78 citations · 185 across the 11 of their papers we have counts for
12 papers
ChartGalaxy: A Dataset for Infographic Chart Understanding and Generation
Zhen Li, Duan Li, Yukai Guo +9
Infographic charts are a powerful medium for communicating abstract data by combining visual elements (e.g., charts, images) with textual information. However, their visual and str…
Optimisation-Based Multi-Modal Semantic Image Editing
Bowen Li, Yongxin Yang, Steven McDonagh +3
Image editing affords increased control over the aesthetics and content of generated images. Pre-existing works focus predominantly on text-based instructions to achieve desired im…
Learning to Model Multimodal Semantic Alignment for Story Visualization
Bowen Li, Thomas Lukasiewicz
Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story, where the images should be realistic and keep global consistency acros…
Word-Level Fine-Grained Story Visualization
Bowen Li, Thomas Lukasiewicz
Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story with a global consistency across dynamic scenes and characters. Current…
Lightweight Long-Range Generative Adversarial Networks
Bowen Li, Thomas Lukasiewicz
In this paper, we introduce novel lightweight generative adversarial networks, which can effectively capture long-range dependencies in the image generation process, and produce hi…
Memory-Driven Text-to-Image Generation
Bowen Li, Philip H. S. Torr, Thomas Lukasiewicz
We introduce a memory-driven semi-parametric approach to text-to-image generation, which is based on both parametric and non-parametric techniques. The non-parametric component is…
Unity Perception: Generate Synthetic Data for Computer Vision
Steve Borkman, Adam Crespi, Saurav Dhakad +12
We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use an…
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
Bowen Li, Xiaojuan Qi, Philip H. S. Torr +1
We propose a novel lightweight generative adversarial network for efficient image manipulation using natural language descriptions. To achieve this, a new word-level discriminator…
Knowledge Graph Extraction from Videos
Louis Mahon, Eleonora Giunchiglia, Bowen Li +1
Nearly all existing techniques for automated video annotation (or captioning) describe videos using natural language sentences. However, this has several shortcomings: (i) it is ve…
Image-to-Image Translation with Text Guidance
Bowen Li, Xiaojuan Qi, Philip H. S. Torr +1
The goal of this paper is to embed controllable factors, i.e., natural language descriptions, into image-to-image translation with generative adversarial networks, which allows tex…
ManiGAN: Text-Guided Image Manipulation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
The goal of our paper is to semantically edit parts of an image matching a given text that describes desired attributes (e.g., texture, colour, and background), while preserving ot…
Controllable Text-to-Image Generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz +1
In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control part…