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

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Cited by in corpus (34)

• Concealed Object Detection
• Boundary-Guided Camouflaged Object Detection
• Deep Gradient Learning for Efficient Camouflaged Object Detection
• Feature Aggregation and Propagation Network for Camouflaged Object Detection
• Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network
• MirrorNet: Bio-Inspired Camouflaged Object Segmentation
• Advances in Deep Concealed Scene Understanding
• ZoomNeXt: A Unified Collaborative Pyramid Network for Camouflaged Object Detection
• SAM Struggles in Concealed Scenes -- Empirical Study on Segment Anything
• SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
• Bilateral Reference for High-Resolution Dichotomous Image Segmentation
• Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite
• Simultaneously Localize, Segment and Rank the Camouflaged Objects
• Depth Awakens: A Depth-perceptual Attention Fusion Network for RGB-D Camouflaged Object Detection
• Camouflaged Object Segmentation with Distraction Mining
• Deep Texture-Aware Features for Camouflaged Object Detection
• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection
• Context in object detection: a systematic literature review
• 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
• Towards Accurate Camouflaged Object Detection with Mixture Convolution and Interactive Fusion
• The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
• Confidence-Aware Learning for Camouflaged Object Detection
• Dense Uncertainty Estimation
• Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations
• Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation
• Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance
• Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
• Utilizing Grounded SAM for self-supervised frugal camouflaged human detection
• Invertible Attention
• CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection
• Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model