pinecone/core-2020-05-10-deduplication huggingface dataset:
Robust Face Alignment Using a Mixture of Invariant Experts

Face alignment, which is the task of finding the locations of a set of facial
landmark points in an image of a face, is useful in widespread application
areas. Face alignment is particularly challenging when there are large
variations in pose (in-plane and out-of-plane rotations) and facial expression.
To address this issue, we propose a cascade in which each stage consists of a
mixture of regression experts. Each expert learns a customized regression model
that is specialized to a different subset of the joint space of pose and
expressions. The system is invariant to a predefined class of transformations
(e.g., affine), because the input is transformed to match each expert's
prototype shape before the regression is applied. We also present a method to
include deformation constraints within the discriminative alignment framework,
which makes our algorithm more robust. Our algorithm significantly outperforms
previous methods on publicly available face alignment datasets.Comment: 17 pages, 6 figure
3D Image Reconstruction from X-Ray Measurements with Overlap

3D image reconstruction from a set of X-ray projections is an important image
reconstruction problem, with applications in medical imaging, industrial
inspection and airport security. The innovation of X-ray emitter arrays allows
for a novel type of X-ray scanners with multiple simultaneously emitting
sources. However, two or more sources emitting at the same time can yield
measurements from overlapping rays, imposing a new type of image reconstruction
problem based on nonlinear constraints. Using traditional linear reconstruction
methods, respective scanner geometries have to be implemented such that no rays
overlap, which severely restricts the scanner design. We derive a new type of
3D image reconstruction model with nonlinear constraints, based on measurements
with overlapping X-rays. Further, we show that the arising optimization problem
is partially convex, and present an algorithm to solve it. Experiments show
highly improved image reconstruction results from both simulated and real-world
measurements.Comment: Published in Computer Vision - ECCV 2016. The final publication is
  available at link.springer.com/chapter/10.1007/978-3-319-46466-4_
Single Image 3D Interpreter Network

Understanding 3D object structure from a single image is an important but
difficult task in computer vision, mostly due to the lack of 3D object
annotations in real images. Previous work tackles this problem by either
solving an optimization task given 2D keypoint positions, or training on
synthetic data with ground truth 3D information. In this work, we propose 3D
INterpreter Network (3D-INN), an end-to-end framework which sequentially
estimates 2D keypoint heatmaps and 3D object structure, trained on both real
2D-annotated images and synthetic 3D data. This is made possible mainly by two
technical innovations. First, we propose a Projection Layer, which projects
estimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D
structural parameters supervised by 2D annotations on real images. Second,
heatmaps of keypoints serve as an intermediate representation connecting real
and synthetic data, enabling 3D-INN to benefit from the variation and abundance
of synthetic 3D objects, without suffering from the difference between the
statistics of real and synthesized images due to imperfect rendering. The
network achieves state-of-the-art performance on both 2D keypoint estimation
and 3D structure recovery. We also show that the recovered 3D information can
be used in other vision applications, such as 3D rendering and image retrieval.Comment: ECCV 2016 (oral). The first two authors contributed equally to this
  wor
Multi-Task Zero-Shot Action Recognition with Prioritised Data
  Augmentation

Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on auxiliary data. Reusing the learned mapping to
project target videos into an embedding space thus allows novel-classes to be
recognised by nearest neighbour inference. However, existing ZSL methods suffer
from auxiliary-target domain shift intrinsically induced by assuming the same
mapping for the disjoint auxiliary and target classes. This compromises the
generalisation accuracy of ZSL recognition on the target data. In this work, we
improve the ability of ZSL to generalise across this domain shift in both
model- and data-centric ways by formulating a visual-semantic mapping with
better generalisation properties and a dynamic data re-weighting method to
prioritise auxiliary data that are relevant to the target classes.
Specifically: (1) We introduce a multi-task visual-semantic mapping to improve
generalisation by constraining the semantic mapping parameters to lie on a
low-dimensional manifold, (2) We explore prioritised data augmentation by
expanding the pool of auxiliary data with additional instances weighted by
relevance to the target domain. The proposed new model is applied to the
challenging zero-shot action recognition problem to demonstrate its advantages
over existing ZSL models.Comment: Published in ECCV 201
Deep Attributes Driven Multi-Camera Person Re-identification

The visual appearance of a person is easily affected by many factors like
pose variations, viewpoint changes and camera parameter differences. This makes
person Re-Identification (ReID) among multiple cameras a very challenging task.
This work is motivated to learn mid-level human attributes which are robust to
such visual appearance variations. And we propose a semi-supervised attribute
learning framework which progressively boosts the accuracy of attributes only
using a limited number of labeled data. Specifically, this framework involves a
three-stage training. A deep Convolutional Neural Network (dCNN) is first
trained on an independent dataset labeled with attributes. Then it is
fine-tuned on another dataset only labeled with person IDs using our defined
triplet loss. Finally, the updated dCNN predicts attribute labels for the
target dataset, which is combined with the independent dataset for the final
round of fine-tuning. The predicted attributes, namely \emph{deep attributes}
exhibit superior generalization ability across different datasets. By directly
using the deep attributes with simple Cosine distance, we have obtained
surprisingly good accuracy on four person ReID datasets. Experiments also show
that a simple metric learning modular further boosts our method, making it
significantly outperform many recent works.Comment: Person Re-identification; 17 pages; 5 figures; In IEEE ECCV 201
Accurate and linear time pose estimation from points and lines

The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such\ud
scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based\ud
counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error\ud
that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms,\ud
the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or\ud
only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft
Human pose estimation via Convolutional Part Heatmap Regression

This paper is on human pose estimation using Convolutional Neural Networks.
Our main contribution is a CNN cascaded architecture specifically designed for
learning part relationships and spatial context, and robustly inferring pose
even for the case of severe part occlusions. To this end, we propose a
detection-followed-by-regression CNN cascade. The first part of our cascade
outputs part detection heatmaps and the second part performs regression on
these heatmaps. The benefits of the proposed architecture are multi-fold: It
guides the network where to focus in the image and effectively encodes part
constraints and context. More importantly, it can effectively cope with
occlusions because part detection heatmaps for occluded parts provide low
confidence scores which subsequently guide the regression part of our network
to rely on contextual information in order to predict the location of these
parts. Additionally, we show that the proposed cascade is flexible enough to
readily allow the integration of various CNN architectures for both detection
and regression, including recent ones based on residual learning. Finally, we
illustrate that our cascade achieves top performance on the MPII and LSP data
sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/Comment: accepted to ECCV 201
PlaNet - Photo Geolocation with Convolutional Neural Networks

Is it possible to build a system to determine the location where a photo was
taken using just its pixels? In general, the problem seems exceptionally
difficult: it is trivial to construct situations where no location can be
inferred. Yet images often contain informative cues such as landmarks, weather
patterns, vegetation, road markings, and architectural details, which in
combination may allow one to determine an approximate location and occasionally
an exact location. Websites such as GeoGuessr and View from your Window suggest
that humans are relatively good at integrating these cues to geolocate images,
especially en-masse. In computer vision, the photo geolocation problem is
usually approached using image retrieval methods. In contrast, we pose the
problem as one of classification by subdividing the surface of the earth into
thousands of multi-scale geographic cells, and train a deep network using
millions of geotagged images. While previous approaches only recognize
landmarks or perform approximate matching using global image descriptors, our
model is able to use and integrate multiple visible cues. We show that the
resulting model, called PlaNet, outperforms previous approaches and even
attains superhuman levels of accuracy in some cases. Moreover, we extend our
model to photo albums by combining it with a long short-term memory (LSTM)
architecture. By learning to exploit temporal coherence to geolocate uncertain
photos, we demonstrate that this model achieves a 50% performance improvement
over the single-image model
On Volumetric Shape Reconstruction from Implicit Forms

International audienceIn this paper we report on the evaluation of volumetric shape reconstruction methods that consider as input implicit forms in 3D. Many visual applications build implicit representations of shapes that are converted into explicit shape representations using geometric tools such as the Marching Cubes algorithm. This is the case with image based reconstructions that produce point clouds from which implicit functions are computed, with for instance a Poisson reconstruction approach. While the Marching Cubes method is a versatile solution with proven efficiency, alternative solutions exist with different and complementary properties that are of interest for shape modeling. In this paper, we propose a novel strategy that builds on Centroidal Voronoi Tessellations (CVTs). These tessellations provide volumetric and surface representations with strong regularities in addition to provably more accurate approximations of the implicit forms considered. In order to compare the existing strategies, we present an extensive evaluation that analyzes various properties of the main strategies for implicit to explicit volumetric conversions: Marching cubes, Delaunay refinement and CVTs, including accuracy and shape quality of the resulting shape mesh
Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing
generic semantic representations, efforts towards developing a similar
representation for 3D has been limited. In this paper, we learn a generic 3D
representation through solving a set of foundational proxy 3D tasks:
object-centric camera pose estimation and wide baseline feature matching. Our
method is based upon the premise that by providing supervision over a set of
carefully selected foundational tasks, generalization to novel tasks and
abstraction capabilities can be achieved. We empirically show that the internal
representation of a multi-task ConvNet trained to solve the above core problems
generalizes to novel 3D tasks (e.g., scene layout estimation, object pose
estimation, surface normal estimation) without the need for fine-tuning and
shows traits of abstraction abilities (e.g., cross-modality pose estimation).
In the context of the core supervised tasks, we demonstrate our representation
achieves state-of-the-art wide baseline feature matching results without
requiring apriori rectification (unlike SIFT and the majority of learned
features). We also show 6DOF camera pose estimation given a pair local image
patches. The accuracy of both supervised tasks come comparable to humans.
Finally, we contribute a large-scale dataset composed of object-centric street
view scenes along with point correspondences and camera pose information, and
conclude with a discussion on the learned representation and open research
questions.Comment: Published in ECCV16. See the project website
  http://3drepresentation.stanford.edu/ and dataset website
  https://github.com/amir32002/3D_Street_Vie
Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European
  Conference on Computer Vision (ECCV) 2016;
  https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5
From Multiview Image Curves to 3D Drawings

Reconstructing 3D scenes from multiple views has made impressive strides in
recent years, chiefly by correlating isolated feature points, intensity
patterns, or curvilinear structures. In the general setting - without
controlled acquisition, abundant texture, curves and surfaces following
specific models or limiting scene complexity - most methods produce unorganized
point clouds, meshes, or voxel representations, with some exceptions producing
unorganized clouds of 3D curve fragments. Ideally, many applications require
structured representations of curves, surfaces and their spatial relationships.
This paper presents a step in this direction by formulating an approach that
combines 2D image curves into a collection of 3D curves, with topological
connectivity between them represented as a 3D graph. This results in a 3D
drawing, which is complementary to surface representations in the same sense as
a 3D scaffold complements a tent taut over it. We evaluate our results against
truth on synthetic and real datasets.Comment: Expanded ECCV 2016 version with tweaked figures and including an
  overview of the supplementary material available at
  multiview-3d-drawing.sourceforge.ne
Updating DL-Lite ontologies through first-order queries

In this paper we study instance-level update in DL-LiteA, the description logic underlying the OWL 2 QL standard. In particular we focus on formula-based approaches to ABox insertion and deletion. We show that DL-LiteA, which is well-known for enjoying first-order rewritability of query answering, enjoys a first-order rewritability property also for updates. That is, every update can be reformulated into a set of insertion and deletion instructions computable through a nonrecursive datalog program. Such a program is readily translatable into a first-order query over the ABox considered as a database, and hence into SQL. By exploiting this result, we implement an update component for DLLiteA-based systems and perform some experiments showing that the approach works in practice.Peer ReviewedPostprint (author's final draft
Algebraic calculi for weighted ontology alignments

inants2016bInternational audienceAlignments between ontologies usually come with numerical attributes expressing the confidence of each correspondence. Semantics supporting such confidences must generalise the semantics of alignments without confidence. There exists a semantics which satisfies this but introduces a discontinuity between weighted and non-weighted interpretations. Moreover, it does not provide a calculus for reasoning with weighted ontology alignments. This paper introduces a calculus for such alignments. It is given by an infinite relation-type algebra, the elements of which are weighted taxonomic relations. In addition, it approximates the non-weighted case in a continuous manner
An Extensible Linear Approach For Holistic Ontology Matching

Resolving the semantic heterogeneity in the semantic web requires finding correspondences between ontologies describing resources. In particular, with the explosive growth of data sets in the Linked Open Data, linking multiple vocabularies and ontologies simultaneously, known as holistic matching problem, becomes necessary. Currently, most state-of-the-art matching approaches are limited to pairwise matching. In this paper, we propose a holistic ontology matching approach that is modeled through a linear program extending the maximum-weighted graph matching problem with linear constraints (cardinality, structural, and coherence constraints). Our approach guarantees the optimal solution with mostly coherent alignments. To evaluate our proposal, we discuss the results of experiments performed on the Conference track of the OAEI 2015, under both holistic and pairwise matching settings
An ontology of soil properties and processes

Assessing the Underworld (ATU) is a large interdisciplinary UK research project, which addresses challenges in integrated inter-asset maintenance. As assets on the surface of the ground (e.g. roads or pavements) and those buried under it (e.g. pipes and cables) are supported by the ground, the properties and processes of soil affect the performance of these assets to a significant degree. In order to make integrated decisions, it is necessary to combine the knowledge and expertise in multiple areas, such as roads, soil, buried assets, sensing, etc. This requires an underpinning knowledge model, in the form of an ontology. Within this context, we present a new ontology for describing soil properties (e.g. soil strength) and processes (e.g. soil compaction), as well as how they affect each other. This ontology can be used to express how the ground affects and is affected by assets buried under the ground or on the ground surface. The ontology is written in OWL 2 and openly available from the University of Leeds data repository: http://​doi.​org/​10.​5518/​54
Linked disambiguated distributional semantic networks

We present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our count-based distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted by patterns and context clues for disambiguation in context. In contrast to dense vector representations, our resource is human readable and interpretable, and thus can be easily embedded within the Semantic Web ecosystem
BESDUI: A Benchmark for End-User Structured Data User Interfaces

The Semantic Web Community has invested significant research efforts in developing systems for Semantic Web search and exploration. But while it has been easy to assess the systems’ computational efficiency, it has been much harder to assess how well different semantic systems’ user interfaces help their users. In this article, we propose and demonstrate the use of a benchmark for evaluating such user interfaces, similar to the TREC benchmark for evaluating traditional search engines. Our benchmark includes a set of typical user tasks and a well-defined procedure for assigning a measure of performance on those tasks to a semantic system. We demonstrate its application to two such system, Virtuoso and Rhizomer. We intend for this work to initiate a community conversation that will lead to a generally accepted framework for comparing systems and for measuring, and thus encouraging, progress towards better semantic search and exploration tools
SPARQLGX: Efficient Distributed Evaluation of SPARQL with Apache Spark

International audiencesparql is the w3c standard query language for querying data expressed in the Resource Description Framework (rdf). The increasing amounts of rdf data available raise a major need and research interest in building efficient and scalable distributed sparql query eval-uators. In this context, we propose sparqlgx: our implementation of a distributed rdf datastore based on Apache Spark. sparqlgx is designed to leverage existing Hadoop infrastructures for evaluating sparql queries. sparqlgx relies on a translation of sparql queries into exe-cutable Spark code that adopts evaluation strategies according to (1) the storage method used and (2) statistics on data. We show that spar-qlgx makes it possible to evaluate sparql queries on billions of triples distributed across multiple nodes, while providing attractive performance figures. We report on experiments which show how sparqlgx compares to related state-of-the-art implementations and we show that our approach scales better than these systems in terms of supported dataset size. With its simple design, sparqlgx represents an interesting alternative in several scenarios
Probabilistic Resource Analysis by Program Transformation

The aim of a probabilistic resource analysis is to derive a probability
distribution of possible resource usage for a program from a probability
distribution of its input. We present an automated multi- phase rewriting based
method to analyze programs written in a subset of C. It generates a probability
distribution of the resource usage as a possibly uncomputable expression and
then transforms it into a closed form expression using over-approximations. We
present the technique, outline the implementation and show results from
experiments with the system