15,085 research outputs found
Multi-Modal Multi-Scale Deep Learning for Large-Scale Image Annotation
Image annotation aims to annotate a given image with a variable number of
class labels corresponding to diverse visual concepts. In this paper, we
address two main issues in large-scale image annotation: 1) how to learn a rich
feature representation suitable for predicting a diverse set of visual concepts
ranging from object, scene to abstract concept; 2) how to annotate an image
with the optimal number of class labels. To address the first issue, we propose
a novel multi-scale deep model for extracting rich and discriminative features
capable of representing a wide range of visual concepts. Specifically, a novel
two-branch deep neural network architecture is proposed which comprises a very
deep main network branch and a companion feature fusion network branch designed
for fusing the multi-scale features computed from the main branch. The deep
model is also made multi-modal by taking noisy user-provided tags as model
input to complement the image input. For tackling the second issue, we
introduce a label quantity prediction auxiliary task to the main label
prediction task to explicitly estimate the optimal label number for a given
image. Extensive experiments are carried out on two large-scale image
annotation benchmark datasets and the results show that our method
significantly outperforms the state-of-the-art.Comment: Submited to IEEE TI
Counterexample-Preserving Reduction for Symbolic Model Checking
The cost of LTL model checking is highly sensitive to the length of the
formula under verification. We observe that, under some specific conditions,
the input LTL formula can be reduced to an easier-to-handle one before model
checking. In our reduction, these two formulae need not to be logically
equivalent, but they share the same counterexample set w.r.t the model. In the
case that the model is symbolically represented, the condition enabling such
reduction can be detected with a lightweight effort (e.g., with SAT-solving).
In this paper, we tentatively name such technique "Counterexample-Preserving
Reduction" (CePRe for short), and finally the proposed technquie is
experimentally evaluated by adapting NuSMV
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