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Microscopic fine-grained instance classification through deep attention
[article]
2020
arXiv
pre-print
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The result is a robust but lightweight end-to-end trainable deep network that yields state-of-the-art results in two separate fine-grained multi-instance biomedical image classification tasks: a benchmark ...
First, it utilises a gated attention module that can focus on multiple key instances at high resolution without extra annotations or region proposals. ...
MF received financial support from the Arthritis Therapy Prgramme (A-TAP) funded by the Kennedy Trust. JR and TC are supported by the EPSRC SeeBiByte Programme Grant (EP/M013774/1). ...
arXiv:2010.02818v1
fatcat:oonqpqsupjbovbkfte2eo4bcia
Rethinking movie genre classification with fine-grained semantic clustering
[article]
2021
arXiv
pre-print
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By leveraging pre-trained 'expert' networks, we learn the influence of different combinations of modes for multi-label genre classification. ...
Using a contrastive loss, we continue to fine-tune this 'coarse' genre classification network to identify high-level intertextual similarities between the movies across all genre labels. ...
Following collaborative gating, each attention vector is then concatenated into a sequence embedding. ...
arXiv:2012.02639v3
fatcat:sufthmjshjdtpdk7ijgitlkhqm
Probability Fusion Decision Framework of Multiple Deep Neural Networks for Fine-grained Visual Classification
2019
IEEE Access
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In this paper, we propose a novel probability fusion decision framework (named as PFDM-Net) for fine-grained visual classification. ...
Fine-grained visual classification tasks often suffer from that the subordinate categories within a basic-level category have low inter-class discrepancy and high intra-class variances, which is still ...
fine-grained visual classification. ...
doi:10.1109/access.2019.2933169
fatcat:ni4ttjp2mvbvfc5vsqg3purs4a
Fine-Grained Multi-Instance Classification in Microscopy Through Deep Attention
2020
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
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Fine-grained object recognition and classification in biomedical images poses a number of challenges. ...
We propose a simple yet effective attention based deep architecture to address these issues, specially improved background suppression and recognition of multiple instances per image. ...
Fig. 1 . 1 Framework for the proposed multiple instance fine-grained classification pipeline. The network consists of three modules: feature extraction, attention module and feature fusion. ...
doi:10.1109/isbi45749.2020.9098704
dblp:conf/isbi/FanCCXR20
fatcat:ykc5t7rlincy5jb7qbsiukrioy
Progressive Multi-stage Interactive Training in Mobile Network for Fine-grained Recognition
[article]
2021
arXiv
pre-print
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Fine-grained Visual Classification (FGVC) aims to identify objects from subcategories. It is a very challenging task because of the subtle inter-class differences. ...
In fact, real-world scenarios of fine-grained recognition often require a more lightweight mobile network that can be utilized offline. ...
for Fine-grained Recognition
Abstract—Fine-grained Visual Classification (FGVC) aims to
identify objects from subcategories. ...
arXiv:2112.04223v1
fatcat:sn2wcwqsgbgv7l7puhgqpapcum
Simultaneous Region Localization and Hash Coding for Fine-grained Image Retrieval
[article]
2019
arXiv
pre-print
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Fine-grained image hashing is a challenging problem due to the difficulties of discriminative region localization and hash code generation. ...
In this paper, we propose a deep fine-grained hashing to simultaneously localize the discriminative regions and generate the efficient binary codes. ...
Limited attention has been paid for fine-grained hashing. ...
arXiv:1911.08028v1
fatcat:nwlm6gw4qff4nowyfopopm2wsm
A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests
2022
Computational Intelligence and Neuroscience
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Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different ...
With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. ...
Fine-Grained Visual Recognition Methods. ...
doi:10.1155/2022/4391491
pmid:35665281
pmcid:PMC9162821
fatcat:2344c2ov3jb6pjf6jcobwk5fli
Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep Learning
[article]
2023
medRxiv
pre-print
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This mechanism also empowers extracting fine-grained attention within image tiles (the small patches), a task largely ignored in most existing weakly supervised-based frameworks. ...
We demonstrate that optimising the patch-level encoder is crucial to achieving high-quality fine-grained and tissue-level subtyping results and offers a significant improvement over task-agnostic encoders ...
Acknowledgements We thank Professor Ian Mills and Oxford Prostate Cancer Biology Group for their guidance and suggestions on molecular biology for this paper. ...
doi:10.1101/2023.07.30.23293391
fatcat:uwzcnbiuj5e2dfxcuujm6skcdm
Cross-modal Multiple Granularity Interactive Fusion Network for Long Document Classification
2024
ACM Transactions on Knowledge Discovery from Data
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In this article, we propose a novel cross-modal method for long document classification, in which multiple granularity feature shifting networks are proposed to integrate the multi-scale text and visual ...
Additionally, a multi-modal collaborative pooling block is proposed to eliminate redundant fine-grained text features and simultaneously reduce the computational complexity. ...
[37] relied on visual information for aligning the important sentences of a document using attention. Hazarika et al. ...
doi:10.1145/3631711
fatcat:nv6dhzl5zbetviempmhpgaqxia
Fine-Grained Image Analysis with Deep Learning: A Survey
[article]
2021
arXiv
pre-print
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Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. ...
In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas -- fine-grained ...
ACKNOWLEDGMENTS The authors would like to thank the editor and the anonymous reviewers for their constructive comments. ...
arXiv:2111.06119v2
fatcat:ninawxsjtnf4lndtqquuwl3weq
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Then, based on initially predicted lesion maps for large quantities of imagelevel annotated data, a lesion attentive disease grading model is designed to improve the severity classification accuracy. ...
Medical image analysis has two important research areas: disease grading and fine-grained lesion segmentation. ...
The goal of disease grading is to predict the classification label for the severity of a disease, while segmentation aims to address more fine-grained, pixel-wise lesion detection. ...
doi:10.1109/cvpr.2019.00218
dblp:conf/cvpr/ZhouHH00C019
fatcat:lj5s56anlraghm2g4tds6cuta4
Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
[article]
2021
arXiv
pre-print
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Target-oriented sentiment classification is a fine-grained task of natural language processing to analyze the sentiment polarity of the targets. ...
To improve the performance of sentiment classification, many approaches proposed various attention mechanisms to capture the important context words of a target. ...
Combining with the coarse-grained and fine-grained attention, MGAN is a multi-attention network and significantly outperforms the coarse-grained attention-based models. ...
arXiv:2010.00767v3
fatcat:g2gjkwvtxnfi5imobw7ev5gbpy
AI Ekphrasis: Multi-Modal Learning with Foundation Models for Fine-Grained Poetry Retrieval
2022
Electronics
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The test results reflect that the shared attention parameters alleviate fine-grained attribute recognition, and the proposed approach is a significant step towards automatic multi-modal retrieval for improved ...
However, it lacks shared cross-modality attention features to model fine-grained relationships. ...
Deep Neural Networks for Visual and Textual Data Matching The unified textual and visual attention mechanism for multimodal reasoning and matching through Dual Attention Networks is proposed by Nam H. ...
doi:10.3390/electronics11081275
fatcat:4vzquef2mbg3xdatmpqoiurwyi
Making Decision like Human: Joint Aspect Category Sentiment Analysis and Rating Prediction with Fine-to-Coarse Reasoning
2022
Proceedings of the ACM Web Conference 2022
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Joint aspect category sentiment analysis (ACSA) and rating prediction (RP) is a newly proposed task (namely ASAP) that integrates the characteristics of both fine-grained and coarse-grained sentiment analysis ...
Second, we build a fine-to-coarse hierarchical label graph, modeling the aspect categories and the overall rating as a hierarchical structure for full interaction of the two granularities. ...
In realistic scenarios, coarse-grained and fine-grained sentiment classification are highly correlated. ...
doi:10.1145/3485447.3512024
fatcat:v6vbjbd3o5autpnhp7hgypr2wi
Cross-X Learning for Fine-Grained Visual Categorization
[article]
2019
arXiv
pre-print
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Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine-grained classification. ...
In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature ...
in DCNNs [34, 26, 40] , and then used to extract part-specific features for fine-grained classification. ...
arXiv:1909.04412v1
fatcat:i6rwtt352ng5xb2dlocec7gw74
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