128 research outputs found
Maxmin convolutional neural networks for image classification
Convolutional neural networks (CNN) are widely used in computer vision,
especially in image classification. However, the way in which information and
invariance properties are encoded through in deep CNN architectures is still an
open question. In this paper, we propose to modify the standard convo- lutional
block of CNN in order to transfer more information layer after layer while
keeping some invariance within the net- work. Our main idea is to exploit both
positive and negative high scores obtained in the convolution maps. This behav-
ior is obtained by modifying the traditional activation func- tion step before
pooling. We are doubling the maps with spe- cific activations functions, called
MaxMin strategy, in order to achieve our pipeline. Extensive experiments on two
classical datasets, MNIST and CIFAR-10, show that our deep MaxMin convolutional
net outperforms standard CNN
Deformable Part-based Fully Convolutional Network for Object Detection
Existing region-based object detectors are limited to regions with fixed box
geometry to represent objects, even if those are highly non-rectangular. In
this paper we introduce DP-FCN, a deep model for object detection which
explicitly adapts to shapes of objects with deformable parts. Without
additional annotations, it learns to focus on discriminative elements and to
align them, and simultaneously brings more invariance for classification and
geometric information to refine localization. DP-FCN is composed of three main
modules: a Fully Convolutional Network to efficiently maintain spatial
resolution, a deformable part-based RoI pooling layer to optimize positions of
parts and build invariance, and a deformation-aware localization module
explicitly exploiting displacements of parts to improve accuracy of bounding
box regression. We experimentally validate our model and show significant
gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on
PASCAL VOC 2007 and 2012 with VOC data only.Comment: Accepted to BMVC 2017 (oral
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Multimodal representation learning is gaining more and more interest within
the deep learning community. While bilinear models provide an interesting
framework to find subtle combination of modalities, their number of parameters
grows quadratically with the input dimensions, making their practical
implementation within classical deep learning pipelines challenging. In this
paper, we introduce BLOCK, a new multimodal fusion based on the
block-superdiagonal tensor decomposition. It leverages the notion of block-term
ranks, which generalizes both concepts of rank and mode ranks for tensors,
already used for multimodal fusion. It allows to define new ways for optimizing
the tradeoff between the expressiveness and complexity of the fusion model, and
is able to represent very fine interactions between modalities while
maintaining powerful mono-modal representations. We demonstrate the practical
interest of our fusion model by using BLOCK for two challenging tasks: Visual
Question Answering (VQA) and Visual Relationship Detection (VRD), where we
design end-to-end learnable architectures for representing relevant
interactions between modalities. Through extensive experiments, we show that
BLOCK compares favorably with respect to state-of-the-art multimodal fusion
models for both VQA and VRD tasks. Our code is available at
https://github.com/Cadene/block.bootstrap.pytorch
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
International audienceThis paper addresses the problem of time series forecasting for non-stationarysignals and multiple future steps prediction. To handle this challenging task, weintroduce DILATE (DIstortion Loss including shApe and TimE), a new objectivefunction for training deep neural networks. DILATE aims at accurately predictingsudden changes, and explicitly incorporates two terms supporting precise shapeand temporal change detection. We introduce a differentiable loss function suitablefor training deep neural nets, and provide a custom back-prop implementation forspeeding up optimization. We also introduce a variant of DILATE, which providesa smooth generalization of temporally-constrained Dynamic Time Warping (DTW).Experiments carried out on various non-stationary datasets reveal the very goodbehaviour of DILATE compared to models trained with the standard Mean SquaredError (MSE) loss function, and also to DTW and variants. DILATE is also agnosticto the choice of the model, and we highlight its benefit for training fully connectednetworks as well as specialized recurrent architectures, showing its capacity toimprove over state-of-the-art trajectory forecasting approaches
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Leveraging physical knowledge described by partial differential equations
(PDEs) is an appealing way to improve unsupervised video prediction methods.
Since physics is too restrictive for describing the full visual content of
generic videos, we introduce PhyDNet, a two-branch deep architecture, which
explicitly disentangles PDE dynamics from unknown complementary information. A
second contribution is to propose a new recurrent physical cell (PhyCell),
inspired from data assimilation techniques, for performing PDE-constrained
prediction in latent space. Extensive experiments conducted on four various
datasets show the ability of PhyDNet to outperform state-of-the-art methods.
Ablation studies also highlight the important gain brought out by both
disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet
presents interesting features for dealing with missing data and long-term
forecasting
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