text stringlengths 27 153 | label stringlengths 27 153 | id int64 0 40k |
|---|---|---|
crowds a dense even Counting is for demanding humans. task in people | Counting people in dense crowds is a demanding task even for humans. | 37,700 |
appearance to variability primarily large the in people. of due This is | This is primarily due to the large variability in appearance of people. | 37,701 |
of seen a only Often blobs. people as are bunch | Often people are only seen as a bunch of blobs. | 37,702 |
clutter further pose difficulty. variations Occlusions, the background and compound | Occlusions, pose variations and background clutter further compound the difficulty. | 37,703 |
In semantics scenario, the requires a identifying of and spatial context larger scene. this person | In this scenario, identifying a person requires larger spatial context and semantics of the scene. | 37,704 |
to prediction the we of Hence, correct the initial CNN. feedback propose top-down | Hence, we propose top-down feedback to correct the initial prediction of the CNN. | 37,705 |
are various bottom-up the the top-down from fed of CNN layers network. to Features | Features from various layers of the bottom-up CNN are fed to the top-down network. | 37,706 |
of crucial. step is process, dermoscopic the first in Segmentation is accuracy images thus this | Segmentation of dermoscopic images is the first step in this process, thus accuracy is crucial. | 37,707 |
tackle paper problem. to an re-identification approach This presents the | This paper presents an approach to tackle the re-identification problem. | 37,708 |
for person are machine to More datasets models train and more learning available re-identification. | More and more datasets are available to train machine learning models for person re-identification. | 37,709 |
These positions, season, i.e. in conditions: numbers, location, cameras camera in datasets size, vary | These datasets vary in conditions: cameras numbers, camera positions, location, season, in size, i.e. | 37,710 |
different number of identities. of images, number | number of images, number of different identities. | 37,711 |
labeling: annotated Finally there in while are are with others datasets not. attributes | Finally in labeling: there are datasets annotated with attributes while others are not. | 37,712 |
on Convolutional multitask learning. trained is (CNN) Our Neural Network a based using and model | Our model is based on a Convolutional Neural Network (CNN) and trained using multitask learning. | 37,713 |
to different are available Several in datasets. losses extract the the information used different | Several losses are used to extract the different information available in the different datasets. | 37,714 |
task classification learned Our with main is loss. a | Our main task is learned with a classification loss. | 37,715 |
the reduce To we with intra-class variation the experiment loss. center | To reduce the intra-class variation we experiment with the center loss. | 37,716 |
We works datasets. our that also outperforms two recent show re-identification system on | We also show that our system outperforms recent re-identification works on two datasets. | 37,717 |
consider streams. the video of highlight-detection problem automatic We in game | We consider the problem of automatic highlight-detection in video game streams. | 37,718 |
present unsupervised methodology for multi-view deep novelty-based learning this we In light, a detection. highlight | In this light, we present a multi-view unsupervised deep learning methodology for novelty-based highlight detection. | 37,719 |
frequency windowed confinement. (OFDM) signals with multiplexing low pulses exhibit spectral Orthogonal division rectangularly | Orthogonal frequency division multiplexing (OFDM) signals with rectangularly windowed pulses exhibit low spectral confinement. | 37,720 |
two This strategies. method and shaping proposes that a unifies generalizes paper spectral these | This paper proposes a spectral shaping method that generalizes and unifies these two strategies. | 37,721 |
hierarchical. structures inherently key are indoor that observation Our scene is | Our key observation is that indoor scene structures are inherently hierarchical. | 37,722 |
is or not recursive network a neural convolutional; it RvNN. our is network Hence, | Hence, our network is not convolutional; it is a recursive neural network or RvNN. | 37,723 |
By a autoencoder a (VAE), resulting training roughly variational the distribution. follow Gaussian codes fixed-length | By training a variational autoencoder (VAE), the resulting fixed-length codes roughly follow a Gaussian distribution. | 37,724 |
for method GRAINS, for We Generative our Autoencoders INdoor coin Scenes. Recursive | We coin our method GRAINS, for Generative Recursive Autoencoders for INdoor Scenes. | 37,725 |
localization on methods based generate produced usually maps supervised attention networks. results by classification Weakly | Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. | 37,726 |
progressively are regions learn SPG confident utilized to maps masks. the high within The attention | The high confident regions within attention maps are utilized to progressively learn the SPG masks. | 37,727 |
object Extensive high-quality demonstrate is producing that ILSVRC maps. effective on localizations SPG in experiments | Extensive experiments on ILSVRC demonstrate that SPG is effective in producing high-quality object localizations maps. | 37,728 |
However, the by of is heterogeneous often challenged datasets. labeling this | However, this is often challenged by heterogeneous labeling of the datasets. | 37,729 |
human analyzing role Facial cognitive a state. significant in expression has | Facial expression has a significant role in analyzing human cognitive state. | 37,730 |
scale. performance influenced user-specified inlier much is the by its First, | First, its performance is much influenced by the user-specified inlier scale. | 37,731 |
large is it data. Second, computationally inefficient for | Second, it is computationally inefficient for large data. | 37,732 |
algorithm reduction problem MaxFS the for data the of This computationally makes realistic. | This reduction of data for the MaxFS problem makes the algorithm computationally realistic. | 37,733 |
the As object a reconstruct LSN cluttered result, and effectively can backgrounds skeletons. suppress | As a result, LSN can effectively suppress the cluttered backgrounds and reconstruct object skeletons. | 37,734 |
results proposed of state-of-the-art the validate performance LSN. the Experimental | Experimental results validate the state-of-the-art performance of the proposed LSN. | 37,735 |
images. to human learning the in consider pose still task We estimate of | We consider the task of learning to estimate human pose in still images. | 37,736 |
denoising application essential imaging. medical which Our is fluoroscopy is image in | Our application is medical image denoising which is essential in fluoroscopy imaging. | 37,737 |
needs a physician, flavor and tailored each different to quality individual. image towards be has | physician, has a different flavor and image quality needs to be tailored towards each individual. | 37,738 |
Also user specific best users test this perform on trained data. models a for | Also models trained for a specific user perform best on this users test data. | 37,739 |
and spatial efficiency high owing is resolution. Brovey method its popular to pan-sharpening a The | The Brovey is a popular pan-sharpening method owing to its efficiency and high spatial resolution. | 37,740 |
be method mathematical optical This sensing can model sensors. explained remote of by | This method can be explained by mathematical model of optical remote sensing sensors. | 37,741 |
model of MNIST handwritten A is presented recognition digit here. simple | A simple model of MNIST handwritten digit recognition is presented here. | 37,742 |
adaptation model is face recognition. of of an previous The theory a | The model is an adaptation of a previous theory of face recognition. | 37,743 |
We present, of achieved example, way by the algorithms. by results state-of-the-art | We present, by way of example, the results achieved by state-of-the-art algorithms. | 37,744 |
tasks this the team all in challenge. in participates Our | Our team participates in all the tasks in this challenge. | 37,745 |
in a is attribute detection way. task, lesion In multi-label also PSPNet the adopted modified | In lesion attribute detection task, the modified PSPNet is also adopted in a multi-label way. | 37,746 |
higher higher accuracy achieves SAW-SS than the cost sensing of computational complexity. at SAE-TF | SAE-TF achieves higher sensing accuracy than SAW-SS at the cost of higher computational complexity. | 37,747 |
i.e. multiscale in applied method This version, is a | This method is applied in a multiscale version, i.e. | 37,748 |
the variable index instead time. is scale of | the variable index is scale instead of time. | 37,749 |
model consistent natural show with We is PAN MS our images. that and more | We show that our model is more consistent with natural MS and PAN images. | 37,750 |
for sampling single diffusion schemes novel and MRI. This presents paper multi-shell | This paper presents novel single and multi-shell sampling schemes for diffusion MRI. | 37,751 |
is . data used also qualitatively Human brain evaluate to reconstruction | Human brain data is also used to qualitatively evaluate reconstruction . | 37,752 |
and algorithms systems family wireless in radar. communication The constant-modulus used widely is of in | The family of constant-modulus algorithms is widely used in wireless communication systems and in radar. | 37,753 |
Instead, entire spectrum. it the equalizes spatial | Instead, it equalizes the entire spatial spectrum. | 37,754 |
We a of example high-SNR provide under conditions. approach the numerical the demonstrate validity to | We provide a numerical example to demonstrate the validity of the approach under high-SNR conditions. | 37,755 |
response. model counting strongest simply the by order, the the roots with | the model order, by simply counting the roots with the strongest response. | 37,756 |
class a to justify agents Existing generating explanation learn prediction. fluently visual | Existing visual explanation generating agents learn to fluently justify a class prediction. | 37,757 |
trust such human agents This concerning users. particularly building fail with is ultimately as in | This is particularly concerning as ultimately such agents fail in building trust with human users. | 37,758 |
prediction, Our capable AI an for is providing arguments of alternative counter explainable agent i.e. | Our explainable AI agent is capable of providing counter arguments for an alternative prediction, i.e. | 37,759 |
counterfactuals, along explanations that classification correct decisions. with the justify | counterfactuals, along with explanations that justify the correct classification decisions. | 37,760 |
seamlessly method be into any proposed CNN The architecture. integrated general can and is | The proposed method is general and can be seamlessly integrated into any CNN architecture. | 37,761 |
proposed experiments Extensive effectiveness the datasets four on approach. of benchmark show the | Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach. | 37,762 |
a and modified based on multi-task the a DeepUNet way. It perform is in segmentation | It is based on a modified DeepUNet and perform the segmentation in a multi-task way. | 37,763 |
are different task groups The and into processed pipelines. clustered channels on | The channels are clustered into groups and processed on different task pipelines. | 37,764 |
show network the fusion efficient. Experiment is that feature results | Experiment results show that the feature fusion network is efficient. | 37,765 |
between controls strength the association unconditional parameters parameters. the top-level of of auxiliary One these | One of these top-level parameters controls the unconditional strength of association between the auxiliary parameters. | 37,766 |
contrast algorithms, assumptions. distributed this approach In Carlo requires similar few to Monte distributional | In contrast to similar distributed Monte Carlo algorithms, this approach requires few distributional assumptions. | 37,767 |
illustrated performance of of examples. algorithms with simulated is the number The a | The performance of the algorithms is illustrated with a number of simulated examples. | 37,768 |
feature paper a of vision: In we attention. visual this focus specific on | In this paper we focus on a specific feature of vision: visual attention. | 37,769 |
field-of-view review for of we extensive cameras. this provide paper, models large an existing In | In this paper, we provide an extensive review of existing models for large field-of-view cameras. | 37,770 |
results We the also qualitative discuss performance models. provide of all and | We also provide qualitative results and discuss the performance of all models. | 37,771 |
ability implicitly the vehicles to automated lack Today's others. cooperate with | Today's automated vehicles lack the ability to cooperate implicitly with others. | 37,772 |
policies predefined policies within for algorithm simultaneously learns over the Without and macro-actions, macro-actions. | Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. | 37,773 |
present and novel algorithm We for robust generating a model hypotheses consistent multiple-structure for fitting. | We present a novel algorithm for generating robust and consistent hypotheses for multiple-structure model fitting. | 37,774 |
influence parameter on experiments. our settings clearly in of the the expose results We algorithm | We clearly expose the influence of algorithm parameter settings on the results in our experiments. | 37,775 |
strategy multi-model an comparison. we testing propose effective Besides, also by applying | Besides, we also propose an effective testing strategy by applying multi-model comparison. | 37,776 |
CCTV are surveillance views full where not often available. scenario, person practical This reflects | This reflects practical CCTV surveillance scenario, where full person views are often not available. | 37,777 |
samples and gels, filled emulsion two-layered sausages. filled gels, emulsion were The | The samples were emulsion filled gels, two-layered emulsion filled gels, and sausages. | 37,778 |
simplifying principle mastication a indicate and subsequent curve The may during master swallowing. | The master curve may indicate a simplifying principle during mastication and subsequent swallowing. | 37,779 |
also particular complexity We have a calculated measure. | We have also calculated a particular complexity measure. | 37,780 |
swallowing. an increase before displays This just measure | This measure displays an increase just before swallowing. | 37,781 |
may of of different. In a transmitter addition, and time receiver rates a be | In addition, rates of time of a transmitter and a receiver may be different. | 37,782 |
genetic might redundant. female and deplete variance However, choice preferences make | However, female preferences might deplete genetic variance and make choice redundant. | 37,783 |
evolutionary empirical the biology. of We and our results to genetics relevance conservation discuss | We discuss the relevance of our results to conservation genetics and empirical evolutionary biology. | 37,784 |
great vision computer neural have achieved successes in Convolutional (CNNs) many problems. networks | Convolutional neural networks (CNNs) have achieved great successes in many computer vision problems. | 37,785 |
generalization on as Cityscapes) (e.g. as domain another well its capacity | Cityscapes) as well as its generalization capacity on another domain (e.g. | 37,786 |
synthesized as images. in show results data unseen realistic We promising our well in as | We show promising results in our synthesized data as well as in unseen realistic images. | 37,787 |
maximization. for paper, efficient deterministic this an algorithm optimization we In propose consensus | In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. | 37,788 |
fact, worse the return a challenging previous solution. In techniques instances, even may off on | In fact, on challenging instances, the previous techniques may even return a worse off solution. | 37,789 |
between since show challenging different very data detection Change can data the characteristics. be multimodal | Change detection between multimodal data can be very challenging since the data show different characteristics. | 37,790 |
detect CNN between to the employed two candidate changes epochs. A Siamese then is | A Siamese CNN is then employed to detect candidate changes between the two epochs. | 37,791 |
individual grouped the object patch-based changes. as Finally, changes candidate and verified are | Finally, the candidate patch-based changes are grouped and verified as individual object changes. | 37,792 |
this so explored much However, not been far in direction. has | However, not much has so far been explored in this direction. | 37,793 |
quality Meanwhile, the control. suggestions are on given photogrammetric | Meanwhile, suggestions are given on the photogrammetric quality control. | 37,794 |
indefinite the to FAQ Due global find to maximum. guaranteed the relaxation, is not | Due to the indefinite relaxation, FAQ is not guaranteed to find the global maximum. | 37,795 |
generalization superior demonstrating capabilities. proposed and the U-Net, learning methods The outperform | The proposed methods outperform the U-Net, demonstrating superior learning and generalization capabilities. | 37,796 |
(coCSIT) focus channel covariance exploiting Here CSIT on only. we | Here we focus on exploiting channel covariance CSIT (coCSIT) only. | 37,797 |
Spatial upsampling precision is learned achieved by edge-aware a function. employing | Spatial precision is achieved by employing a learned edge-aware upsampling function. | 37,798 |
network Our image. Siamese and left from uses to right extract a model the features | Our model uses a Siamese network to extract features from the left and right image. | 37,799 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.