Usually, correspondences are 2D-to-2D and the pose we estimate is defined only up to scale.
The key idea is to eliminate unsafe visual representations from the model regardless of the text input.
We investigate efficient methods for training Large Language Models (LLMs) to possess capabilities in multiple specialized domains, such as coding, math reasoning and world knowledge.
Ranked #30 on Question Answering on TriviaQA
This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation.
LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10x-50x more parameter-efficient than prior state-of-the-art PEFTs.
It comprises two essential components: the localization module (LM) and the reconstruction module (RM) with our proposed bilateral reference (BiRef).
Ranked #1 on RGB Salient Object Detection on HRSOD (using extra training data)
Camouflaged Object Segmentation Dichotomous Image Segmentation +3
To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study.
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 propose an \textit{OmniFusion} model based on a pretrained LLM and adapters for visual modality.
Ranked #36 on Visual Question Answering on MM-Vet
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.