activity
20182023
most citedRethinking Local Perception in Lightweight Vision Transformer

33 citations · 98 across the 14 of their papers we have counts for

collaborators

14 papers

cs.CV2023★ 12 cited

Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis

Linsen Song, Wayne Wu, Chaoyou Fu +2

Existing automated dubbing methods are usually designed for Professionally Generated Content (PGC) production, which requires massive training data and training time to learn a per…

cs.CV2023

Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification

Haichao Shi, Mandi Luo, Xiao-Yu Zhang +1

Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared featu…

cs.SD2023★ 1 cited

Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion

Haogeng Liu, Tao Wang, Jie Cao +2

Denoising Diffusion Probabilistic Models have shown extraordinary ability on various generative tasks. However, their slow inference speed renders them impractical in speech synthe…

cs.CV2023★ 4 cited

Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

Rui Wang, Peipei Li, Huaibo Huang +3

We present a novel language-driven ordering alignment method for ordinal classification. The labels in ordinal classification contain additional ordering relations, making them pro…

cs.CV2023★ 13 cited

Lightweight Vision Transformer with Bidirectional Interaction

Qihang Fan, Huaibo Huang, Xiaoqiang Zhou +1

Recent advancements in vision backbones have significantly improved their performance by simultaneously modeling images' local and global contexts. However, the bidirectional inter…

cs.CV2023★ 33 cited

Rethinking Local Perception in Lightweight Vision Transformer

Qihang Fan, Huaibo Huang, Jiyang Guan +1

Vision Transformers (ViTs) have been shown to be effective in various vision tasks. However, resizing them to a mobile-friendly size leads to significant performance degradation. T…