Log In Sign Up

DRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation

by   Haruya Sakashita, et al.

Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environments. However, most previous studies use limited combinations of source/target datasets, and domain adaptation techniques have never been thoroughly evaluated in a more challenging and diverse set of target domains. This work presents a new multi-domain dataset  for benchmarking domain adaptation techniques on in-the-wild road-scene videos collected from the Internet. The dataset consists of pixel-level annotations for 100 videos selected to cover diverse scenes/domains based on two criteria; human subjective judgment and an anomaly score judged using an existing road-scene dataset. We provide multiple manually labeled ground-truth frames for each video, enabling a thorough evaluation of video-level domain adaptation where each video independently serves as the target domain. Using the dataset, we quantify domain adaptation performances of state-of-the-art methods and clarify the potential and novel challenges of domain adaptation techniques. The dataset is available at


page 1

page 4

page 7

page 8


Source-Free Domain Adaptation for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convo...

Class-Conditional Domain Adaptation on Semantic Segmentation

Semantic segmentation is an important sub-task for many applications, bu...

Colonoscopy Polyp Detection: Domain Adaptation From Medical Report Images to Real-time Videos

Automatic colorectal polyp detection in colonoscopy video is a fundament...

Semantic Segmentation in Art Paintings

Semantic segmentation is a difficult task even when trained in a supervi...

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

While several datasets for autonomous navigation have become available i...

BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation

Semantic segmentation aims to predict pixel-level labels. It has become ...

Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery

The ability to perceive 3D human bodies from a single image has a multit...