4,466 research outputs found
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
In real-world crowd counting applications, the crowd densities vary greatly
in spatial and temporal domains. A detection based counting method will
estimate crowds accurately in low density scenes, while its reliability in
congested areas is downgraded. A regression based approach, on the other hand,
captures the general density information in crowded regions. Without knowing
the location of each person, it tends to overestimate the count in low density
areas. Thus, exclusively using either one of them is not sufficient to handle
all kinds of scenes with varying densities. To address this issue, a novel
end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density
Estimation Network) is proposed. It can adaptively decide the appropriate
counting mode for different locations on the image based on its real density
conditions. DecideNet starts with estimating the crowd density by generating
detection and regression based density maps separately. To capture inevitable
variation in densities, it incorporates an attention module, meant to
adaptively assess the reliability of the two types of estimations. The final
crowd counts are obtained with the guidance of the attention module to adopt
suitable estimations from the two kinds of density maps. Experimental results
show that our method achieves state-of-the-art performance on three challenging
crowd counting datasets.Comment: CVPR 201
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