Scene Classification
122 papers with code • 2 benchmarks • 21 datasets
Scene Classification is a task in which scenes from photographs are categorically classified. Unlike object classification, which focuses on classifying prominent objects in the foreground, Scene Classification uses the layout of objects within the scene, in addition to the ambient context, for classification.
Source: Scene classification with Convolutional Neural Networks
Benchmarks
These leaderboards are used to track progress in Scene Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Datasets
RESISC45
BigEarthNet
Places365
RSICD
DCASE 2016
AID
Million-AID
Most implemented papers
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole.
Object Detectors Emerge in Deep Scene CNNs
With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e. g., ImageNet, Places), the state of the art in computer vision is advancing rapidly.
Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
craston/object_detection_cib
•
•
Learning deeper convolutional neural networks becomes a tendency in recent years.
Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification
keillernogueira/exploit-cnn-rs
•
•
We present an analysis of three possible strategies for exploiting the power of existing convolutional neural networks (ConvNets) in different scenarios from the ones they were trained: full training, fine tuning, and using ConvNets as feature extractors.
Classifying Variable-Length Audio Files with All-Convolutional Networks and Masked Global Pooling
numpde/phonepad
•
•
We trained a deep all-convolutional neural network with masked global pooling to perform single-label classification for acoustic scene classification and multi-label classification for domestic audio tagging in the DCASE-2016 contest.
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images.
What makes ImageNet good for transfer learning?
Which is better: more classes or more examples per class?
Mapping Between fMRI Responses to Movies and their Natural Language Annotations
Several research groups have shown how to correlate fMRI responses to the meanings of presented stimuli.
Semi-supervised multi-label feature selection via label correlation analysis with l1-norm graph embedding
Compared with the previous works, there are two advantages of our algorithm: (1) Manifold learning which leverages the underlying geometric structure of the training data is imposed to utilize both labeled and unlabeled data.
DeepCorrect: Correcting DNN models against Image Distortions
In this paper, we evaluate the effect of image distortions like Gaussian blur and additive noise on the activations of pre-trained convolutional filters.
