activity
20142024
most citedGrad-CAM: Why did you say that?

329 citations · 550 across the 13 of their papers we have counts for

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Showing 2023Show all

5 papers · 1 filter

cs.CV20232 cited

Emu Edit: Precise Image Editing via Recognition and Generation Tasks

Shelly Sheynin, Adam Polyak, Uriel Singer +5

Instruction-based image editing holds immense potential for a variety of applications, as it enables users to perform any editing operation using a natural language instruction. Ho…

cs.CV202330 cited

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…

cs.CV20231 cited

Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation

Samaneh Azadi, Akbar Shah, Thomas Hayes +2

Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models…

cs.CV20232 cited

Text-Conditional Contextualized Avatars For Zero-Shot Personalization

Samaneh Azadi, Thomas Hayes, Akbar Shah +3

Recent large-scale text-to-image generation models have made significant improvements in the quality, realism, and diversity of the synthesized images and enable users to control t…

cs.CV202323 cited

Text-To-4D Dynamic Scene Generation

Uriel Singer, Shelly Sheynin, Adam Polyak +8

We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), whi…