Search Results for author:
Found 18 papers, 8 papers with code
ScaleNet: Searching for the Model to Scale
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.
Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
ViTAS: Vision Transformer Architecture Search
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
Cross-layer Navigation Convolutional Neural Network for Fine-grained Visual Classification
Then, attention mechanisms are used after feature fusion to extract spatial and channel information while linking the high-level semantic information and the low-level texture features, which can better locate the discriminative regions for the FGVC.
Structured DropConnect for Uncertainty Inference in Image Classification
For image classification tasks, we propose a structured DropConnect (SDC) framework to model the output of a deep neural network by a Dirichlet distribution.
Unsupervised Person Re-identification via Simultaneous Clustering and Consistency Learning
Unsupervised person re-identification (re-ID) has become an important topic due to its potential to resolve the scalability problem of supervised re-ID models.
DF^2AM: Dual-level Feature Fusion and Affinity Modeling for RGB-Infrared Cross-modality Person Re-identification
Meanwhile, to further mining the relationships between global features from person images, we propose an Affinities Modeling (AM) module to obtain the optimal intra- and inter-modality image matching.
Cross-Modality Person Re-identification
Person Re-Identification
Grad-CAM guided channel-spatial attention module for fine-grained visual classification
The proposed method outperforms the SOTA attention modules in the FGVC task.
Ranked #21 on
Fine-Grained Image Classification
on FGVC Aircraft
DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference
In the DS-UI, we combine the classifier of a DNN, i. e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer.
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization
Due to lack of data, overfitting ubiquitously exists in real-world applications of deep neural networks (DNNs).
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches
In this work, we propose a novel framework for fine-grained visual classification to tackle these problems.
Ranked #17 on
Fine-Grained Image Classification
on Stanford Cars
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
The proposed loss function, termed as mutual-channel loss (MC-Loss), consists of two channel-specific components: a discriminality component and a diversity component.
Ranked #29 on
Fine-Grained Image Classification
on FGVC Aircraft
Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification
Classifying the sub-categories of an object from the same super-category (e. g. bird species, car and aircraft models) in fine-grained visual classification (FGVC) highly relies on discriminative feature representation and accurate region localization.
Mobile big data analysis with machine learning
Finally, we summarize the main challenges and future development directions of mobile big data analysis.
Impacts of Weather Conditions on District Heat System
Using artificial neural network for the prediction of heat demand has attracted more and more attention.
SEA: A Combined Model for Heat Demand Prediction
ENN and ARIMA are used to predict seasonal and trend components, respectively.
BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.
