PUG: Photorealistic and Semantically Controllable Synthetic Data for
Representation Learning

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning

8 August 2023

Shashank Shekhar

Diane Bouchacourt

Pascal Vincent

Papers citing "PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning"

6 / 6 papers shown

Title
CinePile: A Long Video Question Answering Dataset and Benchmark Ruchit Rawal Khalid Saifullah Ronen Basri David Jacobs Gowthami Somepalli Tom Goldstein 38 39 0 14 May 2024
Back to 3D: Few-Shot 3D Keypoint Detection with Back-Projected 2D Features Thomas Wimmer Peter Wonka M. Ovsjanikov 19 8 0 29 Nov 2023
Equivariant Similarity for Vision-Language Foundation Models Tan Wang Kevin Qinghong Lin Linjie Li Chung-Ching Lin Zhengyuan Yang Hanwang Zhang Zicheng Liu Lijuan Wang CoGe 33 44 0 25 Mar 2023
Why is Winoground Hard? Investigating Failures in Visuolinguistic Compositionality Anuj Diwan Layne Berry Eunsol Choi David F. Harwath Kyle Mahowald CoGe 92 41 0 01 Nov 2022
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Junnan Li Dongxu Li Caiming Xiong S. Hoi MLLM BDL VLM CLIP 382 4,010 0 28 Jan 2022
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 198 477 0 01 Oct 2021