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A comprehensive survey on applications of transformers for deep learning tasks
Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …
mechanism to capture contextual relationships within sequential data. Unlike traditional …
A review on automatic image annotation techniques
Nowadays, more and more images are available. However, to find a required image for an
ordinary user is a challenging task. Large amount of researches on image retrieval have …
ordinary user is a challenging task. Large amount of researches on image retrieval have …
Precise no-reference image quality evaluation based on distortion identification
The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of
knowledge about the distortion in the image, which makes quality assessment blind and …
knowledge about the distortion in the image, which makes quality assessment blind and …
Multiple feature reweight densenet for image classification
Recent network research has demonstrated that the performance of convolutional neural
networks can be improved by introducing a learning block that captures spatial correlations …
networks can be improved by introducing a learning block that captures spatial correlations …
Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
This paper presents a method for recognizing scene categories based on approximate
global geometric correspondence. This technique works by partitioning the image into …
global geometric correspondence. This technique works by partitioning the image into …
Modeling the shape of the scene: A holistic representation of the spatial envelope
In this paper, we propose a computational model of the recognition of real world scenes that
bypasses the segmentation and the processing of individual objects or regions. The …
bypasses the segmentation and the processing of individual objects or regions. The …
Recognizing indoor scenes
Indoor scene recognition is a challenging open problem in high level vision. Most scene
recognition models that work well for outdoor scenes perform poorly in the indoor domain …
recognition models that work well for outdoor scenes perform poorly in the indoor domain …
Learning multi-label scene classification
In classic pattern recognition problems, classes are mutually exclusive by definition.
Classification errors occur when the classes overlap in the feature space. We examine a …
Classification errors occur when the classes overlap in the feature space. We examine a …
A bayesian hierarchical model for learning natural scene categories
We propose a novel approach to learn and recognize natural scene categories. Unlike
previous work, it does not require experts to annotate the training set. We represent the …
previous work, it does not require experts to annotate the training set. We represent the …
SIMPLIcity: Semantics-sensitive integrated matching for picture libraries
We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries),
an image retrieval system, which uses semantics classification methods, a wavelet-based …
an image retrieval system, which uses semantics classification methods, a wavelet-based …