Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RGB-D Salient Object Detection Based on Discriminative Cross-modal
  Transfer Learning   In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap between the "data-hungry" nature of CNNs and the unavailability
of sufficient labeled training data in depth modality. In the proposed
approach, we leverage the auxiliary data from the source modality effectively
by training the RGB saliency detection network to obtain the task-specific
pre-understanding layers for the target modality. Meanwhile, we exploit the
depth-specific information by pre-training a modality classification network
that encourages modal-specific representations during the optimizing course.
Thus, it could make the feature representations of the RGB and depth modalities
as discriminative as possible. These two modules are pre-trained independently
and then stitched to initialize and optimize the eventual depth-induced
saliency detection model. Experiments demonstrate the effectiveness of the
proposed novel pre-training strategy as well as the significant and consistent
improvements of the proposed approach over other state-of-the-art methods.

32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th