We present OOTDiffusion, a novel network architecture for realistic and controllable image-based virtual try-on (VTON).
Animating a still image offers an engaging visual experience.
A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets.
Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components.
Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications.
LLM2LLM (1) fine-tunes a baseline student LLM on the initial seed data, (2) evaluates and extracts data points that the model gets wrong, and (3) uses a teacher LLM to generate synthetic data based on these incorrect data points, which are then added back into the training data.
Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models.
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics.
Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs.
We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks.