We argue that representations in AI models, particularly deep networks, are converging.
However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose challenges for researchers and the community to easily experiment with, understand, and assess the latest advancements.
For vision tasks, as image classification does not align with either characteristic, we hypothesize that Mamba is not necessary for this task; Detection and segmentation tasks are also not autoregressive, yet they adhere to the long-sequence characteristic, so we believe it is still worthwhile to explore Mamba's potential for these tasks.
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
Imitation Learning (IL) holds great promise for enabling agile locomotion in embodied agents.
Visual language models (VLMs) rapidly progressed with the recent success of large language models.
Ranked #24 on Visual Question Answering on MM-Vet
With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence.
Low-rank adaptation is a popular parameter-efficient fine-tuning method for large language models.
As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research.
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.