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- 403
- 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.
- Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"387
This communication shows that the basic ideas of UDP and LPP are identical, and UDP is just a simplified version of LPP on the assumption that the local density is uniform.
- 338
- A Survey on Long-Tailed Visual Recognition215
This survey focuses on the problems caused by long-tailed data distribution, sort out the representative long-tails visual recognition datasets and summarize some mainstream long-tail studies, and quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade.
- Cross-Domain Recommendation via Cluster-Level Latent Factor Model204
A novel cluster-level based latent factor model is proposed to enhance the cross-domain recommendation, which can not only learn the common rating pattern shared across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns of users in each domain that involve the discriminative information propitious to performance improvement.
- Building Dual-Domain Representations for Compression Artifacts Reduction201
This work proposes a highly accurate approach to remove artifacts of JPEG-compressed images that jointly learns a very deep convolutional network in both DCT and pixel domains and shows large improvements over the state of the arts.
- Patients with COVID-19 in 19 ICUs in Wuhan, China: a cross-sectional study194
Critically ill patients with COVID-19 are associated with a higher risk of severe complications and need to receive an intensive level of treatments, and these patients are investigated in a multicenter prospective observational study.
- Monthly streamflow forecasting based on improved support vector machine model184
An improved SVM model with adaptive insensitive factor with phase-space reconstruction theory is proposed to improve the performance of the support vector machine (SVM) model in predicting monthly streamflow, and is of better generalization ability and higher prediction accuracy.
- 181
- Text extraction from natural scene image: A survey175
This paper offers the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images and draws attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist.
- Self-Supervised Convolutional Subspace Clustering Network172
An end-to-end trainable framework that combines a dual self-supervision that exploits the output of spectral clustering to supervise the training of the feature learning module and the self-expression module into a joint optimization framework is proposed.
- 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.
- 148
- 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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- 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.
- Cluster-Guided Asymmetric Contrastive Learning for Unsupervised Person Re-Identification120
This paper proposes a Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised person Re-ID, in which clustering result is leveraged to guide the feature learning in a properly designed asymmetric contrastive learning framework.
- 117
- 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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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 2026-10-16. 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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