Modal complementary fusion network for RGB-T salient object detection

Ma, Shuai; Song, Kechen; Dong, Hongwen; Tian, Hongkun; Yan, Yunhui

doi:10.1007/s10489-022-03950-1

Cited by 15 publications

(2 citation statements)

References 47 publications

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“…[25] introduce intra-identity regularization in the unsupervised domain to improve image robustness with respect to global images, and employ intrinsic contrast constraints to fully exploit local discrimination cues with respect to local patches. [26] propose a novel Modal Complementary Fusion Network (MCFNet) to mitigate the contamination effect of low-quality images from both global and local perspectives, and a novel spatial complementary fusion module (SCFM) for uncovering complementary local regions between different modalities. [27] develope a model called Knowledge Refresh and Consolidation (KRC) which enhanced by the introduction of a dynamic memory model and an adaptive working model that enables bidirectional transfer of knowledge and a knowledge consolidation scheme that operates on a dual space.…”

Section: Short-term Person Re-identificationmentioning

confidence: 99%

A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

Ge,

Lu,

Xia

et al. 2024

Preprint

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Due to the effect of clothing change on person re-identification models, some researchers have car-ried out in-depth studies on clothes-changing person re-identification(CC-ReID). However, there are some problem of the loss of edge identity information in the semantic guidance process in current methods. In this work, we propose a dual-stream network model, named GFSAnet, which consists of both global and face streams. This model is capable of retaining edge identity information while reinforcing the weight of fine-grained discriminative information. Firstly, in the global stream, we de-sign a difference guide model (DGM) and a feature self-augmentation model (FSAM). The differential features are learned through the difference guide module to preserve the edge identity information of the boundary between background and foreground, while the weights of the local information in the network are optimized through the feature self-augmentation module. Secondly, in the face stream, the multi-scale structure design of pyramid residual network is used to learn the facial features fusing coarse and fine granularity. Finally, the contributions of global and facial features are dynamically adjusted to work together in the inference by setting the hyperparameter α. Extensive experiments show that the method in this paper achieves better performance on the PRCC, Celeb-ReID and Celeb-Light datasets.

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Section: Short-term Person Re-identificationmentioning

confidence: 99%

A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

Ge,

Lu,

Xia

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Many works have made efforts in this area [38,39]. To fuse two-modal features, several methods have been proposed, including CBAM [12,13], the complementary weighting module [40], the crossmodal multi-stage fusion module [41], the multi-modal interactive attention unit [42], the effective cross-modality fusion module [43], the semantic constraint provider [44], the modality difference reduction module [45], the spatial complementary fusion module [46], and the cross-modal interaction module [15]. To fuse two-level features during the decoding stage, the FAM module [13] and interactive decoders [47] were proposed.…”

Section: Related Work Salient Object Detectionmentioning

confidence: 99%

Lightweight Cross-Modal Information Mutual Reinforcement Network for RGB-T Salient Object Detection

Lv,

Wan,

Zhou

et al. 2024

Entropy

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RGB-T salient object detection (SOD) has made significant progress in recent years. However, most existing works are based on heavy models, which are not applicable to mobile devices. Additionally, there is still room for improvement in the design of cross-modal feature fusion and cross-level feature fusion. To address these issues, we propose a lightweight cross-modal information mutual reinforcement network for RGB-T SOD. Our network consists of a lightweight encoder, the cross-modal information mutual reinforcement (CMIMR) module, and the semantic-information-guided fusion (SIGF) module. To reduce the computational cost and the number of parameters, we employ the lightweight module in both the encoder and decoder. Furthermore, to fuse the complementary information between two-modal features, we design the CMIMR module to enhance the two-modal features. This module effectively refines the two-modal features by absorbing previous-level semantic information and inter-modal complementary information. In addition, to fuse the cross-level feature and detect multiscale salient objects, we design the SIGF module, which effectively suppresses the background noisy information in low-level features and extracts multiscale information. We conduct extensive experiments on three RGB-T datasets, and our method achieves competitive performance compared to the other 15 state-of-the-art methods.

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One-stop multiscale reconciliation attention network with scribble supervision for salient object detection in optical remote sensing images

Yan,

Cao

et al. 2024

Appl Intell

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Modal complementary fusion network for RGB-T salient object detection

Cited by 15 publications

References 47 publications

A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

A method based on difference guide and feature self-enhancement for clothes-changing person re-identification

Lightweight Cross-Modal Information Mutual Reinforcement Network for RGB-T Salient Object Detection

One-stop multiscale reconciliation attention network with scribble supervision for salient object detection in optical remote sensing images

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