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Found 7 papers, 4 papers with code
DUBLIN -- Document Understanding By Language-Image Network
Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images.
Ranked #1 on
Visual Question Answering (VQA)
on DeepForm
ArK: Augmented Reality with Knowledge Interactive Emergent Ability
In this study, we develop an infinite agent that learns to transfer knowledge memory from general foundation models (e. g. GPT4, DALLE) to novel domains or scenarios for scene understanding and generation in the physical or virtual world.
Language Is Not All You Need: Aligning Perception with Language Models
A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.
Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks
A big convergence of language, vision, and multimodal pretraining is emerging.
Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks
A big convergence of language, vision, and multimodal pretraining is emerging.
Ranked #1 on
Visual Reasoning
on NLVR2 Test
Training Vision-Language Transformers from Captions
Vision-Language Transformers can be learned without low-level human labels (e. g. class labels, bounding boxes, etc).
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network.
Ranked #2 on
Image Retrieval
on PhotoChat
