arXiv Computer Vision
2d ago

Harnessing Vision-Language Models for Perceptual Quality Assessment and Autonomous Content Adjustment in Augmented Reality

arXiv:2610.00677v1 Announce Type: new Abstract: Advancements in augmented reality (AR) continue to foster innovative solutions, facilitating novel methodologies within educational systems, healthcare...

By Elias Rotondo (Duke University), Lin Duan (Duke University), Yanming Xiu (Duke University), Sangjun Eom (Duke University), Conrad Li (Duke University), Maria Gorlatova (Duke University)
Hugging Face Trending Papers
Jul 23

Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features

Conventional face recognition relies on static appearance cues and degrades in unconstrained settings with expression variation, occlusion, and poor lighting. We hypothesize that audiovisual expression dynamics carry identity-discriminative information complementary to static appearance, and that extracting this signal requires multimodal representations robust to the variable input quality of in-the-wild video.

arXiv Computer Vision
Sep 23

LLaVA-Assessor: Building the Foundation LMM For Visual Quality Assessment

LLaVA‑Assessor is a unified large multi‑modal model (LMM) designed for visual quality assessment, combining image and video inputs. It introduces a two‑task framework—quality interpretation and quality scoring—supported by an adaptive architecture, a rigorous human‑annotated dataset, and a machine‑synthesized data expansion pipeline. The model employs a prompt‑disentanglement strategy to stabilize multi‑task training and achieves strong performance across 11 quality scoring test sets and 4 interpretation benchmarks.

By Ziheng Jia, Zicheng Zhang, Jiaying Qian, Guangtao Zhai, Xiongkuo Min