848 research outputs found
RGB-D Salient Object Detection: A Survey
Salient object detection (SOD), which simulates the human visual perception
system to locate the most attractive object(s) in a scene, has been widely
applied to various computer vision tasks. Now, with the advent of depth
sensors, depth maps with affluent spatial information that can be beneficial in
boosting the performance of SOD, can easily be captured. Although various RGB-D
based SOD models with promising performance have been proposed over the past
several years, an in-depth understanding of these models and challenges in this
topic remains lacking. In this paper, we provide a comprehensive survey of
RGB-D based SOD models from various perspectives, and review related benchmark
datasets in detail. Further, considering that the light field can also provide
depth maps, we review SOD models and popular benchmark datasets from this
domain as well. Moreover, to investigate the SOD ability of existing models, we
carry out a comprehensive evaluation, as well as attribute-based evaluation of
several representative RGB-D based SOD models. Finally, we discuss several
challenges and open directions of RGB-D based SOD for future research. All
collected models, benchmark datasets, source code links, datasets constructed
for attribute-based evaluation, and codes for evaluation will be made publicly
available at https://github.com/taozh2017/RGBDSODsurveyComment: 24 pages, 12 figures. Has been accepted by Computational Visual Medi
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
Most of the existing bi-modal (RGB-D and RGB-T) salient object detection
methods utilize the convolution operation and construct complex interweave
fusion structures to achieve cross-modal information integration. The inherent
local connectivity of the convolution operation constrains the performance of
the convolution-based methods to a ceiling. In this work, we rethink these
tasks from the perspective of global information alignment and transformation.
Specifically, the proposed \underline{c}ross-mod\underline{a}l
\underline{v}iew-mixed transform\underline{er} (CAVER) cascades several
cross-modal integration units to construct a top-down transformer-based
information propagation path. CAVER treats the multi-scale and multi-modal
feature integration as a sequence-to-sequence context propagation and update
process built on a novel view-mixed attention mechanism. Besides, considering
the quadratic complexity w.r.t. the number of input tokens, we design a
parameter-free patch-wise token re-embedding strategy to simplify operations.
Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that
such a simple two-stream encoder-decoder framework can surpass recent
state-of-the-art methods when it is equipped with the proposed components.Comment: Updated version, more flexible structure, better performanc
- …