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Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features
Fine-grained visual classification is a challenging task that recognizes...
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ATRM: Attention-based Task-level Relation Module for GNN-based Few-shot Learning
Recently, graph neural networks (GNNs) have shown powerful ability to ha...
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Grad-CAM guided channel-spatial attention module for fine-grained visual classification
Fine-grained visual classification (FGVC) is becoming an important resea...
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Progressive Co-Attention Network for Fine-grained Visual Classification
Fine-grained visual classification aims to recognize images belonging to...
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Knowledge Transfer Based Fine-grained Visual Classification
Fine-grained visual classification (FGVC) aims to distinguish the sub-cl...
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Dilated-Scale-Aware Attention ConvNet For Multi-Class Object Counting
Object counting aims to estimate the number of objects in images. The le...
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BSNet: Bi-Similarity Network for Few-shot Fine-grained Image Classification
Few-shot learning for fine-grained image classification has gained recen...
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Your "Labrador" is My "Dog": Fine-Grained, or Not
Whether what you see in Figure 1 is a "labrador" or a "dog", is the ques...
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DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
This paper proposes a dual-supervised uncertainty inference (DS-UI) fram...
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Actor and Action Modular Network for Text-based Video Segmentation
The actor and action semantic segmentation is a challenging problem that...
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CC-Loss: Channel Correlation Loss For Image Classification
The loss function is a key component in deep learning models. A commonly...
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Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Due to lack of data, overfitting ubiquitously exists in real-world appli...
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GINet: Graph Interaction Network for Scene Parsing
Recently, context reasoning using image regions beyond local convolution...
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SSKD: Self-Supervised Knowledge Distillation for Cross Domain Adaptive Person Re-Identification
Domain adaptive person re-identification (re-ID) is a challenging task d...
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ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification
Despite achieving state-of-the-art performance, deep learning methods ge...
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A Concise Review of Recent Few-shot Meta-learning Methods
Few-shot meta-learning has been recently reviving with expectations to m...
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OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer
A deep neural network of multiple nonlinear layers forms a large functio...
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Channel Attention with Embedding Gaussian Process: A Probabilistic Methodology
Channel attention mechanisms, as the key components of some modern convo...
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Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization
In this paper, we propose a dual-attention guided dropblock module, and ...
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Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
Fine-grained visual classification (FGVC) is much more challenging than ...
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Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
Unsupervised domain adaptation aims to leverage labeled data from a sour...
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Object-Oriented Video Captioning with Temporal Graph and Prior Knowledge Building
Traditional video captioning requests a holistic description of the vide...
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Fine-Grained Instance-Level Sketch-Based Video Retrieval
Existing sketch-analysis work studies sketches depicting static objects ...
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The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Key for solving fine-grained image categorization is finding discriminat...
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Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category...
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Semi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval
Sketch-based image retrieval (SBIR) is a challenging task due to the lar...
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Competing Ratio Loss for Discriminative Multi-class Image Classification
The development of deep convolutional neural network architecture is cri...
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Deep Zero-Shot Learning for Scene Sketch
We introduce a novel problem of scene sketch zero-shot learning (SSZSL),...
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On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models
Variational inference (VI) is a widely used framework in Bayesian estima...
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Vehicular Edge Computing via Deep Reinforcement Learning
The smart vehicles construct Vehicle of Internet which can execute vario...
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Language Identification with Deep Bottleneck Features
In this paper we proposed an end-to-end short utterances speech language...
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Dirichlet Mixture Model based VQ Performance Prediction for Line Spectral Frequency
In this paper, we continue our previous work on the Dirichlet mixture mo...
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Impacts of Weather Conditions on District Heat System
Using artificial neural network for the prediction of heat demand has at...
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Deep Neural Network for Analysis of DNA Methylation Data
Many researches demonstrated that the DNA methylation, which occurs in t...
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Classification of EEG Signal based on non-Gaussian Neutral Vector
In the design of brain-computer interface systems, classification of Ele...
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Statistical Speech Model Description with VMF Mixture Model
In this paper, we present the LSF parameters by a unit vector form, whic...
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Histogram Transform-based Speaker Identification
A novel text-independent speaker identification (SI) method is proposed....
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Mobile big data analysis with machine learning
This paper investigates to identify the requirement and the development ...
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SEA: A Combined Model for Heat Demand Prediction
Heat demand prediction is a prominent research topic in the area of inte...
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Infinite Mixture of Inverted Dirichlet Distributions
In this work, we develop a novel Bayesian estimation method for the Diri...
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BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
A Bayesian approach termed BAyesian Least Squares Optimization with Nonn...
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SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval
We propose a deep hashing framework for sketch retrieval that, for the f...
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Decorrelation of Neutral Vector Variables: Theory and Applications
In this paper, we propose novel strategies for neutral vector variable d...
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The Role of Data Analysis in the Development of Intelligent Energy Networks
Data analysis plays an important role in the development of intelligent ...
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Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval
Sketch-based image retrieval (SBIR) is challenging due to the inherent d...
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