2022
DOI: 10.1109/tip.2022.3215887
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Disentangled Capsule Routing for Fast Part-Object Relational Saliency

Abstract: Recently, the Part-Object Relational (POR) saliency underpinned by the Capsule Network (CapsNet) has been demonstrated to be an effective modeling mechanism to improve the saliency detection accuracy. However, it is widely known that the current capsule routing operations have huge computational complexity, which seriously limited the usability of the POR saliency models in real-time applications. To this end, this paper takes an early step towards a fast POR saliency inference by proposing a novel disentangle… Show more

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Cited by 32 publications
(4 citation statements)
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References 72 publications
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“…Input size FLOPs (G) Time (s) TSPOANet [20] 352 × 352 197.78 0.32 TSPORTNet [21] 352 × 352 267.50 0.35 POCINet [18] 352 × 352 332.30 0.1 DCR [49] 352 × 352 60.78 0.06 ICON [23] 352 × 352 64.90 0.013 PWHCNet [19] 256 for salient object detection. Our key idea is integrating the correlations of these two cues from CNNs and CapsNets and let them interact.…”
Section: Methodsmentioning
confidence: 99%
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“…Input size FLOPs (G) Time (s) TSPOANet [20] 352 × 352 197.78 0.32 TSPORTNet [21] 352 × 352 267.50 0.35 POCINet [18] 352 × 352 332.30 0.1 DCR [49] 352 × 352 60.78 0.06 ICON [23] 352 × 352 64.90 0.013 PWHCNet [19] 256 for salient object detection. Our key idea is integrating the correlations of these two cues from CNNs and CapsNets and let them interact.…”
Section: Methodsmentioning
confidence: 99%
“…On top of that, several efforts are devoted to advocate CapsNets-based part-whole visual saliency [21,48]. To solve the heavy computation of the part-whole relational saliency, Liu et al [49] disentangled the horizontal and vertical capsule routing within the capsule routing algorithm for fast saliency prediction. Besides, a few works have been devoted to the complementary of CNNs and CapsNets.…”
Section: B Capsnets For Salient Object Detectionmentioning
confidence: 99%
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“…3) Performance comparison with state-of-the-arts: Table IX illustrates the quantitative comparison on four benchmarks with 10 state-of-the-art methods, including ICON-S [20], TSPOANet [42], DCR [71], DPNet [72], SelfReformer [73], JointCRF [75], ToHR [76], BMP [77], LFR [78], and Amulet [79]. Specifically in Table IX, ICON-S is one version of ICON [20] with Transformer as the backbone.…”
Section: D Image Saliencymentioning
confidence: 99%