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Sung-Ju Lee

8 papers hereh-index 467 citations15 works total

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

author position
  • middle author2
  • last author6

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.CL1
  • cs.CV1
  • cs.HC1
  • eess.AS1
  • eess.SP1
same name
  • Sung-Ju Lee — 5 papers, h 3
  • Sung-Ju Lee — 5 papers, h 3
  • Sung-Ju Lee — 5 papers, h 7
  • Sung-Ju Lee — 4 papers, h 3
  • Sung-Ju Lee — 3 papers, h 2
  • Sung-Ju Lee — 2 papers, h 2

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
20242026
most citedTest-Time Adaptation with Binary Feedback

1 citations · 2 across the 8 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.LG2025

SNAP: Low-Latency Test-Time Adaptation with Sparse Updates

Hyeongheon Cha, Dong Min Kim, Hye Won Chung +2

Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts. However, existing methods rely on frequent adaptation and high computatio…

eess.AS2025

Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization

Hyungjun Yoon, Seungjoo Lee, Yu Yvonne Wu +11

Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to tw…

cs.CV2025

QuRe: Query-Relevant Retrieval through Hard Negative Sampling in Composed Image Retrieval

Jaehyun Kwak, Ramahdani Muhammad Izaaz Inhar, Se-Young Yun +1

Composed Image Retrieval (CIR) retrieves relevant images based on a reference image and accompanying text describing desired modifications. However, existing CIR methods only focus…

cs.LG2025★ 1 cited

Test-Time Adaptation with Binary Feedback

Taeckyung Lee, Sorn Chottananurak, Junsu Kim +3

Deep learning models perform poorly when domain shifts exist between training and test data. Test-time adaptation (TTA) is a paradigm to mitigate this issue by adapting pre-trained…

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