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C. Taylor

4 papers hereh-index 350 citations5 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
same name
  • C. Taylor — 46 papers
  • C. Taylor — 32 papers, h 34
  • C. Taylor — 16 papers, h 8
  • C. Taylor — 9 papers, h 9
  • C. Taylor — 9 papers
  • C. Taylor — 9 papers, h 7

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedTowards Visual Foundational Models of Physical Scenes

1 citations · 1 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2024

THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models

Prannay Kaul, Zhizhong Li, Hao Yang +4

Mitigating hallucinations in large vision-language models (LVLMs) remains an open problem. Recent benchmarks do not address hallucinations in open-ended free-form responses, which…

cs.CV2024

Grounded Compositional and Diverse Text-to-3D with Pretrained Multi-View Diffusion Model

Xiaolong Li, Jiawei Mo, Ying Wang +7

In this paper, we propose an effective two-stage approach named Grounded-Dreamer to generate 3D assets that can accurately follow complex, compositional text prompts while achievin…

cs.CV2024

A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D

Xiaohan Fei, Chethan Parameshwara, Jiawei Mo +5

The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pr…

cs.CV2023★ 1 cited

Towards Visual Foundational Models of Physical Scenes

Chethan Parameshwara, Alessandro Achille, Matthew Trager +7

We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.