24 research outputs found
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
Text-to-video editing aims to edit the visual appearance of a source video
conditional on textual prompts. A major challenge in this task is to ensure
that all frames in the edited video are visually consistent. Most recent works
apply advanced text-to-image diffusion models to this task by inflating 2D
spatial attention in the U-Net into spatio-temporal attention. Although
temporal context can be added through spatio-temporal attention, it may
introduce some irrelevant information for each patch and therefore cause
inconsistency in the edited video. In this paper, for the first time, we
introduce optical flow into the attention module in the diffusion model's U-Net
to address the inconsistency issue for text-to-video editing. Our method,
FLATTEN, enforces the patches on the same flow path across different frames to
attend to each other in the attention module, thus improving the visual
consistency in the edited videos. Additionally, our method is training-free and
can be seamlessly integrated into any diffusion-based text-to-video editing
methods and improve their visual consistency. Experiment results on existing
text-to-video editing benchmarks show that our proposed method achieves the new
state-of-the-art performance. In particular, our method excels in maintaining
the visual consistency in the edited videos.Comment: Project page: https://flatten-video-editing.github.io
A Vector Angle-Based Evolutionary Algorithm for Unconstrained Many-Objective Optimization
A many-objective particle swarm optimizer with leaders selected from historical solutions by using scalar projections
A vector angle based evolutionary algorithm for unconstrained many-objective optimization
A vector angle based evolutionary algorithm for unconstrained many-objective optimization
Configuring software product lines by combining many-objective optimization and SAT solvers
Configuring software product lines by combining many-objective optimization and SAT solvers
A multiobjective evolutionary algorithm based on objective-space localization selection
This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing with problems with irregular Pareto front. The proposed algorithm does not need to deal with the issues of predefining weight vectors and calculating indicators in the search process. It is mainly based on the thought of adaptively selecting multiple promising search directions according to crowdedness information in local objective spaces. Concretely, the proposed algorithm attempts to dynamically delete an individual of poor quality until enough individuals survive into the next generation. In this environmental selection process, the proposed algorithm considers two or three individuals in the most crowded area, which is determined by the local information in objective space, according to a probability selection mechanism, and deletes the worst of them from the current population. Thus, these surviving individuals are representative of promising search directions. The performance of the proposed algorithm is verified and compared with seven state-of-the-art algorithms [including four general multi/many-objective EAs and three algorithms specially designed for dealing with problems with irregular Pareto-optimal front (PF)] on a variety of complicated problems with different numbers of objectives ranging from 2 to 15. Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives.This work was supported in part by the National Natural Science Foundation of China under Grant 61773410 and Grant 61673403; in part by the Science and Technology Program of Guangzhou under Grant 202002030355; and in part by the Fundamental Research Funds for the Central Universities under Grant 2019MS088