Browse > Computer Vision > Scene Parsing > Scene Recognition

Scene Recognition

18 papers with code · Computer Vision
Subtask of Scene Parsing

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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

6 Oct 2013jetpacapp/DeepBeliefSDK

We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.

DOMAIN ADAPTATION OBJECT RECOGNITION SCENE RECOGNITION TRANSFER LEARNING

Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs

4 Oct 2016yjxiong/caffe

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.

SCENE CLASSIFICATION SCENE RECOGNITION

CNN Features off-the-shelf: an Astounding Baseline for Recognition

23 Mar 2014baldassarreFe/deep-koalarization

We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13.

IMAGE CLASSIFICATION IMAGE RETRIEVAL OBJECT CLASSIFICATION SCENE RECOGNITION

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

CVPR 2018 leeyeehoo/CSRNet-pytorch

We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance.

SCENE RECOGNITION

Deep Filter Banks for Texture Recognition and Segmentation

CVPR 2015 mcimpoi/deep-fbanks

Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.

MATERIAL RECOGNITION SCENE RECOGNITION

Places205-VGGNet Models for Scene Recognition

7 Aug 2015wanglimin/Places205-VGGNet

We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205.

OBJECT RECOGNITION SCENE RECOGNITION

Self-Supervised Model Adaptation for Multimodal Semantic Segmentation

11 Aug 2018DeepSceneSeg/SSMA

To address this limitation, we propose a mutimodal semantic segmentation framework that dynamically adapts the fusion of modality-specific features while being sensitive to the object category, spatial location and scene context in a self-supervised manner.

SCENE RECOGNITION SEMANTIC SEGMENTATION

Translate-to-Recognize Networks for RGB-D Scene Recognition

CVPR 2019 ownstyledu/Translate-to-Recognize-Networks

Empirically, we verify that this new semi-supervised setting is able to further enhance the performance of recognition network.

SCENE RECOGNITION

Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition

1 Sep 2016wangzheallen/vsad

In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition.

SCENE RECOGNITION

Learning image representations tied to ego-motion

ICCV 2015 tu-rbo/concarne

Understanding how images of objects and scenes behave in response to specific ego-motions is a crucial aspect of proper visual development, yet existing visual learning methods are conspicuously disconnected from the physical source of their images.

AUTONOMOUS DRIVING SCENE RECOGNITION