Search Results for author:
Found 9 papers, 8 papers with code
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on SUN
Image Classification
Zero-Shot Transfer Image Classification
Generative Multimodal Models are In-Context Learners
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #19 on
Visual Question Answering
on MM-Vet
CapsFusion: Rethinking Image-Text Data at Scale
To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions.
Generative Pretraining in Multimodality
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.
Ranked #1 on
Visual Question Answering
on VQA v2
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs.
Ranked #4 on
Zero-Shot Transfer Image Classification
on Food-101
EVA-02: A Visual Representation for Neon Genesis
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.
Thermal Infrared Image Inpainting via Edge-Aware Guidance
In this paper, we propose a novel task -- Thermal Infrared Image Inpainting, which aims to reconstruct missing regions of TIR images.
The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions
Multi-agent behavior modeling aims to understand the interactions that occur between agents.
