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Heng Ji

6 papers hereh-index 5104 citations6 works total

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

author position
  • middle author1
  • last author5

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.CV2
  • cs.CL1
  • cs.HC1
same name
  • Heng Ji — 52 papers, h 37
  • Heng Ji — 45 papers, h 59
  • Heng Ji — 29 papers, h 22
  • Heng Ji — 24 papers, h 13
  • Heng Ji — 13 papers, h 1
  • Heng Ji — 12 papers, h 10

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 citedFiner: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models

4 citations · 8 across the 6 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.AI2024★ 1 cited

Infogent: An Agent-Based Framework for Web Information Aggregation

Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim +3

Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of…

cs.CL2024

Aligning LLMs with Individual Preferences via Interaction

Shujin Wu, May Fung, Cheng Qian +3

As large language models (LLMs) demonstrate increasingly advanced capabilities, aligning their behaviors with human values and preferences becomes crucial for their wide adoption.…

cs.AI2024

ARMADA: Attribute-Based Multimodal Data Augmentation

Xiaomeng Jin, Jeonghwan Kim, Yu Zhou +4

In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal d…

cs.CV2024★ 4 cited

Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models

Jeonghwan Kim, Heng Ji

Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. Whi…

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