Browse > Computer Vision > Scene Parsing > Scene Recognition

Scene Recognition

15 papers with code · Computer Vision
Subtask of Scene Parsing

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Greatest papers with code

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

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

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

CVPR 2018 Neerajj9/CSRNet-keras

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

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

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

Scene Recognition by Combining Local and Global Image Descriptors

21 Feb 2017flytxtds/scene-recognition

In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification.

OBJECT RECOGNITION SCENE RECOGNITION

Temporal Residual Networks for Dynamic Scene Recognition

CVPR 2017 feichtenhofer/temporal-resnet

Finally, our temporal ResNet boosts recognition performance and establishes a new state-of-the-art on dynamic scene recognition, as well as on the complementary task of action recognition.

SCENE RECOGNITION ZERO-SHOT ACTION RECOGNITION