1,715 research outputs found
Dynamic Feature Integration for Simultaneous Detection of Salient Object, Edge and Skeleton
In this paper, we solve three low-level pixel-wise vision problems, including
salient object segmentation, edge detection, and skeleton extraction, within a
unified framework. We first show some similarities shared by these tasks and
then demonstrate how they can be leveraged for developing a unified framework
that can be trained end-to-end. In particular, we introduce a selective
integration module that allows each task to dynamically choose features at
different levels from the shared backbone based on its own characteristics.
Furthermore, we design a task-adaptive attention module, aiming at
intelligently allocating information for different tasks according to the image
content priors. To evaluate the performance of our proposed network on these
tasks, we conduct exhaustive experiments on multiple representative datasets.
We will show that though these tasks are naturally quite different, our network
can work well on all of them and even perform better than current
single-purpose state-of-the-art methods. In addition, we also conduct adequate
ablation analyses that provide a full understanding of the design principles of
the proposed framework. To facilitate future research, source code will be
released
Frequency Perception Network for Camouflaged Object Detection
Camouflaged object detection (COD) aims to accurately detect objects hidden
in the surrounding environment. However, the existing COD methods mainly locate
camouflaged objects in the RGB domain, their performance has not been fully
exploited in many challenging scenarios. Considering that the features of the
camouflaged object and the background are more discriminative in the frequency
domain, we propose a novel learnable and separable frequency perception
mechanism driven by the semantic hierarchy in the frequency domain. Our entire
network adopts a two-stage model, including a frequency-guided coarse
localization stage and a detail-preserving fine localization stage. With the
multi-level features extracted by the backbone, we design a flexible frequency
perception module based on octave convolution for coarse positioning. Then, we
design the correction fusion module to step-by-step integrate the high-level
features through the prior-guided correction and cross-layer feature channel
association, and finally combine them with the shallow features to achieve the
detailed correction of the camouflaged objects. Compared with the currently
existing models, our proposed method achieves competitive performance in three
popular benchmark datasets both qualitatively and quantitatively.Comment: Accepted by ACM MM 202
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