SCEP—A New Image Dimensional Emotion Recognition Model Based on Spatial and Channel-Wise Attention Mechanisms

Li, Bo; Hui, Rutai; Jiang, Xuekun; Miao, Fang; Feng, Feng; Jin, Libiao

doi:10.1109/access.2021.3057373

Cited by 15 publications

(8 citation statements)

References 44 publications

(57 reference statements)

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“…However, these methods concentrate only on building deep networks to increase the models' representation capability, which results in high computation and memory demands. Besides, in most cases, the conventional spatial attention mechanism [45] only provides one-direction weight allocation [12]- [14], which results in the loss of vital information up to a specific level.…”

Section: A Adjacent Attention Blockmentioning

confidence: 99%

Improving Image Compression With Adjacent Attention and Refinement Block

et al. 2023

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Recently, learned image compression algorithms have shown incredible performance compared to classic hand-crafted image codecs. Despite its considerable achievements, the fundamental disadvantage is not optimized for retaining local redundancies, particularly non-repetitive patterns, which have a detrimental influence on the reconstruction quality. This paper introduces the autoencoder-style network-based efficient image compression method, which contains three novel blocks, i.e., adjacent attention block, Gaussian merge block, and decoded image refinement block, to improve the overall image compression performance. The adjacent attention block allocates the additional bits required to capture spatial correlations (both vertical and horizontal) and effectively remove worthless information. The Gaussian merge block assists the rate-distortion optimization performance, while the decoded image refinement block improves the defects in low-resolution reconstructed images. A comprehensive ablation study analyzes and evaluates the qualitative and quantitative capabilities of the proposed model. Experimental results on two publicly available datasets reveal that our method outperforms the state-of-the-art methods on the KODAK dataset (by around 4dB and 5dB) and CLIC dataset (by about 4dB and 3dB) in terms of PSNR and MS-SSIM.

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Section: A Adjacent Attention Blockmentioning

confidence: 99%

Improving Image Compression With Adjacent Attention and Refinement Block

et al. 2023

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“…Samara et al [18] proposed a hierarchical machine learning method for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customized encoding for users' self-reported affective states. Ren et al [19] proposed a novel spatial and channel-wise attention-based emotion prediction model, which integrates both spatial attention and channel-wise weight mechanisms into a CNN layer structure to predict image emotions, and finally output the emotion values in a continuous 2-D valence and arousal space.…”

Section: Traditional Facial Expression Recognitionmentioning

confidence: 99%

“…Ren et al. [19] proposed a novel spatial and channel‐wise attention‐based emotion prediction model, which integrates both spatial attention and channel‐wise weight mechanisms into a CNN layer structure to predict image emotions, and finally output the emotion values in a continuous 2‐D valence and arousal space.…”

Section: Related Workmentioning

confidence: 99%

Research on image sentiment analysis technology based on sparse representation

Jin

et al. 2022

CAAI Trans on Intel Tech

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Many methods based on deep learning have achieved amazing results in image sentiment analysis. However, these existing methods usually pursue high accuracy, ignoring the effect on model training efficiency. Considering that when faced with large‐scale sentiment analysis tasks, the high accuracy rate often requires long experimental time. In view of the weakness, a method that can greatly improve experimental efficiency with only small fluctuations in model accuracy is proposed, and singular value decomposition (SVD) is used to find the sparse feature of the image, which are sparse vectors with strong discriminativeness and effectively reduce redundant information; The authors propose the Fast Dictionary Learning algorithm (FDL), which can combine neural network with sparse representation. This method is based on K‐Singular Value Decomposition, and through iteration, it can effectively reduce the calculation time and greatly improve the training efficiency in the case of small fluctuation of accuracy. Moreover, the effectiveness of the proposed method is evaluated on the FER2013 dataset. By adding singular value decomposition, the accuracy of the test suite increased by 0.53%, and the total experiment time was shortened by 8.2%; Fast Dictionary Learning shortened the total experiment time by 36.3%.

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“…Zhao et al explored the spatial connectivity patterns and interdependency between channels through spatialwise attention and channelwise attention [ 32 ]. Li et al employed spatial attention to enhance the contrast between salient and irrelevant regions and adopted channel attention to emphasize informative features [ 33 ]. Ding et al proposed pyramid spatial attention and pyramid channel attention to locate discriminative regions [ 34 ].…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition

Yang

Xing

Chang

et al. 2022

Computational Intelligence and Neuroscience

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Image sentiment recognition has attracted considerable attention from academia and industry due to the increasing tendency of expressing opinions via images and videos online. Previous studies focus on multilevel representation from global and local views to improve recognition performance. However, it is insufficient to research the importance and relationship of visual regions for image sentiment recognition. This paper proposes an attention-based sentiment region importance and relationship (ASRIR) analysis method, including important attention and relation attention for image sentiment recognition. First, we extract spatial region features using a multilevel pyramid network from the image. Second, we design important attention to exploring the sentiment semantic-related regions and relation attention to investigating the relationship between regions. In order to release the excessive concentration of attention, we employ a unimodal function as the objective function for regularization. Finally, the region features weighted by the attention mechanism are fused and input into a fully connected layer for classification. Extensive experiments on various commonly used image sentiment datasets demonstrate that our proposed method outperforms the state-of-the-art approaches.

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SCEP—A New Image Dimensional Emotion Recognition Model Based on Spatial and Channel-Wise Attention Mechanisms

Cited by 15 publications

References 44 publications

Improving Image Compression With Adjacent Attention and Refinement Block

Improving Image Compression With Adjacent Attention and Refinement Block

Research on image sentiment analysis technology based on sparse representation

Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition

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