Coarse-to-Fine Depth Super-Resolution With Adaptive RGB-D Feature Attention

Zhang, Fan; Liu, Na; Duan, Fuqing

doi:10.1109/tmm.2023.3301238

Cited by 3 publications

(1 citation statement)

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“…This model utilizes a cross-modal interaction module to obtain cross-modal complementary cues by crossly fusing enhanced features, while also exploring the longrange dependencies of RGB-D features. Reference (Zhang et al, 2023) presents a coarse-to-fine network model for adaptive RGBD image feature attention. This model consists of two subnetworks: the "CONet" model for coarse super-resolution and the "RENet" model for refinement.…”

Section: Ijicc 172mentioning

confidence: 99%

Manifold embedded global and local discriminative features selection for single-shot multi-categories clothing recognition and retrieval

Huang

2023

IJICC

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PurposeSingle-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.Design/methodology/approachTo address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.FindingsEmpirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.Originality/valueThis paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

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Section: Ijicc 172mentioning

confidence: 99%