Search Results for author:
Found 7 papers, 1 papers with code
Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework.
Ranked #51 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Image Recognition
+1
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks.
CUNet: A Compact Unsupervised Network for Image Classification
In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge.
Coarse2Fine: Two-Layer Fusion For Image Retrieval
Departing from the previous methods fusing multiple image descriptors simultaneously, C2F is featured by a layered procedure composed by filtering and refining.
A Hierarchical Distributed Processing Framework for Big Image Data
The proposed ICP framework consists of two mechanisms, i. e. SICP (Static ICP) and DICP (Dynamic ICP).
A Distributed Deep Representation Learning Model for Big Image Data Classification
The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches.
NIST: An Image Classification Network to Image Semantic Retrieval
This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge.
