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20202025
most citedDiffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

25 citations · 65 across the 12 of their papers we have counts for

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6 papers · 1 filter

cs.CV2025

Zero-Shot Vision Encoder Grafting via LLM Surrogates

Kaiyu Yue, Vasu Singla, Menglin Jia +6

Vision language models (VLMs) typically pair a modestly sized vision encoder with a large language model (LLM), e.g., Llama-70B, making the decoder the primary computational burden…

cs.CV2024

Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives

Alex Hanson, Allen Tu, Geng Lin +3

3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differe…

cs.CV2024★ 1 cited

From Pixels to Prose: A Large Dataset of Dense Image Captions

Vasu Singla, Kaiyu Yue, Sukriti Paul +7

Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To…

cs.CV2024★ 2 cited

PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting

Alex Hanson, Allen Tu, Vasu Singla +3

Recent advances in novel view synthesis have enabled real-time rendering speeds with high reconstruction accuracy. 3D Gaussian Splatting (3D-GS), a foundational point-based paramet…

cs.CV2023

A Simple and Efficient Baseline for Data Attribution on Images

Vasu Singla, Pedro Sandoval-Segura, Micah Goldblum +2

Data attribution methods play a crucial role in understanding machine learning models, providing insight into which training data points are most responsible for model outputs duri…

cs.CV2020★ 3 cited

ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors

Rohun Tripathi, Vasu Singla, Mahyar Najibi +3

The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines. Unfortunately, for the region proposal stage of…