749 citations · 1.3k across the 36 of their papers we have counts for
5 papers · 2 filters
Gen2Det: Generate to Detect
Saksham Suri, Fanyi Xiao, Animesh Sinha +4
Recently diffusion models have shown improvement in synthetic image quality as well as better control in generation. We motivate and present Gen2Det, a simple modular pipeline to c…
SqueezeSAM: User friendly mobile interactive segmentation
Balakrishnan Varadarajan, Bilge Soran, Forrest Iandola +6
The Segment Anything Model (SAM) has been a cornerstone in the field of interactive segmentation, propelling significant progress in generative AI, computational photography, and m…
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
Zhuoran Yu, Chenchen Zhu, Sean Culatana +3
Recent advances in generative deep learning have enabled the creation of high-quality synthetic images in text-to-image generation. Prior work shows that fine-tuning a pretrained d…
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
Yunyang Xiong, Bala Varadarajan, Lemeng Wu +9
Segment Anything Model (SAM) has emerged as a powerful tool for numerous vision applications. A key component that drives the impressive performance for zero-shot transfer and high…
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Jun Chen, Deyao Zhu, Xiaoqian Shen +7
Large language models have shown their remarkable capabilities as a general interface for various language-related applications. Motivated by this, we target to build a unified int…