699 citations · 1.1k across the 16 of their papers we have counts for
16 papers · 1 filter
Edit One for All: Interactive Batch Image Editing
Thao Nguyen, Utkarsh Ojha, Yuheng Li +2
In recent years, image editing has advanced remarkably. With increased human control, it is now possible to edit an image in a plethora of ways; from specifying in text what we wan…
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
Mu Cai, Haotian Liu, Dennis Park +4
While existing large vision-language multimodal models focus on whole image understanding, there is a prominent gap in achieving region-specific comprehension. Current approaches t…
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents
Shilong Liu, Hao Cheng, Haotian Liu +10
LLaVA-Plus is a general-purpose multimodal assistant that expands the capabilities of large multimodal models. It maintains a skill repository of pre-trained vision and vision-lang…
Improved Baselines with Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Yuheng Li +1
Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal co…
Aligning Large Multimodal Models with Factually Augmented RLHF
Zhiqing Sun, Sheng Shen, Shengcao Cao +9
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounde…
An Empirical Study of Scaling Instruct-Tuned Large Multimodal Models
Yadong Lu, Chunyuan Li, Haotian Liu +3
Visual instruction tuning has recently shown encouraging progress with open-source large multimodal models (LMM) such as LLaVA and MiniGPT-4. However, most existing studies of open…
Benchmarking and Analyzing Generative Data for Visual Recognition
Bo Li, Haotian Liu, Liangyu Chen +3
Advancements in large pre-trained generative models have expanded their potential as effective data generators in visual recognition. This work delves into the impact of generative…
Generate Anything Anywhere in Any Scene
Yuheng Li, Haotian Liu, Yangming Wen +1
Text-to-image diffusion models have attracted considerable interest due to their wide applicability across diverse fields. However, challenges persist in creating controllable mode…
LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Chunyuan Li, Cliff Wong, Sheng Zhang +6
Conversational generative AI has demonstrated remarkable promise for empowering biomedical practitioners, but current investigations focus on unimodal text. Multimodal conversation…
Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Qingyang Wu +1
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored i…
Learning Customized Visual Models with Retrieval-Augmented Knowledge
Haotian Liu, Kilho Son, Jianwei Yang +4
Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-s…
GLIGEN: Open-Set Grounded Text-to-Image Generation
Yuheng Li, Haotian Liu, Qingyang Wu +5
Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propos…
End-to-End Instance Edge Detection
Xueyan Zou, Haotian Liu, Yong Jae Lee
Edge detection has long been an important problem in the field of computer vision. Previous works have explored category-agnostic or category-aware edge detection. In this paper, w…
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Chunyuan Li, Haotian Liu, Liunian Harold Li +8
Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual mode…
Masked Discrimination for Self-Supervised Learning on Point Clouds
Haotian Liu, Mu Cai, Yong Jae Lee
Masked autoencoding has achieved great success for self-supervised learning in the image and language domains. However, mask based pretraining has yet to show benefits for point cl…
YolactEdge: Real-time Instance Segmentation on the Edge
Haotian Liu, Rafael A. Rivera Soto, Fanyi Xiao +1
We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on…