17 papers
EdgeSAM: Prompt-In-the-Loop Distillation for SAM
Chong Zhou, Xiangtai Li, Chen Change Loy +1
This paper presents EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. O…
Decouple and Track: Benchmarking and Improving Video Diffusion Transformers for Motion Transfer
Qingyu Shi, Jianzong Wu, Jinbin Bai +4
The motion transfer task aims to transfer motion from a source video to newly generated videos, requiring the model to decouple motion from appearance. Previous diffusion-based met…
DreamRelation: Bridging Customization and Relation Generation
Qingyu Shi, Lu Qi, Jianzong Wu +4
Customized image generation is essential for creating personalized content based on user prompts, allowing large-scale text-to-image diffusion models to more effectively meet indiv…
Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language
Yicheng Chen, Xiangtai Li, Yining Li +4
Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks. However, leveraging these models to boos…
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation
Jianzong Wu, Chao Tang, Jingbo Wang +3
Story visualization, the task of creating visual narratives from textual descriptions, has seen progress with text-to-image generation models. However, these models often lack effe…
Explore In-Context Segmentation via Latent Diffusion Models
Chaoyang Wang, Xiangtai Li, Henghui Ding +5
In-context segmentation has drawn increasing attention with the advent of vision foundation models. Its goal is to segment objects using given reference images. Most existing appro…