9,646 research outputs found
Context-aware CNNs for person head detection
Person detection is a key problem for many computer vision tasks. While face
detection has reached maturity, detecting people under a full variation of
camera view-points, human poses, lighting conditions and occlusions is still a
difficult challenge. In this work we focus on detecting human heads in natural
scenes. Starting from the recent local R-CNN object detector, we extend it with
two types of contextual cues. First, we leverage person-scene relations and
propose a Global CNN model trained to predict positions and scales of heads
directly from the full image. Second, we explicitly model pairwise relations
among objects and train a Pairwise CNN model using a structured-output
surrogate loss. The Local, Global and Pairwise models are combined into a joint
CNN framework. To train and test our full model, we introduce a large dataset
composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate
our method and demonstrate improvements of person head detection against
several recent baselines in three datasets. We also show improvements of the
detection speed provided by our model.Comment: To appear in International Conference on Computer Vision (ICCV), 201
GPstruct: Bayesian structured prediction using Gaussian processes
We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M ^3 N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct
Distributed Parameter Estimation via Pseudo-likelihood
Estimating statistical models within sensor networks requires distributed
algorithms, in which both data and computation are distributed across the nodes
of the network. We propose a general approach for distributed learning based on
combining local estimators defined by pseudo-likelihood components,
encompassing a number of combination methods, and provide both theoretical and
experimental analysis. We show that simple linear combination or max-voting
methods, when combined with second-order information, are statistically
competitive with more advanced and costly joint optimization. Our algorithms
have many attractive properties including low communication and computational
cost and "any-time" behavior.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
An Universal Image Attractiveness Ranking Framework
We propose a new framework to rank image attractiveness using a novel
pairwise deep network trained with a large set of side-by-side multi-labeled
image pairs from a web image index. The judges only provide relative ranking
between two images without the need to directly assign an absolute score, or
rate any predefined image attribute, thus making the rating more intuitive and
accurate. We investigate a deep attractiveness rank net (DARN), a combination
of deep convolutional neural network and rank net, to directly learn an
attractiveness score mean and variance for each image and the underlying
criteria the judges use to label each pair. The extension of this model
(DARN-V2) is able to adapt to individual judge's personal preference. We also
show the attractiveness of search results are significantly improved by using
this attractiveness information in a real commercial search engine. We evaluate
our model against other state-of-the-art models on our side-by-side web test
data and another public aesthetic data set. With much less judgments (1M vs
50M), our model outperforms on side-by-side labeled data, and is comparable on
data labeled by absolute score.Comment: Accepted by 2019 Winter Conference on Application of Computer Vision
(WACV
- …