most citedReduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

58 citations · 72 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV20232 cited

Feature Proliferation -- the "Cancer" in StyleGAN and its Treatments

Shuang Song, Yuanbang Liang, Jing Wu +2

Despite the success of StyleGAN in image synthesis, the images it synthesizes are not always perfect and the well-known truncation trick has become a standard post-processing techn…

cs.CV2023

Motion-R3: Fast and Accurate Motion Annotation via Representation-based Representativeness Ranking

Jubo Yu, Tianxiang Ren, Shihui Guo +6

In this paper, we follow a data-centric philosophy and propose a novel motion annotation method based on the inherent representativeness of motion data in a given dataset. Specific…

cs.CV20232 cited

Diverse Motion In-betweening with Dual Posture Stitching

Tianxiang Ren, Jubo Yu, Shihui Guo +5

In-betweening is a technique for generating transitions given initial and target character states. The majority of existing works require multiple (often 10) frames as input, wh…

cs.MM202358 cited

Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection

Wei Zhou, Guanghui Yue, Ruizeng Zhang +2

Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. To evaluate the perceptual quality of various point clouds, in t…

cs.CV20225 cited

Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels

Ganlong Zhao, Guanbin Li, Yipeng Qin +2

Deep models trained with noisy labels are prone to over-fitting and struggle in generalization. Most existing solutions are based on an ideal assumption that the label noise is cla…

cs.CV20225 cited

Multi-level Consistency Learning for Semi-supervised Domain Adaptation

Zizheng Yan, Yushuang Wu, Guanbin Li +3

Semi-supervised domain adaptation (SSDA) aims to apply knowledge learned from a fully labeled source domain to a scarcely labeled target domain. In this paper, we propose a Multi-l…