371 citations · 3.9k across the 110 of their papers we have counts for
112 papers
Multimodal Foundation Models: From Specialists to General-Purpose Assistants
Chunyuan Li, Zhe Gan, Zhengyuan Yang +4
This paper presents a comprehensive survey of the taxonomy and evolution of multimodal foundation models that demonstrate vision and vision-language capabilities, focusing on the t…
Guiding Instruction-based Image Editing via Multimodal Large Language Models
Tsu-Jui Fu, Wenze Hu, Xianzhi Du +3
Instruction-based image editing improves the controllability and flexibility of image manipulation via natural commands without elaborate descriptions or regional masks. However, h…
Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-0534
Chen-Chen Miao, Victoria Blackmon, Wei-Wei Zhu +41
We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M. We obtain t…
MOFI: Learning Image Representations from Noisy Entity Annotated Images
Wentao Wu, Aleksei Timofeev, Chen Chen +8
We present MOFI, Manifold OF Images, a new vision foundation model designed to learn image representations from noisy entity annotated images. MOFI differs from previous work in tw…
An Empirical Study of Multimodal Model Merging
Yi-Lin Sung, Linjie Li, Kevin Lin +3
Model merging (e.g., via interpolation or task arithmetic) fuses multiple models trained on different tasks to generate a multi-task solution. The technique has been proven success…
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
Jaemin Cho, Linjie Li, Zhengyuan Yang +3
Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets…
Generalized Decoding for Pixel, Image, and Language
Xueyan Zou, Zi-Yi Dou, Jianwei Yang +11
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) gen…
Exploring Discrete Diffusion Models for Image Captioning
Zixin Zhu, Yixuan Wei, Jianfeng Wang +7
The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one. We present a diffusion-based captioning model, dubbed the name…
GRiT: A Generative Region-to-text Transformer for Object Understanding
Jialian Wu, Jianfeng Wang, Zhengyuan Yang +4
This paper presents a Generative RegIon-to-Text transformer, GRiT, for object understanding. The spirit of GRiT is to formulate object understanding as <region, text> pairs, where…
ReCo: Region-Controlled Text-to-Image Generation
Zhengyuan Yang, Jianfeng Wang, Zhe Gan +8
Recently, large-scale text-to-image (T2I) models have shown impressive performance in generating high-fidelity images, but with limited controllability, e.g., precisely specifying…
Non-Contrastive Learning Meets Language-Image Pre-Training
Jinghao Zhou, Li Dong, Zhe Gan +2
Contrastive language-image pre-training (CLIP) serves as a de-facto standard to align images and texts. Nonetheless, the loose correlation between images and texts of web-crawled d…
Vision-Language Pre-training: Basics, Recent Advances, and Future Trends
Zhe Gan, Linjie Li, Chunyuan Li +3
This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categ…
Prompting GPT-3 To Be Reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang +4
Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applicatio…
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual Modeling
Tsu-Jui Fu, Linjie Li, Zhe Gan +4
Masked visual modeling (MVM) has been recently proven effective for visual pre-training. While similar reconstructive objectives on video inputs (e.g., masked frame modeling) have…
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Chenfei Wu, Jian Liang, Xiaowei Hu +6
In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or lo…
LAVENDER: Unifying Video-Language Understanding as Masked Language Modeling
Linjie Li, Zhe Gan, Kevin Lin +4
Unified vision-language frameworks have greatly advanced in recent years, most of which adopt an encoder-decoder architecture to unify image-text tasks as sequence-to-sequence gene…
Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone
Zi-Yi Dou, Aishwarya Kamath, Zhe Gan +9
Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as…
GIT: A Generative Image-to-text Transformer for Vision and Language
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu +6
In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generati…
K-LITE: Learning Transferable Visual Models with External Knowledge
Sheng Shen, Chunyuan Li, Xiaowei Hu +11
The new generation of state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. Thi…
MLP Architectures for Vision-and-Language Modeling: An Empirical Study
Yixin Nie, Linjie Li, Zhe Gan +6
We initiate the first empirical study on the use of MLP architectures for vision-and-language (VL) fusion. Through extensive experiments on 5 VL tasks and 5 robust VQA benchmarks,…
Injecting Semantic Concepts into End-to-End Image Captioning
Zhiyuan Fang, Jianfeng Wang, Xiaowei Hu +5
Tremendous progress has been made in recent years in developing better image captioning models, yet most of them rely on a separate object detector to extract regional features. Re…
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning
Jianfeng Wang, Xiaowei Hu, Zhe Gan +5
In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e.g., image or language) or multimodal inputs (e.g., the concat…
SwinBERT: End-to-End Transformers with Sparse Attention for Video Captioning
Kevin Lin, Linjie Li, Chung-Ching Lin +5
The canonical approach to video captioning dictates a caption generation model to learn from offline-extracted dense video features. These feature extractors usually operate on vid…
VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling
Tsu-Jui Fu, Linjie Li, Zhe Gan +4
A great challenge in video-language (VidL) modeling lies in the disconnection between fixed video representations extracted from image/video understanding models and downstream Vid…