to appearance facial ways in information. the They differ | They differ in the ways to utilize the facial appearance and shape information. | 35,293 |
models explicitly global build holistic represent and appearance to shape The the methods information. facial | The holistic methods explicitly build models to represent the global facial appearance and shape information. | 35,294 |
the models. appearance local model build global shape explicitly CLMs The but the leverage | The CLMs explicitly leverage the global shape model but build the local appearance models. | 35,295 |
facial shape capture appearance implicitly information. regression-based methods and The | The regression-based methods implicitly capture facial shape and appearance information. | 35,296 |
theories as discuss we differences. category, each as algorithms well their their within For underlying | For algorithms within each category, we discuss their underlying theories as well as their differences. | 35,297 |
strengths out and their weaknesses. on evaluations, the point respective we Based | Based on the evaluations, we point out their respective strengths and weaknesses. | 35,298 |
section There separate the deep review to learning-based is also a latest algorithms. | There is also a separate section to review the latest deep learning-based algorithms. | 35,299 |
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