Canva CORE CN
Staff Research Scientist, working on multi-layer image generation, design editing, etc.
Staff Research Scientist at Canva · Ph.D.
Multimodal learning · Image & video generation · Fine-grained recognition
ylbai AT outlook DOT com Google Scholar
I am a Staff Research Scientist at Canva, where I work on multi-layer image generation and design editing. Previously, I was a research manager at Du Xiaoman Financial, leading multimodal content generation initiatives spanning text-to-image, image-to-video, and text-to-speech, and before that a senior researcher at JD AI Research. I received my Ph.D. through the joint program of Microsoft Research Asia and Harbin Institute of Technology. My research interests include multimodal learning, image and video generation, and fine-grained visual recognition.
Staff Research Scientist, working on multi-layer image generation, design editing, etc.
Research Manager, leading multimodal content generation initiatives, including text-to-image (T2I), image-to-video (I2V), text-to-speech (TTS), any-to-any multimodal LLMs, and more.
Senior Researcher, working on SnapShop (visual search), VQA, fine-grained recognition, relationship modeling in images, 3D imaging, etc.
Research intern, working on deep learning for image representation and computer vision.
Research intern, working on document retrieval results re-ranking.
Supervisors: Wei-Ying Ma and Tiejun Zhao.
Thesis: Research and Applications of Image-Text Multimodal Correlation Learning.
School of Computer Science and Technology. Supervisor: Sheng Li.
School of Computer Science and Technology.
A full list is available on Google Scholar.
V2Flow: Unifying Visual Tokenization and Large Language Model Vocabularies for Autoregressive Image Generation
STAR: Scale-wise Text-to-image Generation via Auto-Regressive Representations
Deep Equilibrium Multimodal Fusion
Products-10K: A Large-scale Product Recognition Dataset
Masked Region Transformer for Layered Image Generation and Editing at Scale
Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation
PreferThinker: Reasoning-based Personalized Image Preference Assessment
Enhancing Reward Models for High-quality Image Generation: Beyond Text-Image Alignment
Interactive Conversational Head Generation
Uniform Attention Maps: Boosting Image Fidelity in Reconstruction and Editing
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models
Teaching Masked Autoencoder With Strong Augmentations
Dynamic Prompt Optimizing for Text-to-Image Generation
CAMEL: CAusal Motion Enhancement Tailored for Lifting Text-driven Video Editing
Learning and Evaluating Human Preferences for Conversational Head Generation
Augmentation Pathways Network for Visual Recognition
Visualizing and Understanding Patch Interactions in Vision Transformer
Visual-Aware Text-to-Speech
Boosting Generic Visual-Linguistic Representation with Dynamic Contexts
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Directional Self-supervised Learning for Heavy Image Augmentations
Exploiting Relationship for Complex-scene Image Generation
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
VrR-VG: Refocusing Visually-Relevant Relationships
Destruction and Construction Learning for Fine-grained Image Recognition
Deep Attention Neural Tensor Network for Visual Question Answering
Automatic Data Augmentation from Massive Web Images for Deep Visual Recognition
Convolutional Neural Networks for Posed and Spontaneous Expression Recognition
Improve Dog Recognition by Mining More Information from Both Click-through Logs and Pre-trained Models
Automatic Image Dataset Construction from Click-through Logs Using Deep Neural Network
Learning Cross Space Mapping via DNN using Large Scale Click-through Logs
RC-NET: A General Framework for Incorporating Knowledge into Word Representations
Bag-of-Words Based Deep Neural Network for Image Retrieval
DNN Flow: DNN Feature Pyramid Based Image Matching
Visualizing and Comparing Convolutional Neural Networks
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Cross-lingual Projections between Languages from Different Families
Learning Domain Differences Automatically for Dependency Parsing Adaptation
* denotes interns mentored by me.
The PyTorch implementation of our research paper “Destruction and Construction Learning for Fine-grained Image Recognition” (CVPR 2019). The first-place solutions for the CVPR 2020 AliProducts Challenge: Large-scale Product Recognition [1], the CVPR 2019 iMaterialist Challenge on Product Recognition [2], and the CVPR 2019 Fieldguide Challenge: Moths and Butterflies [3].
Official solution for the first Conversational Head Generation Challenge, including vivid talking-head video generation, responsive listening-head video generation and implementations of 11 quantitative evaluation metrics.
Repositories of AIGC research projects, including STAR-T2I (scale-wise text-to-image model), V2Flow (vector-quantized image tokenizer based on LLM vocabularies), etc.