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Found 5 papers, 2 papers with code
Vision-Language Matching for Text-to-Image Synthesis via Generative Adversarial Networks
To address this issue, we propose a novel Vision-Language Matching strategy for text-to-image synthesis, named VLMGAN*, which introduces a dual vision-language matching mechanism to strengthen the image quality and semantic consistency.
A Unified Two-Stage Group Semantics Propagation and Contrastive Learning Network for Co-Saliency Detection
With the design of negative samples, the noise objects are suppressed.
Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation Learning and Retrieval
Concretely, the first module is a weight-sharing transformer that builds on the head of the visual and textual encoders, aiming to semantically align text and image.
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-Training for Vision-Language Representation
There are two key designs in it, one is the weight-sharing transformer on top of the visual and textual encoders to align text and image semantically, the other is three kinds of contrastive learning designed for sharing knowledge between different modalities.
Learning Dual Semantic Relations with Graph Attention for Image-Text Matching
Thus, a novel multi-level semantic relations enhancement approach named Dual Semantic Relations Attention Network(DSRAN) is proposed which mainly consists of two modules, separate semantic relations module and the joint semantic relations module.
