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arXiv Computer Vision

Multidimensional Observer Model and Perceptual Dimensions of Human Image Quality Assessment

The paper introduces a multidimensional observer model that represents images as distributions in a latent perceptual space and models human image quality judgment as comparisons of noisy samples. By aligning the model with neural representations in the primate ventral stream and fitting it to large-scale behavioral data, the authors demonstrate that the perceptual space required for human quality assessment is extremely low-dimensional relative to the image space. The study reveals that the structure of this perceptual space differs between low-level and high-level quality judgments, indicating that humans construct task-dependent perceptual spaces during visual decision making.

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arXiv AI
1d ago

Future Video Generation Better Aligns with the Human Visual Cortex than Observed Video

The study investigates how internal representations of video diffusion models align with human visual cortex responses. It finds that representations used for future video generation in an autoregressive (AR) model better match cortical activity than those for observed video, with future‑generation alignment concentrated in higher‑order visual areas. A behavioral experiment further shows that humans prefer videos enhanced by layers that align more strongly with cortical responses.

By Chang-Bae Bang, Hyungjin Chung, Byung-Hoon Kim
diffusionsafety
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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
nlpmultimodalbenchmarks
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Hugging Face Trending Papers
Jun 23

Benchmarking the Alignment of Data-Quality Metrics, Human Judgment and Land-Cover Segmentation Performance for Earth Observation

Volume and quality of datasets are crucial for deep learning model training, yet they are often constrained by availability and data acquisition costs. Synthetic data augmentation can extend existing datasets with realistic images, and the quality of these images is generally assessed through fidelity metrics such as FID, KID, IS, LPIPS and SSIM that measure structural or distributional similarity.

computer-visionbenchmarkssafety
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arXiv Computer Vision
Aug 27

PreResQ-R1: Response-Preference Disentangled Ranking-and-Scoring Reinforcement Optimization for Robust Visual Quality Assessment

PreResQ‑R1 introduces a Preference‑Response Disentangled Reinforcement Learning framework for Visual Quality Assessment that jointly optimizes absolute score regression and relative ranking consistency. It employs a dual‑branch reward system—modeling intra‑sample response coherence and inter‑sample preference alignment—trained with Group Relative Policy Optimization. The method extends to video quality assessment via a global‑temporal and local‑spatial data flow strategy, achieving state‑of‑the‑art results on 10 IQA and 5 VQA benchmarks with only 6K images and 28K videos, and provides human‑aligned reasoning traces.

By Zehui Feng, Weichuan Wang, Xiaohan Chen, Ting Han
llmsreinforcement-learningfine-tuningmultimodalbenchmarkssafety
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