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Works327 from public data
- Dual Graph Convolutional Networks for Aspect-based Sentiment Analysis472
A dual graph convolutional networks (DualGCN) model that considers the complementarity of syntax structures and semantic correlations simultaneously and proposes orthogonal and differential regularizers to capture semantic correlations between words precisely by constraining attention scores in the SemGCN module.
- The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification401
Experimental results show the superiority of the MC-Loss when implemented on top of common base networks can achieve state-of-the-art performance on all four fine-grained categorization datasets, and ablative studies further demonstrate the supremacy of the loss when compared with other recently proposed general-purpose losses for visual classification.
- Fine-Grained Visual Classification via Progressive Multi-granularity Training of Jigsaw Patches389
This work proposes a novel framework for fine-grained visual classification with a progressive training strategy that effectively fuses features from different granularities, and a random jigsaw patch generator that encourages the network to learn features at specificgranularities.
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- Prediction of short-term PV power output and uncertainty analysis281
The results imply that the two-stage model proposed in the paper outperforms conventional forecast methods in terms of prediction of short-term PV power outputs and associated uncertainties.
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- Deep metric learning for few-shot image classification: A Review of recent developments237
This paper provides an up-to-date review of deep metric learning methods for few-shot image classification from 2018 to 2022 and categorizes them into three groups according to three stages of metric learning, namely learning feature embeddings, learning class representations, and learning distance measures.
- BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification214
A so-called Bi-Similarity Network (BSNet) that consists of a single embedding module and a bi-similarity module of two similarity measures of diverse characteristics that is enabled to learn more discriminative and less similarity-biased features from few shots of fine-grained images, such that the model generalization ability can be significantly improved.
- Your “Flamingo” is My “Bird”: Fine-Grained, or Not155
The key intuition that: coarse- level label prediction exacerbates fine-grained feature learning, yet fine-level feature betters the learning of coarse-level classifier is discovered, which enables the design of a very simple albeit surprisingly effective solution to a new problem.
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- A concise review of recent few-shot meta-learning methods145
This short communication gives a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics.
- The Role of Data Analysis in the Development of Intelligent Energy Networks145
More comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information in intelligent energy networks.
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- Duplex Contextual Relation Network For Polyp Segmentation136
It is argued that exploring contextual correlation from a holistic view of the whole dataset is essential and a Duplex Contextual Relation Network (DCRNet) is proposed to capture both within-image and cross-image contextual relations.
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- DemoFusion: Democratising High-Resolution Image Generation With No $$$131
This paper aims to democratise high-resolution GenAl by advancing the frontier of high-resolution generation while remaining accessible to a broad audience, and demonstrates that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation.
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- SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval130
This work proposes a deep hashing framework for sketch retrieval that, for the first time, works on a multi-million scale human sketch dataset and shows that state-of-the-art hashing models specifically engineered for static images fail to perform well on temporal sketch data.
- Decorrelation of Neutral Vector Variables: Theory and Applications121
Two fundamental invertible transformations, namely, serial non linear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation, which shows that a highly negatively correlated neutral vector can be transformed to a set of mutually independent scalar variables with the same degrees of freedom.
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- Efficient Face Super-Resolution via Wavelet-based Feature Enhancement Network115
A wavelet-based feature enhancement network is proposed, which mitigates feature distortion by losslessly decomposing the input feature into high and low-frequency components using the wavelet transform and processing them separately.
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- Bayesian estimation of Dirichlet mixture model with variational inference114
The extended factorized approximation method is applied to introduce a single lower-bound to the variational objective function and an analytically tractable estimation solution is derived and the convergence of the proposed algorithm is theoretically guaranteed.
- Fine-Grained Age Estimation in the Wild With Attention LSTM Networks113
Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network methods on the Adience, MORPH Album 2, FG-NET and 15/16LAP datasets.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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