Search Results for author:
Found 10 papers, 2 papers with code
Competing Ratio Loss for Discriminative Multi-class Image Classification
However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR.
Competing Ratio Loss for Discriminative Multi-class Image Classification
A lot of studies of image classification based on deep convolutional neural network focus on the network structure to improve the image classification performance.
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
In this paper, we propose a new CNN based method for age group and gender estimation leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group and gender classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. In order to further improve the performance and alleviate over-fitting problem, RoR model is pre-trained on ImageNet firstly, and then it is fune-tuned on the IMDB-WIKI-101 data set for further learning the features of face images, finally, it is used to fine-tune on Adience data set.
Ranked #5 on
Age And Gender Classification
on Adience Age
(using extra training data)
Multiple Instance Learning Convolutional Neural Networks for Object Recognition
In this paper, we propose a weakly supervised CNN framework named Multiple Instance Learning Convolutional Neural Networks (MILCNN) to solve this problem.
Residual Networks of Residual Networks: Multilevel Residual Networks
This paper proposes a novel residual-network architecture, Residual networks of Residual networks (RoR), to dig the optimization ability of residual networks.
Ranked #16 on
Image Classification
on SVHN
Latent Model Ensemble with Auto-localization
Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene label- ing, due to their large learning capacity and resistance to overfit.
A Classification Leveraged Object Detector
Currently, the state-of-the-art image classification algorithms outperform the best available object detector by a big margin in terms of average precision.
Large-Scale Visual Font Recognition
This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content.
Ranked #1 on
Font Recognition
on VFR-447
Ensemble Video Object Cut in Highly Dynamic Scenes
We incorporate this similarity information into a graph-cut energy minimization framework for foreground object segmentation.
Detection Evolution with Multi-order Contextual Co-occurrence
The so-called (1 st -order) context feature is computed as a set of randomized binary comparisons on the response map of the baseline object detector.
