73,678 research outputs found
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning
Object category localization is a challenging problem in computer vision.
Standard supervised training requires bounding box annotations of object
instances. This time-consuming annotation process is sidestepped in weakly
supervised learning. In this case, the supervised information is restricted to
binary labels that indicate the absence/presence of object instances in the
image, without their locations. We follow a multiple-instance learning approach
that iteratively trains the detector and infers the object locations in the
positive training images. Our main contribution is a multi-fold multiple
instance learning procedure, which prevents training from prematurely locking
onto erroneous object locations. This procedure is particularly important when
using high-dimensional representations, such as Fisher vectors and
convolutional neural network features. We also propose a window refinement
method, which improves the localization accuracy by incorporating an objectness
prior. We present a detailed experimental evaluation using the PASCAL VOC 2007
dataset, which verifies the effectiveness of our approach.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI
Orthogonal parallel processing in vector pascal
Despite the widespread adoption of parallel operations in contemporary CPU designs, their use has been restricted by a lack of appropriate programming language abstractions and development environments. To fully exploit the SIMD model of computation such operations offer, programmers depend on CPU specific machine code or implementation dependent libraries. Vector Pascal is a language designed to enable the elegant and efficient expression of SIMD algorithms. It imports into Pascal abstraction mechanisms derived from functional languages, in turn having their origins in APL. In particular, it extends all operators to work on vectors of data. The type system is also extended to handle pixels and dimensional analysis. Code generation is via the ILCG system that allows retargeting to multiple different SIMD instruction sets based on formalised descriptions of the instruction set semantics
Efficient On-the-fly Category Retrieval using ConvNets and GPUs
We investigate the gains in precision and speed, that can be obtained by
using Convolutional Networks (ConvNets) for on-the-fly retrieval - where
classifiers are learnt at run time for a textual query from downloaded images,
and used to rank large image or video datasets.
We make three contributions: (i) we present an evaluation of state-of-the-art
image representations for object category retrieval over standard benchmark
datasets containing 1M+ images; (ii) we show that ConvNets can be used to
obtain features which are incredibly performant, and yet much lower dimensional
than previous state-of-the-art image representations, and that their
dimensionality can be reduced further without loss in performance by
compression using product quantization or binarization. Consequently, features
with the state-of-the-art performance on large-scale datasets of millions of
images can fit in the memory of even a commodity GPU card; (iii) we show that
an SVM classifier can be learnt within a ConvNet framework on a GPU in parallel
with downloading the new training images, allowing for a continuous refinement
of the model as more images become available, and simultaneous training and
ranking. The outcome is an on-the-fly system that significantly outperforms its
predecessors in terms of: precision of retrieval, memory requirements, and
speed, facilitating accurate on-the-fly learning and ranking in under a second
on a single GPU.Comment: Published in proceedings of ACCV 201
PASCal: A principal-axis strain calculator for thermal expansion and compressibility determination
We describe a web-based tool (PASCal; Principal Axis Strain Calculator) aimed
at simplifying the determination of principal coefficients of thermal expansion
and compressibilities from variable-temperature and variable-pressure lattice
parameter data. In a series of three case studies, we use PASCal to re-analyse
previously-published lattice parameter data and show that additional scientific
insight is obtainable in each case. First, the two-dimensional metal-organic
framework Cu-SIP-3 is found to exhibit the strongest area-negative thermal
expansion (NTE) effect yet observed; second, the widely-used explosive HMX
exhibits much stronger mechanical anisotropy than had previously been
anticipated, including uniaxial NTE driven by thermal changes in molecular
conformation; and, third, the high-pressure form of the mineral malayaite is
shown to exhibit a strong negative linear compressibility (NLC) effect that
arises from correlated tilting of SnO6 and SiO4 coordination polyhedra.Comment: 31 pages, 8 figures, formatted as preprint for J. Appl. Crys
Scalable Nonlinear Embeddings for Semantic Category-based Image Retrieval
We propose a novel algorithm for the task of supervised discriminative
distance learning by nonlinearly embedding vectors into a low dimensional
Euclidean space. We work in the challenging setting where supervision is with
constraints on similar and dissimilar pairs while training. The proposed method
is derived by an approximate kernelization of a linear Mahalanobis-like
distance metric learning algorithm and can also be seen as a kernel neural
network. The number of model parameters and test time evaluation complexity of
the proposed method are O(dD) where D is the dimensionality of the input
features and d is the dimension of the projection space - this is in contrast
to the usual kernelization methods as, unlike them, the complexity does not
scale linearly with the number of training examples. We propose a stochastic
gradient based learning algorithm which makes the method scalable (w.r.t. the
number of training examples), while being nonlinear. We train the method with
up to half a million training pairs of 4096 dimensional CNN features. We give
empirical comparisons with relevant baselines on seven challenging datasets for
the task of low dimensional semantic category based image retrieval.Comment: ICCV 2015 preprin
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