I Can Find You! Boundary-Guided Separated Attention Network for Camouflaged Object Detection

Abstract

Can you find me? By simulating how humans to discover the so-called 'perfectly'-camouflaged object, we present a novel boundary-guided separated attention network (call BSA-Net). Beyond the existing camouflaged object detection (COD) wisdom, BSA-Net utilizes two-stream separated attention modules to highlight the separator (or say the camouflaged object's boundary) between an image's background and foreground: the reverse attention stream helps erase the camouflaged object's interior to focus on the background, while the normal attention stream recovers the interior and thus pay more attention to the foreground; and both streams are followed by a boundary guider module and combined to strengthen the understanding of boundary. The core design of such separated attention is motivated by the COD procedure of humans: find the subtle difference between the foreground and background to delineate the boundary of a camouflaged object, then the boundary can help further enhance the COD accuracy. We validate on three benchmark datasets that the proposed BSA-Net is very beneficial to detect camouflaged objects with the blurred boundaries and similar colors/patterns with their backgrounds. Extensive results exhibit very clear COD improvements on our BSA-Net over sixteen SOTAs.

Deep Gradient Learning for Efficient Camouflaged Object Detection

“…To mimic the detection process of predators, Mei et al [14] develop PFNet, which contains a positioning and focusing module to conduct the identification. Some works propose delicate structures such as covariance matrices of feature [30] and multivariate calibration components [19] to improve the robustness of the network. Kajiura et al [31] improve the detection accuracy by exploring the uncertainties of pseudo-edge and pseudo-map labels.…”

“…We explore the necessity of our decoupling strategy. Inspired by [19], we replace the feature extracted from the texture encoder with the low-level feature from the context encoder, which yields a single-stream network 5(c) demonstrates that decoupling the network into two streams can improve the performance ( : +5.3% on CAMO-), which benefits from the modelling of separated branches without feature ambiguity in different hierarchies.…”

Guided Slot Attention for Unsupervised Video Object Segmentation

“…We use sum of IOU loss and weighted binary crossentropy loss as objective functions, which are often used in salient object detection tasks [29,39]. This loss function helps assign more weight to the hard case pixels.…”

MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection

“…classification task as the second branch and auxiliary task of camouflaged object segmentation. Zhu et al [67] proposed a new boundary-guided separated attention network (BSA-NET), which uses two streams of separated attention modules to highlight the boundaries of camouflaged objects. Lv et al [32] proposed a multi-task learning framework to jointly localize and segment the camouflaged objects while inferring their ranks.…”

“…Recently, many researches put emphasis on learning from a fixed single view with either auxiliary tasks [18,32,34,58,67,15], uncertainty discovery [20,26], or vision transformers [56,38] and their proposed methods achieved significant progress. Nevertheless, due to visual insignificance of camouflaged objects and contextual insufficiency from single-view input, they are still striving to precisely recognize camouflaged objects and their performance needs to be improved.…”