3 citations · 3 across the 6 of their papers we have counts for
6 papers
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Zizheng Yang, Hu Yu, Bing Li +3
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation
Shuwei Shi, Biao Gong, Xi Chen +9
The image-to-video (I2V) generation is conditioned on the static image, which has been enhanced recently by the motion intensity as an additional control signal. These motion-aware…
Text Prompt is Not Enough: Sound Event Enhanced Prompt Adapter for Target Style Audio Generation
Chenxu Xiong, Ruibo Fu, Shuchen Shi +9
Current mainstream audio generation methods primarily rely on simple text prompts, often failing to capture the nuanced details necessary for multi-style audio generation. To addre…
DiffLoss: unleashing diffusion model as constraint for training image restoration network
Jiangtong Tan, Feng Zhao
Image restoration aims to enhance low quality images, producing high quality images that exhibit natural visual characteristics and fine semantic attributes. Recently, the diffusio…
Prototype Clustered Diffusion Models for Versatile Inverse Problems
Jinghao Zhang, Zizheng Yang, Qi Zhu +1
Diffusion models have made remarkable progress in solving various inverse problems, attributing to the generative modeling capability of the data manifold. Posterior sampling from…
Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification
Zizheng Yang, Xin Jin, Kecheng Zheng +1
Existing person re-identification (ReID) methods typically directly load the pre-trained ImageNet weights for initialization. However, as a fine-grained classification task, ReID i…