WIDER Face and Pedestrian Challenge 2018: Methods and Results
This paper presents a review of the 2018 WIDER Challenge on Face and Pedestrian. The challenge focuses on the problem of precise localization of human faces and bodies, and accurate association of identities. It comprises of three tracks: (i) WIDER Face which aims at soliciting new approaches to advance the state-of-the-art in face detection, (ii) WIDER Pedestrian which aims to find effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments, and (iii) WIDER Person Search which presents an exciting challenge of searching persons across 192 movies. In total, 73 teams made valid submissions to the challenge tracks. We summarize the winning solutions for all three tracks. and present discussions on open problems and potential research directions in these topics.
Code (0)
등록된 구현이 없습니다.
Tasks
Face DetectionPedestrian DetectionPerson SearchvalidSimilar Papers 제목 키워드 기반
Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge
The main essence of this paper is to investigate the performance of RetinaNet based object detectors on pedestrian detection. Pedestrian detection is an important research topic as it provides a baseline for general obje…
General Classificationimage-classificationImage ClassificationObject+3HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces
Current face detectors utilize anchors to frame a multi-task learning problem which combines classification and bounding box regression. Effective anchor design and anchor matching strategy enable face detectors to local…
Face DetectionMulti-Task LearningregressionVocal Bursts Intensity PredictionWiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild
Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian det…
DiversityObject DetectionPedestrian DetectionCross-dataset Training for Class Increasing Object Detection
We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for different object classes, cross-dataset training…
Objectobject-detectionObject DetectionAutomatic adaptation of object detectors to new domains using self-training
This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readily available. We automatically obtain lab…
Domain AdaptationKnowledge DistillationPedestrian DetectionUnsupervised Domain Adaptation