This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e. g., keyboard and mouse operations).
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made.
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains.
Ranked #1 on
Continual Learning
on AIDS
(using extra training data)
We show that while diffusion loss is ineffective, end-to-end training can be unlocked through the representation-alignment (REPA) loss -- allowing both VAE and diffusion model to be jointly tuned during the training process.
In this paper, we tackle the task of blurry video super-resolution (BVSR), aiming to generate high-resolution (HR) videos from low-resolution (LR) and blurry inputs.
To address this, our VAE decoder is tasked with both latent-to-pixel conversion and the final denoising step, producing the clean result directly in pixel space.
Chain-of-Thought (CoT) prompting enhances the reasoning of large language models (LLMs) by decomposing problems into sequential steps, mimicking human logic and reducing errors.
This work presents SimpleAR, a vanilla autoregressive visual generation framework without complex architecure modifications.
Monocular 3D estimation is crucial for visual perception.
Ranked #2 on
Monocular Depth Estimation
on KITTI Eigen split