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20212026
most citedEnhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations

3 citations · 3 across the 11 of their papers we have counts for

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cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

RzenEmbed: Towards Comprehensive Multimodal Retrieval

Weijian Jian, Yajun Zhang, Dawei Liang +4

The rapid advancement of Multimodal Large Language Models (MLLMs) has extended CLIP-based frameworks to produce powerful, universal embeddings for retrieval tasks. However, existin…

cs.CV2025

CTA-Flux: Integrating Chinese Cultural Semantics into High-Quality English Text-to-Image Communities

Yue Gong, Shanyuan Liu, Liuzhuozheng Li +7

We proposed the Chinese Text Adapter-Flux (CTA-Flux). An adaptation method fits the Chinese text inputs to Flux, a powerful text-to-image (TTI) generative model initially trained o…

cs.CV2025

NanoControl: A Lightweight Framework for Precise and Efficient Control in Diffusion Transformer

Shanyuan Liu, Jian Zhu, Junda Lu +8

Diffusion Transformers (DiTs) have demonstrated exceptional capabilities in text-to-image synthesis. However, in the domain of controllable text-to-image generation using DiTs, mos…