416 citations · 438 across the 9 of their papers we have counts for
10 papers · 1 filter
Multimodal Flow: Unified Flow Modeling of Language and Vision in Embedding Spaces
Hongyuan Tao, Xinggang Wang, Lianghui Zhu +7
We present Multimodal Flow, a fully continuous generative model of language and vision. Most unified multimodal models either model both language and quantized images as discrete t…
Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
Gangwei Xu, Haotong Lin, Hongcheng Luo +11
This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds fro…
TransLight: Image-Guided Customized Lighting Control with Generative Decoupling
Zongming Li, Lianghui Zhu, Haocheng Shen +3
Most existing illumination-editing approaches fail to simultaneously provide customized control of light effects and preserve content integrity. This makes them less effective for…
LENS: Learning to Segment Anything with Unified Reinforced Reasoning
Lianghui Zhu, Bin Ouyang, Yuxuan Zhang +8
Text-prompted image segmentation enables fine-grained visual understanding and is critical for applications such as human-computer interaction and robotics. However, existing super…
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
Rui Hu, Lianghui Zhu, Yuxuan Zhang +7
Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its immense potential for bridging the gap between v…
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…