Zobrazeno 1 - 10
of 382
pro vyhledávání: '"Jing, Zhongliang"'
Currently, personalized image generation methods mostly require considerable time to finetune and often overfit the concept resulting in generated images that are similar to custom concepts but difficult to edit by prompts. We propose an effective an
Externí odkaz:
http://arxiv.org/abs/2403.11284
Autor:
Zhang, Yuxuan, Song, Yiren, Liu, Jiaming, Wang, Rui, Yu, Jinpeng, Tang, Hao, Li, Huaxia, Tang, Xu, Hu, Yao, Pan, Han, Jing, Zhongliang
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging. Addressing this, we introduce the SSR-Encoder, a novel architecture design
Externí odkaz:
http://arxiv.org/abs/2312.16272
Considerable progress has recently been made in leveraging CLIP (Contrastive Language-Image Pre-Training) models for text-guided image manipulation. However, all existing works rely on additional generative models to ensure the quality of results, be
Externí odkaz:
http://arxiv.org/abs/2212.02122
Eliminating ghosting artifacts due to moving objects is a challenging problem in high dynamic range (HDR) imaging. In this letter, we present a hybrid model consisting of a convolutional encoder and a Transformer decoder to generate ghost-free HDR im
Externí odkaz:
http://arxiv.org/abs/2212.00595