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Scene Classification

24 papers with code · Computer Vision

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Deliberative Explanations: visualizing network insecurities

NeurIPS 2019 peiwang062/Deliberative-explanation

The explanation consists of a list of insecurities, each composed of 1) an image region (more generally, a set of input variables), and 2) an ambiguity formed by the pair of classes responsible for the network uncertainty about the region.

OBJECT RECOGNITION SCENE CLASSIFICATION

3
01 Dec 2019

The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification

3 Jul 2019kkoutini/cpjku_dcase19

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.

ACOUSTIC SCENE CLASSIFICATION SCENE CLASSIFICATION

26
03 Jul 2019

SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion

18 Jun 2019lucashu1/land-cover

The availability of curated large-scale training data is a crucial factor for the development of well-generalizing deep learning methods for the extraction of geoinformation from multi-sensor remote sensing imagery.

SCENE CLASSIFICATION SEMANTIC SEGMENTATION

7
18 Jun 2019

Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification

24 Apr 2019dr-costas/undaw

A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions.

ACOUSTIC SCENE CLASSIFICATION SCENE CLASSIFICATION UNSUPERVISED DOMAIN ADAPTATION

7
24 Apr 2019

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

10 Apr 2019hackerekcah/distinct-events-asc

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events.

ACOUSTIC SCENE CLASSIFICATION SCENE CLASSIFICATION

3
10 Apr 2019

Equivariant Multi-View Networks

ICCV 2019 daniilidis-group/emvn

Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.

3D SHAPE RETRIEVAL SCENE CLASSIFICATION

30
01 Apr 2019

Understanding and Visualizing Deep Visual Saliency Models

CVPR 2019 SenHe/uavdvsm

Our analyses reveal that: 1) some visual regions (e. g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e. g. head) over some others (e. g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e. g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.

OBJECT RECOGNITION SALIENCY PREDICTION SCENE CLASSIFICATION

9
06 Mar 2019

A Remote Sensing Image Dataset for Cloud Removal

3 Jan 2019BUPTLdy/RICE_DATASET

Removing clouds is an indispensable pre-processing step in remote sensing image analysis.

SCENE CLASSIFICATION

11
03 Jan 2019