TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification

Liu, Hai; Zhang, Cheng; Deng, Yongjian; Xie, Bochen; Liu, Tingting; Zhang, Wei; Li, Youfu

doi:10.1109/tmm.2023.3238548

Cited by 48 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ViT [42] ViT-B_16 448 × 448 90.8 TransIFC [65] ViT-B_16 448 × 448 91.0 TransFG [44] ViT-B_16 448 × 448 91.1 TPSKG [46] ViT-B_16 448 × 448 91.3 RAMS-Trans [28] ViT-B_16 448 × 448 91.3 FFVT [45] ViT-B_16 448 × 448 91.4 DCAL [27] ViT-B_16 448 × 448 91.4 SIM-Trans [25] ViT-B_16 448 × 448 91.5 AFTrans [26] ViT-B_16 448 × 448 91.5 IELT [24] ViT The state-of-the-art methods at this stage are organized in Table 8. We can see that our model obtains 1.7% and 1.0% improvements compared to the state-of-the-art CNNbased model PRIS [66] and ViT [42], respectively, which are higher than the results for CUB-200-2011, indicating that our method does not fail due to the increase in the amount of data.…”

Section: Ablation Experiments and Analysismentioning

confidence: 99%

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

Cui,

Hui

2024

Sensors

View full text Add to dashboard Cite

Visual transformers (ViTs) are widely used in various visual tasks, such as fine-grained visual classification (FGVC). However, the self-attention mechanism, which is the core module of visual transformers, leads to quadratic computational and memory complexity. The sparse-attention and local-attention approaches currently used by most researchers are not suitable for FGVC tasks. These tasks require dense feature extraction and global dependency modeling. To address this challenge, we propose a dual-dependency attention transformer model. It decouples global token interactions into two paths. The first is a position-dependency attention pathway based on the intersection of two types of grouped attention. The second is a semantic dependency attention pathway based on dynamic central aggregation. This approach enhances the high-quality semantic modeling of discriminative cues while reducing the computational cost to linear computational complexity. In addition, we develop discriminative enhancement strategies. These strategies increase the sensitivity of high-confidence discriminative cue tracking with a knowledge-based representation approach. Experiments on three datasets, NABIRDS, CUB, and DOGS, show that the method is suitable for fine-grained image classification. It finds a balance between computational cost and performance.

show abstract

Section: Ablation Experiments and Analysismentioning

confidence: 99%

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

Cui,

Hui

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…For example, even when a sentence contains a substantial amount of colloquial information (Figure 1a), we can still capture the key meaning of the sentence, such as the words "team" and "scoring". Grabbing special features in the image can achieve a major effect breakthrough, such as in [13][14][15][16]. Specifically, accurate prediction can be achieved despite the serious interference of colloquial information by utilizing the semantic relationships of the remaining key words.…”

Section: Observations and Insightsmentioning

confidence: 99%

APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification

Ji,

Chen,

Liu

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Text classification is not only a prerequisite for natural language processing work, such as sentiment analysis and natural language reasoning, but is also of great significance for screening massive amounts of information in daily life. However, the performance of classification algorithms is always affected due to the diversity of language expressions, inaccurate semantic information, colloquial information, and many other problems. We identify three clues in this study, namely, core relevance information, semantic location associations, and the mining characteristics of deep and shallow networks for different information, to cope with these challenges. Two key insights about the text are revealed based on these three clues: key information relationship and word group inline relationship. We propose a novel attention feature fusion network, Attention Pyramid Transformer (APTrans), which is capable of learning the core semantic and location information from sentences using the above-mentioned two key insights. Specially, a hierarchical feature fusion module, Feature Fusion Connection (FFCon), is proposed to merge the semantic features of higher layers with positional features of lower layers. Thereafter, a Transformer-based XLNet network is used as the backbone to initially extract the long dependencies from statements. Comprehensive experiments show that APTrans can achieve leading results on the THUCNews Chinese dataset, AG News, and TREC-QA English dataset, outperforming most excellent pre-trained models. Furthermore, extended experiments are carried out on a self-built Chinese dataset theme analysis of teachers’ classroom corpus. We also provide visualization work, further proving that APTrans has good potential in text classification work.

show abstract

“…CNN remains one of the most important and effective models in computer vision. The multiscale feature fusion pyramid in this study is also based on the backbone network of CNN to introduce residual connectivity between levels, thus separating different levels of features from shallow to deep, mining the channel and pixel relationships of different scale features, and performing feature fusion to obtain multiscale fusion features [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Deng,

Liu,

Zhu

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

Partial discharge (PD) is a localized discharge phenomenon in the insulator of electrical equipment resulting from the electric field strength exceeding the local dielectric breakdown electric field. Partial-discharge signal identification is an important means of assessing the insulation status of electrical equipment and critical to the safe operation of electrical equipment. The identification effect of traditional methods is not ideal because the PD signal collected is subject to strong noise interference. To overcome noise interference, quickly and accurately identify PD signals, and eliminate potential safety hazards, this study proposes a PD signal identification method based on multiscale feature fusion. The method improves identification efficiency through the multiscale feature fusion and feature aggregation of phase-resolved partial-discharge (PRPD) diagrams by using PMSNet. The whole network consists of three parts: a CNN backbone composed of a multiscale feature fusion pyramid, a down-sampling feature enhancement (DSFB) module for each layer of the pyramid to acquire features from different layers, a Transformer encoder module dominated by a spatial interaction–attention mechanism to enhance subspace feature interactions, a final categorized feature recognition method for the PRPD maps and a final classification feature generation module (F-Collect). PMSNet improves recognition accuracy by 10% compared with traditional high-frequency current detection methods and current pulse detection methods. On the PRPD dataset, the validation accuracy of PMSNet is above 80%, the validation loss is about 0.3%, and the training accuracy exceeds 85%. Experimental results show that the use of PMSNet can greatly improve the recognition accuracy and robustness of PD signals and has good practicality and application prospects.

show abstract

TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification

Cited by 48 publications

References 0 publications

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

Dual-Dependency Attention Transformer for Fine-Grained Visual Classification

APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification

PMSNet: Multiscale Partial-Discharge Signal Feature Recognition Model via a Spatial Interaction Attention Mechanism

Contact Info

Product

Resources

About