416 citations · 438 across the 9 of their papers we have counts for
5 papers · 1 filter
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
Yuxuan Zhang, Tianheng Cheng, Lianghui Zhu +7
Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text…
ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention
Bencheng Liao, Xinggang Wang, Lianghui Zhu +2
Recently, linear complexity sequence modeling networks have achieved modeling capabilities similar to Vision Transformers on a variety of computer vision tasks, while using fewer F…
DiG: Scalable and Efficient Diffusion Models with Gated Linear Attention
Lianghui Zhu, Zilong Huang, Bencheng Liao +4
Diffusion models with large-scale pre-training have achieved significant success in the field of visual content generation, particularly exemplified by Diffusion Transformers (DiT)…
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
Lianghui Zhu, Junwei Zhou, Yan Liu +3
Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem. It significantly reduces human labeling costs and traditionally relie…
Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Lianghui Zhu, Bencheng Liao, Qian Zhang +3
Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile…