1,090 research outputs found
Multi-Image Semantic Matching by Mining Consistent Features
This work proposes a multi-image matching method to estimate semantic
correspondences across multiple images. In contrast to the previous methods
that optimize all pairwise correspondences, the proposed method identifies and
matches only a sparse set of reliable features in the image collection. In this
way, the proposed method is able to prune nonrepeatable features and also
highly scalable to handle thousands of images. We additionally propose a
low-rank constraint to ensure the geometric consistency of feature
correspondences over the whole image collection. Besides the competitive
performance on multi-graph matching and semantic flow benchmarks, we also
demonstrate the applicability of the proposed method for reconstructing
object-class models and discovering object-class landmarks from images without
using any annotation.Comment: CVPR 201
Embedding based on function approximation for large scale image search
The objective of this paper is to design an embedding method that maps local
features describing an image (e.g. SIFT) to a higher dimensional representation
useful for the image retrieval problem. First, motivated by the relationship
between the linear approximation of a nonlinear function in high dimensional
space and the stateof-the-art feature representation used in image retrieval,
i.e., VLAD, we propose a new approach for the approximation. The embedded
vectors resulted by the function approximation process are then aggregated to
form a single representation for image retrieval. Second, in order to make the
proposed embedding method applicable to large scale problem, we further derive
its fast version in which the embedded vectors can be efficiently computed,
i.e., in the closed-form. We compare the proposed embedding methods with the
state of the art in the context of image search under various settings: when
the images are represented by medium length vectors, short vectors, or binary
vectors. The experimental results show that the proposed embedding methods
outperform existing the state of the art on the standard public image retrieval
benchmarks.Comment: Accepted to TPAMI 2017. The implementation and precomputed features
of the proposed F-FAemb are released at the following link:
http://tinyurl.com/F-FAem
The relation between fluid intelligence and the general factor as a function of cultural background: a test of Cattell's investment theory
According to Cattellâs (1987) Investment theory individual differences in acquisition of knowledge and skills are partly the result of investment of Fluid Intelligence (Gf) in learning situations demanding insights in complex relations. If this theory holds true Gf will be a factor of General Intelligence (g) because it is involved in all domains of learning. The purpose of the current study was to test the Investment theory, through investigating effects on the relation between Gf and g of differential learning opportunities for different subsets of a population. A second-order model was fitted with confirmatory factor analysis to a battery of 17 tests hypothesized to measure four broad cognitive abilities The model was estimated for three groups with different learning opportunities (N = 2358 Swedes, N = 620 European immigrants, N = 591 non-European immigrants), as well as for the total group. For this group the g Gf relationship was 0.83, while it was close to unity within each of the three subgroups. These results support the Investment theory.Structure of intelligence; Cattellâs Investment theory; fluid Intelligence; general Intelligence
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The truth-telling motor cortex: Response competition in M1 discloses deceptive behaviour
Neural circuits associated with response conflict are active during deception. Here we use transcranial magnetic stimulation to examine for the first time whether competing responses in primary motor cortex can be used to detect lies. Participants used their little finger or thumb to respond either truthfully or deceitfully regarding facial familiarity. Motor-evoked-potentials (MEPs) from muscles associated with both digits tracked the development of each motor plan. When preparing to deceive, the MEP of the non-responding digit (i.e. the plan corresponding to the truth) exceeds the MEP of the responding digit (i.e. the lie), whereas a mirror-reversed pattern occurs when telling the truth. This give away response conflict interacts with the time of stimulation during a speeded reaction period. Lies can even activate digit-specific cortical representations when only verbal responses are made. Our findings support neurobiological models which blend cognitive decision-making with motor programming, and suggest a novel index for discriminating between honest and intentionally false facial recognition
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