Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration.
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains.
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0. 1s.
Evaluating outputs of large language models (LLMs) is challenging, requiring making -- and making sense of -- many responses.
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters.
We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue.
Ranked #1 on Audio Classification on ESC-50 (using extra training data)