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20152023
most citedVariational Autoencoder for Deep Learning of Images, Labels and Captions

371 citations · 3.9k across the 110 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 25 cited

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…

cs.CV2023★ 11 cited

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…

astro-ph.HE2023★ 7 cited

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…

cs.CV2023★ 3 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 1 cited

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…