4 citations · 5 across the 4 of their papers we have counts for
5 papers
EvoGM: Learning to Merge LLMs via Evolutionary Generative Optimization
Tao Jiang, Xinmeng Yu, Chenhao Yi +5
Evolutionary model merging provides a powerful framework for the automated, training-free composition of LLMs through parameter-space search. However, existing methods predominantl…
FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models
Zheng Chong, Yanwei Lei, Shiyue Zhang +7
Despite its great potential, virtual try-on technology is hindered from real-world application by two major challenges: the inability of current methods to support multi-reference…
HumanPCR: Probing MLLM Capabilities in Diverse Human-Centric Scenes
Keliang Li, Hongze Shen, Hao Shi +9
The aspiration for artificial general intelligence, fueled by the rapid progress of multimodal models, demands human-comparable performance across diverse environments. We propose…
CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation
Zheng Chong, Wenqing Zhang, Shiyue Zhang +6
Virtual try-on (VTON) technology has gained attention due to its potential to transform online retail by enabling realistic clothing visualization of images and videos. However, mo…
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
Zheng Chong, Xiao Dong, Haoxiang Li +6
Virtual try-on methods based on diffusion models achieve realistic effects but often require additional encoding modules, a large number of training parameters, and complex preproc…