16 citations · 36 across the 28 of their papers we have counts for
31 papers
Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision
Weijian Jian, Xiaoyue Zhang, Bin Xiao +5
The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised methods attempt to learn obje…
RevealLayer: Disentangling Hidden and Visible Layers via Occlusion-Aware Image Decomposition
Binhao Wang, Shihao Zhao, Bo Cheng +6
Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images remains challenging due to dif…
RPiAE: A Representation-Pivoted Autoencoder Enhancing Both Image Generation and Editing
Yue Gong, Hongyu Li, Shanyuan Liu +8
Diffusion models have become the dominant paradigm for image generation and editing, with latent diffusion models shifting denoising to a compact latent space for efficiency and sc…
AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation
Tongfei Chen, Shuo Yang, Yuguang Yang +7
Referring Image Segmentation (RIS) aims to segment the object in an image uniquely referred to by a natural language expression. However, RIS training often contains hard-to-align…
Generation-Augmented Generation: A Plug-and-Play Framework for Private Knowledge Injection in Large Language Models
Rongji Li, Jian Xu, Yi Chen +7
In domains such as materials science, biomedicine, and finance, high-stakes deployment of large language models (LLMs) requires injecting private, domain-specific knowledge that is…
RefTon: Reference person shot assist virtual Try-on
Liuzhuozheng Li, Yue Gong, Shanyuan Liu +7
We introduce RefTon, a flux-based person-to-person virtual try-on framework that enhances garment realism through unpaired visual references. Unlike conventional approaches that re…