computer-vision-10

All-day Semantic Segmentation

3260 papers • 126 benchmarks • 313 datasets

Semantic segmentation under all-day conditions

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in all-day-semantic-segmentation-10

Trend

Dataset

Best Model

Actions

All-day CityScapes

Libraries

i

Use these libraries to find all-day-semantic-segmentation-10 models and implementations

Datasets

All-day CityScapes

Subtasks

No subtasks available.

Most implemented papers

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

Ping Luo, Xiaoou Tang, Jianping Shi, Xingang Pan•Tue Jul 24 2018

IBN-Net is presented, a novel convolutional architecture, which remarkably enhances a CNN’s modeling ability on one domain as well as its generalization capacity on another domain without finetuning.

879

Content

0

Paper Graph

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

Yuanduo Hong, Huihui Pan, Weichao Sun, Yisong Jia•Thu Jan 14 2021

Novel deep dual-resolution networks (DDRNets) are proposed for real-time semantic segmentation of road scenes and a new contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) is designed to enlarge effective receptive fields and fuse multi-scale context.

348 0

Paper Graph

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

J. Choo, Seungryong Kim, Sungha Choi, Sanghun Jung, Huiwon Yun, J. Kim•Sun Mar 28 2021

This paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unseen domains and conducts extensive experiments in urban-scene segmentation to show the superiority of the approach to existing work.

365 0

Paper Graph

Interactive Learning of Intrinsic and Extrinsic Properties for All-Day Semantic Segmentation

T. Gevers, Shaodi You, Qi Bi•Thu Jul 06 2023

Scene appearance changes drastically throughout the day. Existing semantic segmentation methods mainly focus on well-lit daytime scenarios and are not well designed to cope with such great appearance changes. Naively using domain adaption does not solve this problem because it usually learns a fixed mapping between the source and target domain and thus have limited generalization capability on all-day scenarios (i. e., from dawn to night). In this paper, in contrast to existing methods, we tackle this challenge from the perspective of image formulation itself, where the image appearance is determined by both intrinsic (e. g., semantic category, structure) and extrinsic (e. g., lighting) properties. To this end, we propose a novel intrinsic-extrinsic interactive learning strategy. The key idea is to interact between intrinsic and extrinsic representations during the learning process under spatial-wise guidance. In this way, the intrinsic representation becomes more stable and, at the same time, the extrinsic representation gets better at depicting the changes. Consequently, the refined image representation is more robust to generate pixel-wise predictions for all-day scenarios. To achieve this, we propose an All-in-One Segmentation Network (AO-SegNet) in an end-to-end manner. Large scale experiments are conducted on three real datasets (Mapillary, BDD100K and ACDC) and our proposed synthetic All-day CityScapes dataset. The proposed AO-SegNet shows a significant performance gain against the state-of-the-art under a variety of CNN and ViT backbones on all the datasets.

33 0

Paper Graph

Adding a benchmark result helps the community track progress.