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Found 6 papers, 1 papers with code
Language-agnostic Semantic Consistent Text-to-Image Generation
Recent GAN-based text-to-image generation models have advanced that they can generate photo-realistic images matching semantically with descriptions.
Generative Adversarial Network
Multi-lingual Text-to-Image Generation
+2
Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models
In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of our knowledge.
SGRAM: Improving Scene Graph Parsing via Abstract Meaning Representation
To this end, we design a simple yet effective two-stage scene graph parsing framework utilizing abstract meaning representation, SGRAM (Scene GRaph parsing via Abstract Meaning representation): 1) transforming a textual description of an image into an AMR graph (Text-to-AMR) and 2) encoding the AMR graph into a Transformer-based language model to generate a scene graph (AMR-to-SG).
Hypergraph Transformer: Weakly-supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering
Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself.
Toward a Human-Level Video Understanding Intelligence
In this paper, we propose the Video Turing Test to provide effective and practical assessments of video understanding intelligence as well as human-likeness evaluation of AI agents.
Toward General Scene Graph: Integration of Visual Semantic Knowledge with Entity Synset Alignment
In experiment, the integrated scene graph is applied to the image-caption retrieval task as a down-stream task.
