67 research outputs found
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
When Leibniz Bialgebras are Nijenhuis?
Leibniz algebras can be seen as a "non-commutative" analogue of Lie algebras.
Nijenhuis operators on Leibniz algebras introduced by Cari\~{n}ena, Grabowski,
and Marmo in [J. Phys. A: Math. Gen. 37(2004)] are (1, 1)-tensors with
vanishing Nijenhuis torsion. Recently triangular Leibniz bialgebras were
introduced by Tang and Sheng in [J. Noncommut. Geom. 16(2022)] via the twisting
theory of twilled Leibniz algebras. In this paper we find that Leibniz algebras
are very closely related to Nijenhuis operators, and prove that a triangular
symplectic Leibniz bialgebra together with a dual triangular structure must
possess Nijenhuis operators, which makes it possible to study Nijehhuis
geometry from the perspective of Leibniz algebras. At the same time, we regain
the classical Leibniz Yang-Baxter equation by using the tensor form of
classical -matrics. At last we give the classification of triangular Leibniz
bialgebras of low dimensions
Generalized Composition Operators from ℬ
Let 0<p<∞, let -2<q<∞, and let φ be an analytic self-map of and g∈H(). The boundedness and compactness of generalized composition operators (Cφgf)(z)=∫0zf'(φ(ξ))g(ξ)dξ, z∈, f∈H(), from ℬμ (ℬμ,0) spaces to QK,ω(p,q) spaces are investigated
Knowledge Graph Transfer Network for Few-Shot Recognition
Few-shot learning aims to learn novel categories from very few samples given
some base categories with sufficient training samples. The main challenge of
this task is the novel categories are prone to dominated by color, texture,
shape of the object or background context (namely specificity), which are
distinct for the given few training samples but not common for the
corresponding categories (see Figure 1). Fortunately, we find that transferring
information of the correlated based categories can help learn the novel
concepts and thus avoid the novel concept being dominated by the specificity.
Besides, incorporating semantic correlations among different categories can
effectively regularize this information transfer. In this work, we represent
the semantic correlations in the form of structured knowledge graph and
integrate this graph into deep neural networks to promote few-shot learning by
a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing
each node with the classifier weight of the corresponding category, a
propagation mechanism is learned to adaptively propagate node message through
the graph to explore node interaction and transfer classifier information of
the base categories to those of the novel ones. Extensive experiments on the
ImageNet dataset show significant performance improvement compared with current
leading competitors. Furthermore, we construct an ImageNet-6K dataset that
covers larger scale categories, i.e, 6,000 categories, and experiments on this
dataset further demonstrate the effectiveness of our proposed model.Comment: accepted by AAAI 2020 as oral pape
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels
Deep learning-based video salient object detection has recently achieved
great success with its performance significantly outperforming any other
unsupervised methods. However, existing data-driven approaches heavily rely on
a large quantity of pixel-wise annotated video frames to deliver such promising
results. In this paper, we address the semi-supervised video salient object
detection task using pseudo-labels. Specifically, we present an effective video
saliency detector that consists of a spatial refinement network and a
spatiotemporal module. Based on the same refinement network and motion
information in terms of optical flow, we further propose a novel method for
generating pixel-level pseudo-labels from sparsely annotated frames. By
utilizing the generated pseudo-labels together with a part of manual
annotations, our video saliency detector learns spatial and temporal cues for
both contrast inference and coherence enhancement, thus producing accurate
saliency maps. Experimental results demonstrate that our proposed
semi-supervised method even greatly outperforms all the state-of-the-art fully
supervised methods across three public benchmarks of VOS, DAVIS, and FBMS.Comment: ICCV2019, code is available at
https://github.com/Kinpzz/RCRNet-Pytorc
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