4 papers
Winner Team Mia at TextVQA Challenge 2021: Vision-and-Language Representation Learning with Pre-trained Sequence-to-Sequence Model
Yixuan Qiao, Hao Chen, Jun Wang +7
TextVQA requires models to read and reason about text in images to answer questions about them. Specifically, models need to incorporate a new modality of text present in the image…
Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists
Bojia Zi, Penghui Ruan, Marco Chen +7
Recent advancements in video generation have spurred the development of video editing techniques, which can be divided into inversion-based and end-to-end methods. However, current…
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities
Shaozhe Hao, Xuantong Liu, Xianbiao Qi +5
We introduce BiGR, a novel conditional image generation model using compact binary latent codes for generative training, focusing on enhancing both generation and representation ca…
Elucidating the design space of language models for image generation
Xuantong Liu, Shaozhe Hao, Xianbiao Qi +4
The success of autoregressive (AR) language models in text generation has inspired the computer vision community to adopt Large Language Models (LLMs) for image generation. However…