Search Results for author:
Found 8 papers, 6 papers with code
StyleBooth: Image Style Editing with Multimodal Instruction
We integrate encoded textual instruction and image exemplar as a unified condition for diffusion model, enabling the editing of original image following multimodal instructions.
Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance
The process involves (i) Locate: concatenating the noise with masked scene image to achieve precise regional editing, (ii) Assign: employing decoupled cross-attention mechanism to accommodate multi-modal guidance, and (iii) Refine: using a novel RefineNet to supplement subject details.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Image diffusion models have been utilized in various tasks, such as text-to-image generation and controllable image synthesis.
A generalized likelihood-weighted optimal sampling algorithm for rare-event probability quantification
In this work, we introduce a new acquisition function for sequential sampling to efficiently quantify rare-event statistics of an input-to-response (ItR) system with given input probability and expensive function evaluations.
Scanning Only Once: An End-to-end Framework for Fast Temporal Grounding in Long Videos
Video temporal grounding aims to pinpoint a video segment that matches the query description.
VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval
Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models.
Self-supervised Video Retrieval Transformer Network
In addition, most retrieval systems are based on frame-level features for video similarity searching, making it expensive both storage wise and search wise.
Sequential Bayesian experimental design for estimation of extreme-event probability in stochastic dynamical systems
We consider an input-to-response (ItR) system characterized by (1) parameterized input with a known probability distribution and (2) stochastic ItR function with heteroscedastic randomness.
Methodology Fluid Dynamics
