The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.
citation dossier
Litrico, A
1Pith papers citing it
1reference links
cs.CVtop field · 1 papers
UNVERDICTEDtop verdict bucket · 1 papers
why this work matters in Pith
Pith has found this work in 1 reviewed paper. Its strongest current cluster is cs.CV (1 papers). The largest review-status bucket among citing papers is UNVERDICTED (1 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Rethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model
The paper introduces the VODA setting for domain adaptation from scratch using vision-language models and presents TS-DRD, which achieves competitive performance on standard benchmarks without source models.