collaborators

7 papers

cs.CV2026

COMEX: A Composition-Grounded Benchmark and Learning Framework for Explainable Aesthetic Image Cropping

Rui Yang, Wei Zhou, Dingyong Gou +5

Explainable aesthetic image cropping requires not only localizing a visually pleasing crop but also explaining why it is preferred. Existing crop-and-explain methods largely treat…

cs.CV2026

The 1st PortraitCraft Challenge: A CVPR 2026 Workshop Competition on Portrait Composition Understanding and Generation

Zijie Lou, Youyun Tang, Xiaochao Qu +40

This paper presents an overview of the inaugural PortraitCraft Challenge, held as one of the official competitions at CVPR 2026. The challenge focuses on portrait composition under…

eess.AS2026

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

Zhiyuan Zhu, Yixuan Chen, Yiwen Shao +13

Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial rel…

cs.RO2026

LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving

Ruoyu Yao, Pei Liu, Ruiguo Zhong +3

While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited reasoning diversity, high c…

cs.CL2026

OpenSpatial: A Principled Data Engine for Empowering Spatial Intelligence

Jianhui Liu, Haoze Sun, Wenbo Li +11

Spatial understanding is a fundamental cornerstone of human-level intelligence. Nonetheless, current research predominantly focuses on domain-specific data production, leaving a cr…

cs.CV2025

DrivePI: Spatial-aware 4D MLLM for Unified Autonomous Driving Understanding, Perception, Prediction and Planning

Zhe Liu, Runhui Huang, Rui Yang +6

Although multi-modal large language models (MLLMs) have shown strong capabilities across diverse domains, their application in generating fine-grained 3D perception and prediction…