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researcher

D. Chung

4 papers hereh-index 67k citations13 works total

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

author position
  • middle author1

Across the 1 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CL2
  • cs.CV1
  • cs.MM1
same name
  • D. Chung — 60 papers, h 33
  • D. Chung — 42 papers, h 45
  • D. Chung — 35 papers, h 17
  • D. Chung — 19 papers, h 32
  • D. Chung — 12 papers, h 8
  • D. Chung — 6 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

most citedGemini 1.5: Unlocking multimodal understanding across millions of tokens of context

297 citations · 346 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2025★ 49 cited

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431

In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…

cs.CV2024

VISTA: A Visual and Textual Attention Dataset for Interpreting Multimodal Models

Harshit, Tolga Tasdizen

The recent developments in deep learning led to the integration of natural language processing (NLP) with computer vision, resulting in powerful integrated Vision and Language Mode…

cs.MM2024

Pegasus-v1 Technical Report

Raehyuk Jung, Hyojun Go, Jaehyuk Yi +41

This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed t…

cs.CL2024★ 297 cited

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Petko Georgiev, Ving Ian Lei +1132

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…

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