◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Haonan Wang

National University of Singapore

15 papers hereh-index 7226 citations22 works total

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

author position
  • first author9
  • middle author6

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.CV4
  • cs.CR3
  • cs.LG2
  • cs.AI1
  • cs.MM1
affiliations
  • National University of Singapore
  • University of Illinois Urbana-Champaign
Homepage
same name
  • Haonan Wang — 44 papers, h 13
  • Haonan Wang — 17 papers, h 27
  • Haonan Wang — 9 papers, h 7
  • Haonan Wang — 8 papers, h 17
  • Haonan Wang — 5 papers, h 2
  • Haonan Wang — 3 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
20232026
most citedCan AI Be as Creative as Humans?

6 citations · 6 across the 10 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

ST-Gen4D: Embedding 4D Spatiotemporal Cognition into World Model for 4D Generation

Haonan Wang, Hanyu Zhou, Tao Gu +1

Generative models have achieved success in producing apparently coherent 2D videos, but remain challenging in the physical world due to lack of 4D spatiotemporal scale. Typically,…

cs.CV2026

Lost in Adaptation: Layer-Selective Recovery of Temporal Reasoning in Video-Language Models

Zihang Fu, Haonan Wang, Jian Kang +2

Multimodal adaptation can erode temporal reasoning (TR) in video-language models (VLMs), leaving models able to perceive salient events yet unable to infer their temporal and causa…

cs.CV2026

Cog2Gen3D: Sculpturing 3D Semantic-Geometric Cognition for 3D Generation

Haonan Wang, Hanyu Zhou, Haoyue Liu +2

Generative models have achieved success in producing semantically plausible 2D images, but it remains challenging in 3D generation due to the absence of spatial geometry constraint…

cs.CV2023

Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data

Haonan Wang, Minbin Huang, Runhui Huang +7

Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning. Improving CLIP typically requires additional data and ret…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.