Scene Segmentation

53 papers with code • 3 benchmarks • 4 datasets

Scene segmentation is the task of splitting a scene into its various object components.

Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.

Greatest papers with code

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

tensorflow/models ECCV 2020

We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.

Optical Flow Estimation Panoptic Segmentation +2

Dual Attention Network for Scene Segmentation

osmr/imgclsmob CVPR 2019

Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.

Scene Segmentation

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

osmr/imgclsmob 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

Crowd Counting General Classification +4

Simplifying Object Segmentation with PixelLib Library

ayoolaolafenwa/PixelLib 20 Jan 2021

PixelLib [1] is a library created to allow easy implementation of object segmentation in real life applications.

Image Classification Scene Segmentation +1

Point-Voxel CNN for Efficient 3D Deep Learning

mit-han-lab/pvcnn NeurIPS 2019

The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.

 Ranked #1 on 3D Semantic Segmentation on S3DIS (mIoU metric)

3D Object Detection 3D Semantic Segmentation +1

Index Network

poppinace/indexnet_matting 11 Aug 2019

By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.

Grayscale Image Denoising Image Denoising +3