18,027 research outputs found
Fine-grained sketch-based image retrieval by matching deformable part models
(c) 2014. The copyright of this document resides with its authors.
It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. An important characteristic of sketches, compared with text, rests with their ability to intrinsically capture object appearance and structure. Nonetheless, akin to traditional text-based image retrieval, conventional sketch-based image retrieval (SBIR) principally focuses on retrieving images of the same category, neglecting the fine-grained characteristics of sketches. In this paper, we advocate the expressiveness of sketches and examine their efficacy under a novel fine-grained SBIR framework. In particular, we study how sketches enable fine-grained retrieval within object categories. Key to this problem is introducing a mid-level sketch representation that not only captures object pose, but also possesses the ability to traverse sketch and image domains. Specifically, we learn deformable part-based model (DPM) as a mid-level representation to discover and encode the various poses in sketch and image domains independently, after which graph matching is performed on DPMs to establish pose correspondences across the two domains. We further propose an SBIR dataset that covers the unique aspects of fine-grained SBIR. Through in-depth experiments, we demonstrate the superior performance of our SBIR framework, and showcase its unique ability in fine-grained retrieval
Quantum Phase Transition, O(3) Universality Class and Phase Diagram of Spin-1/2 Heisenberg Antiferromagnet on Distorted Honeycomb Lattice: A Tensor Renormalization Group Study
The spin-1/2 Heisenberg antiferromagnet on the distorted honeycomb (DHC)
lattice is studied by means of the tensor renormalization group method. It is
unveiled that the system has a quantum phase transition of second-order between
the gapped quantum dimer phase and a collinear Neel phase at the critical point
of coupling ratio \alpha_{c} = 0.54, where the quantum critical exponents \nu =
0.69(2) and \gamma = 1.363(8) are obtained. The quantum criticality is found to
fall into the O(3) universality class. A ground-state phase diagram in the
field-coupling ratio plane is proposed, where the phases such as the dimer,
semi-classical Neel, and polarized phases are identified. A link between the
present spin system to the boson Hubbard model on the DHC lattice is also
discussed.Comment: 6 pages, 5 figures, published in Phys. Rev.
Video-based online face recognition using identity surfaces
Recognising faces across multiple views is more challenging
than that from a fixed view because of the severe
non-linearity caused by rotation in depth, self-occlusion,
self-shading, and change of illumination. The problem
can be related to the problem of modelling the spatiotemporal
dynamics of moving faces from video input for
unconstrained live face recognition. Both problems remain
largely under-developed. To address the problems, a novel
approach is presented in this paper. A multi-view dynamic
face model is designed to extract the shape-and-pose-free
texture patterns of faces. The model provides a precise correspondence
to the task of recognition since the 3D shape
information is used to warp the multi-view faces onto the
model mean shape in frontal-view. The identity surface of
each subject is constructed in a discriminant feature space
from a sparse set of face texture patterns, or more practically,
from one or more learning sequences containing
the face of the subject. Instead of matching templates or
estimating multi-modal density functions, face recognition
can be performed by computing the pattern distances to the
identity surfaces or trajectory distances between the object
and model trajectories. Experimental results depict that this
approach provides an accurate recognition rate while using
trajectory distances achieves a more robust performance
since the trajectories encode the spatio-temporal information
and contain accumulated evidence about the moving
faces in a video input
Neural Graph Embedding for Neural Architecture Search
Existing neural architecture search (NAS) methods often operate in discrete or continuous spaces directly, which ignores the graphical topology knowledge of neural networks. This leads to suboptimal search performance and efficiency, given the factor that neural networks are essentially directed acyclic graphs (DAG). In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). Specifically, we represent the building block (i.e. the cell) of neural networks with a neural DAG, and learn it by leveraging a Graph Convolutional Network to propagate and model the intrinsic topology information of network architectures. This results in a generic neural network representation integrable with different existing NAS frameworks. Extensive experiments show the superiority of NGE over the state-of-the-art methods on image classification and semantic segmentation
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