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Found 8 papers, 4 papers with code
AsyncDSB: Schedule-Asynchronous Diffusion Schrödinger Bridge for Image Inpainting
Although these methods have shown superior performance, in this paper, we find that 1) existing methods suffer from a schedule-restoration mismatching issue, i. e., the theoretical schedule and practical restoration processes usually exist a large discrepancy, which theoretically results in the schedule not fully leveraged for restoring images; and 2) the key reason causing such issue is that the restoration process of all pixels are actually asynchronous but existing methods set a synchronous noise schedule to them, i. e., all pixels shares the same noise schedule.
FashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-training
We propose a method for fine-grained fashion vision-language pre-training based on fashion Symbols and Attributes Prompt (FashionSAP) to model fine-grained multi-modalities fashion attributes and characteristics.
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training
In this paper, we propose a fine-grained VLP scheme without object annotations from the linguistic perspective.
VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval
Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE).
Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning
In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer.
Prototype Completion with Primitive Knowledge for Few-Shot Learning
To avoid the prototype completion error caused by primitive knowledge noises or class differences, we further develop a Gaussian based prototype fusion strategy that combines the mean-based and completed prototypes by exploiting the unlabeled samples.
Text-Guided Neural Image Inpainting
To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet).
Semi-supervised Visual Feature Integration for Pre-trained Language Models
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference
Natural Language Understanding
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