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James Smith

18 papers hereh-index 121.4k citations31 works total

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

author position
  • sole author3
  • first author8
  • middle author7

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV8
  • cs.LG7
  • cs.DS2
  • cs.CR1
same name
  • James Smith — 3 papers, h 1
  • James Smith — 3 papers
  • James Smith — 2 papers, h 2
  • James Smith — 2 papers, h 0
  • James Smith — 2 papers, h 1
  • James Smith — 2 papers

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

activity
20132023
most citedContinual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

20 citations · 59 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.DS2023

Robust Topological Orderings for Directed Graphs

James Smith

We modify the Pearce-Kelly algorithm that maintains a topological ordering for a directed acyclic graph in order to allow cycles to be tolerated. Cycles make topological orderings…

cs.CV2023★ 1 cited

Continual Adaptation of Vision Transformers for Federated Learning

Shaunak Halbe, James Seale Smith, Junjiao Tian +1

In this paper, we focus on the important yet understudied problem of Continual Federated Learning (CFL), where a server communicates with a set of clients to incrementally learn ne…

cs.CV2023★ 20 cited

Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA

James Seale Smith, Yen-Chang Hsu, Lingyu Zhang +4

Recent works demonstrate a remarkable ability to customize text-to-image diffusion models while only providing a few example images. What happens if you try to customize such model…

cs.CV2023

Going Beyond Nouns With Vision & Language Models Using Synthetic Data

Paola Cascante-Bonilla, Khaled Shehada, James Seale Smith +8

Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot ope…

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