28,175 research outputs found
PAC-Bayesian Majority Vote for Late Classifier Fusion
A lot of attention has been devoted to multimedia indexing over the past few
years. In the literature, we often consider two kinds of fusion schemes: The
early fusion and the late fusion. In this paper we focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle this problem, we investigate a recent and elegant well-founded
quadratic program named MinCq coming from the Machine Learning PAC-Bayes
theory. MinCq looks for the weighted combination, over a set of real-valued
functions seen as voters, leading to the lowest misclassification rate, while
making use of the voters' diversity. We provide evidence that this method is
naturally adapted to late fusion procedure. We propose an extension of MinCq by
adding an order- preserving pairwise loss for ranking, helping to improve Mean
Averaged Precision measure. We confirm the good behavior of the MinCq-based
fusion approaches with experiments on a real image benchmark.Comment: 7 pages, Research repor
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce
best possible decisions by integrating information coming from multiple media.
While most of the past multimodal approaches either work by projecting the
features of different modalities into the same space, or by coordinating the
representations of each modality through the use of constraints, our approach
borrows from both visions. More specifically, assuming each modality can be
processed by a separated deep convolutional network, allowing to take decisions
independently from each modality, we introduce a central network linking the
modality specific networks. This central network not only provides a common
feature embedding but also regularizes the modality specific networks through
the use of multi-task learning. The proposed approach is validated on 4
different computer vision tasks on which it consistently improves the accuracy
of existing multimodal fusion approaches
ModDrop: adaptive multi-modal gesture recognition
We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a training strategy which exploits: i) careful initialization
of individual modalities; and ii) gradual fusion involving random dropping of
separate channels (dubbed ModDrop) for learning cross-modality correlations
while preserving uniqueness of each modality-specific representation. We
present experiments on the ChaLearn 2014 Looking at People Challenge gesture
recognition track, in which we placed first out of 17 teams. Fusing multiple
modalities at several spatial and temporal scales leads to a significant
increase in recognition rates, allowing the model to compensate for errors of
the individual classifiers as well as noise in the separate channels.
Futhermore, the proposed ModDrop training technique ensures robustness of the
classifier to missing signals in one or several channels to produce meaningful
predictions from any number of available modalities. In addition, we
demonstrate the applicability of the proposed fusion scheme to modalities of
arbitrary nature by experiments on the same dataset augmented with audio.Comment: 14 pages, 7 figure
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Trip hazards are a significant contributor to accidents on construction and
manufacturing sites, where over a third of Australian workplace injuries occur
[1]. Current safety inspections are labour intensive and limited by human
fallibility,making automation of trip hazard detection appealing from both a
safety and economic perspective. Trip hazards present an interesting challenge
to modern learning techniques because they are defined as much by affordance as
by object type; for example wires on a table are not a trip hazard, but can be
if lying on the ground. To address these challenges, we conduct a comprehensive
investigation into the performance characteristics of 11 different colour and
depth fusion approaches, including 4 fusion and one non fusion approach; using
colour and two types of depth images. Trained and tested on over 600 labelled
trip hazards over 4 floors and 2000m in an active construction
site,this approach was able to differentiate between identical objects in
different physical configurations (see Figure 1). Outperforming a colour-only
detector, our multi-modal trip detector fuses colour and depth information to
achieve a 4% absolute improvement in F1-score. These investigative results and
the extensive publicly available dataset moves us one step closer to assistive
or fully automated safety inspection systems on construction sites.Comment: 9 Pages, 12 Figures, 2 Tables, Accepted to Robotics and Automation
Letters (RA-L
Saliency-guided video classification via adaptively weighted learning
Video classification is productive in many practical applications, and the
recent deep learning has greatly improved its accuracy. However, existing works
often model video frames indiscriminately, but from the view of motion, video
frames can be decomposed into salient and non-salient areas naturally. Salient
and non-salient areas should be modeled with different networks, for the former
present both appearance and motion information, and the latter present static
background information. To address this problem, in this paper, video saliency
is predicted by optical flow without supervision firstly. Then two streams of
3D CNN are trained individually for raw frames and optical flow on salient
areas, and another 2D CNN is trained for raw frames on non-salient areas. For
the reason that these three streams play different roles for each class, the
weights of each stream are adaptively learned for each class. Experimental
results show that saliency-guided modeling and adaptively weighted learning can
reinforce each other, and we achieve the state-of-the-art results.Comment: 6 pages, 1 figure, accepted by ICME 201
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Multimodal Deep Learning for Robust RGB-D Object Recognition
Robust object recognition is a crucial ingredient of many, if not all,
real-world robotics applications. This paper leverages recent progress on
Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture
for object recognition. Our architecture is composed of two separate CNN
processing streams - one for each modality - which are consecutively combined
with a late fusion network. We focus on learning with imperfect sensor data, a
typical problem in real-world robotics tasks. For accurate learning, we
introduce a multi-stage training methodology and two crucial ingredients for
handling depth data with CNNs. The first, an effective encoding of depth
information for CNNs that enables learning without the need for large depth
datasets. The second, a data augmentation scheme for robust learning with depth
images by corrupting them with realistic noise patterns. We present
state-of-the-art results on the RGB-D object dataset and show recognition in
challenging RGB-D real-world noisy settings.Comment: Final version submitted to IROS'2015, results unchanged,
reformulation of some text passages in abstract and introductio
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
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