Scene Recognition

64 papers with code • 8 benchmarks • 15 datasets

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Most implemented papers

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

leeyeehoo/CSRNet-pytorch CVPR 2018

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.

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

jetpacapp/DeepBeliefSDK 6 Oct 2013

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.

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

baldassarreFe/deep-koalarization 23 Mar 2014

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.

Bilinear CNNs for Fine-grained Visual Recognition

tommarvoloriddle/Bilinear-CNN-Tensorflow2.4-implementation 29 Apr 2015

We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

wang-chen/interestingness ECCV 2020

In this paper, we explore the problem of interesting scene prediction for mobile robots.

Places205-VGGNet Models for Scene Recognition

wanglimin/Places205-VGGNet 7 Aug 2015

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

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

yjxiong/caffe 4 Oct 2016

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.

HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN

ParitoshParmar/HalluciNet 10 Dec 2019

The hallucination task is treated as an auxiliary task, which can be used with any other action related task in a multitask learning setting.

Indoor Scene Recognition in 3D

HenrryBryant/Scene-Recognition-in-3D 28 Feb 2020

Moreover, we advocate multi-task learning as a way of improving scene recognition, building on the fact that the scene type is highly correlated with the objects in the scene, and therefore with its semantic segmentation into different object classes.

Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics

laura-wang/video_repres_sts 31 Aug 2020

Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc.