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Hao Wang

9 papers hereh-index 4116 citations12 works total

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

author position
  • first author1
  • middle author7

Across the 8 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.AI1
  • cs.RO1
  • eess.IV1
same name
  • Hao Wang — 79 papers, h 101
  • Hao Wang — 34 papers, h 18
  • Hao Wang — 33 papers, h 13
  • Hao Wang — 24 papers, h 7
  • Hao Wang — 23 papers, h 9
  • Hao Wang — 22 papers, h 48

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 citedMambaBEV: An EV-based 3D detection model with Mamba2

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing 2025 · cs.CVShow all

4 papers · 2 filters

cs.CV2025

HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices

HyperAI Team, Yuchen Liu, Kaiyang Han +26

Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy direc…

cs.CV2025

EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence

Ding Zou, Feifan Wang, Mengyu Ge +17

The realization of Artificial General Intelligence (AGI) necessitates Embodied AI agents capable of robust spatial perception, effective task planning, and adaptive execution in ph…

cs.CV2025

Kwai Keye-VL 1.5 Technical Report

Biao Yang, Bin Wen, Boyang Ding +58

In recent years, the development of Large Language Models (LLMs) has significantly advanced, extending their capabilities to multimodal tasks through Multimodal Large Language Mode…

cs.CV2025

VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction

Hao Wang, Eiki Murata, Lingfang Zhang +11

Recent advances in multimodal large language models (MLLMs) have significantly enhanced video understanding capabilities, opening new possibilities for practical applications. Yet…

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