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Found 13 papers, 5 papers with code
RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models
In this paper, we propose a new training-free and transferred-friendly text-to-image generation framework, namely RealCompo, which aims to leverage the advantages of text-to-image and layout-to-image models to enhance both realism and compositionality of the generated images.
AllSpark: A Multimodal Spatio-Temporal General Intelligence Model with Thirteen Modalities
We introduce the Language as Reference Framework (LaRF), a fundamental principle for constructing a multimodal unified model, aiming to strike a trade-off between the cohesion and autonomy among different modalities.
Privileged Prior Information Distillation for Image Matting
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance.
Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning
By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size.
Situational Perception Guided Image Matting
In this paper, we propose a Situational Perception Guided Image Matting (SPG-IM) method that mitigates subjective bias of matting annotations and captures sufficient situational perception information for better global saliency distilled from the visual-to-textual task.
Joint Multi-view Unsupervised Feature Selection and Graph Learning
Second, they often learn the similarity structure by either global structure learning or local structure learning, which lack the capability of graph learning with both global and local structural awareness.
Balanced and Hierarchical Relation Learning for One-Shot Object Detection
In this paper, we introduce the balanced and hierarchical learning for our detector.
GANet: Glyph-Attention Network for Few-Shot Font Generation
Font generation is a valuable but challenging task, it is time consuming and costly to design font libraries which cover all glyphs with various styles.
ExamGAN and Twin-ExamGAN for Exam Script Generation
There are opportunities to further improve the quality of generated exam scripts in various aspects.
Tripartite Information Mining and Integration for Image Matting
Regarding the nature of image matting, most researches have focused on solutions for transition regions.
Modal-aware Features for Multimodal Hashing
The modal-aware operation consists of a kernel network and an attention network.
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.
Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems
Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration.
