3 citations · 6 across the 6 of their papers we have counts for
10 papers
Efficient Track Anything
Yunyang Xiong, Chong Zhou, Xiaoyu Xiang +10
Segment Anything Model 2 (SAM 2) has emerged as a powerful tool for video object segmentation and tracking anything. Key components of SAM 2 that drive the impressive video object…
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
Zechun Liu, Changsheng Zhao, Forrest Iandola +9
This paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top…
Taming Mode Collapse in Score Distillation for Text-to-3D Generation
Peihao Wang, Dejia Xu, Zhiwen Fan +8
Despite the remarkable performance of score distillation in text-to-3D generation, such techniques notoriously suffer from view inconsistency issues, also known as "Janus" artifact…
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
Peihao Wang, Zhiwen Fan, Dejia Xu +8
Score distillation has emerged as one of the most prevalent approaches for text-to-3D asset synthesis. Essentially, score distillation updates 3D parameters by lifting and back-pro…
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…
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…