Anabranch Network for Camouflaged Object Segmentation

Abstract

Camouflaged objects attempt to conceal their texture into the background and discriminating them from the background is hard even for human beings. The main objective of this paper is to explore the camouflaged object segmentation problem, namely, segmenting the camouflaged object(s) for a given image. This problem has not been well studied in spite of a wide range of potential applications including the preservation of wild animals and the discovery of new species, surveillance systems, search-and-rescue missions in the event of natural disasters such as earthquakes, floods or hurricanes. This paper addresses a new challenging problem of camouflaged object segmentation. To address this problem, we provide a new image dataset of camouflaged objects for benchmarking purposes. In addition, we propose a general end-to-end network, called the Anabranch Network, that leverages both classification and segmentation tasks. Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy. Extensive experiments conducted on the newly built dataset demonstrate the effectiveness of our network using various fully convolutional networks. \url{https://sites.google.com/view/ltnghia/research/camo}

Published in CVIU 2019. Project page: https://sites.google.com/view/ltnghia/research/camo

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Two-Stream Convolutional Networks for Action Recognition in Videos
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Fully Convolutional Networks for Semantic Segmentation
• Object Detectors Emerge in Deep Scene CNNs
• Spatiotemporal Residual Networks for Video Action Recognition
• Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

Cited by in corpus (11)

• Simultaneously Localize, Segment and Rank the Camouflaged Objects
• Deep Texture-Aware Features for Camouflaged Object Detection
• Camouflaged Object Segmentation with Distraction Mining
• Context-aware Cross-level Fusion Network for Camouflaged Object Detection
• Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
• Improving Camouflaged Object Detection with the Uncertainty of Pseudo-edge Labels
• Mutual Graph Learning for Camouflaged Object Detection
• Confidence-Aware Learning for Camouflaged Object Detection
• Dense Uncertainty Estimation
• Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
• Invertible Attention