98 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
These leaderboards are used to track progress in Scene Classification
Most implemented papers
Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification
We present a novel technique to automatically colorize grayscale images that combines both global priors and local image features.
Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
The proposed methods are highly modular, readily plugged into existing deep CNNs.
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions
Finally, we demonstrate that the use of spatial and temporal attention layers improves our model's performance by 2. 7% and 0. 7% respectively, and increases its explainability.
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2.
Remote Sensing Image Scene Classification: Benchmark and State of the Art
During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images.
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine.
A multi-device dataset for urban acoustic scene classification
This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task.
Training neural audio classifiers with few data
We investigate supervised learning strategies that improve the training of neural network audio classifiers on small annotated collections.
A Remote Sensing Image Dataset for Cloud Removal
Removing clouds is an indispensable pre-processing step in remote sensing image analysis.