Titlebook: Human Centric Visual Analysis with Deep Learning; Liang Lin,Dongyu Zhang,Wangmeng Zuo Book 2020 Springer Nature Singapore Pte Ltd. 2020 De

Magisterial

surmount

Pedestrian Detection with RPN and Boosted Forestedestrians; therefore, previous leading pedestrian detectors were generally hybrid methods combining handcrafted and deep convolutional features. In this chapter, we propose a very simple but effective baseline for pedestrian detection using an RPN followed by boosted forest on shared high-resolutio

GLOSS

Self-supervised Structure-Sensitive Learning for Human Parsing“Look into Person (LIP),” that makes a significant advance in terms of scalability, diversity, and difficulty, a contribution that we feel is crucial for future developments in human-centric analysis. Furthermore, in contrast to the existing efforts to improve feature discriminative capability, we s

orthopedist

Instance-Level Human Parsingal difficulty in parsing multiple instances in a single pass. In this chapter, we make the first attempt to explore a detection-free part grouping network (PGN) to efficiently parse multiple people in an image in a single pass. PGN reformulates instance-level human parsing as twinned subtasks that c

表狀態(tài)

Video Instance-Level Human Parsinged feature propagation from other consecutive frames between two key frames. Specifically, ATEN first incorporates a Parsin-RCNN to produce the instance-level parsing result for each key frame, which integrates global human parsing and instance-level human segmentation into a unified model. To balan

Isthmus

LATHE

Face Verificationg number of facial images of different individuals. By naturally combining two recently emerging techniques, active learning (AL) and self-paced learning (SPL), the proposed framework is capable of automatically annotating new instances and incorporating them into the training with weak expert recer

Lumbar-Stenosis

愚笨

Constituent

Face Verification the mixture of SPL and AL effectively improves not only the classifier accuracy but also the robustness against noisy data compared to the existing AL/SPL methods (.[2019] IEEE. Reprinted, with permission, from [.]).