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Hao Zhong

5 accepted papers

2026

ACTIVE-o3 : Empowering MLLMs with Active Perception via Pure Reinforcement Learning

ICML 2026poster

Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information. It is a critical component of efficient perception and decision-making in humans and advanced embodied agents. With the rise of Multimodal Large Language M…

Cited by 0SourceScholar
2026

Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching

CVPR 2026

Wide-baseline matching (WBM) requires integrating geometric understanding, viewpoint changes, fine-grained perception, and occlusion reasoning, making it a challenging testbed for spatial reasoning in multimodal large language models (MLLMs) deployed in physical environments. However, current MLLMs

Cited by 0SourceScholar
2026

Exploring Spatial Intelligence from a Generative Perspective

CVPR 2026

Spatial intelligence is essential for multimodal large language models, yet current benchmarks largely assess it only from an understanding perspective. We ask whether modern generative or unified multimodal models also possess generative spatial intelligence (GSI)--the ability to respect and manipu

Cited by 0SourcecodeScholar
2026

Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality

CVPR 2026

Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes

Cited by 0SourcecodeScholar
2025

Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration

NeurIPS 2025poster

Long-horizon video-audio reasoning and fine-grained pixel understanding impose conflicting requirements on omnimodal models: dense temporal coverage demands many low-resolution frames, whereas precise grounding calls for high-resolution inputs. We tackle this trade-off with a two-system architecture…

Cited by 0SourcecodeScholar