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Nisha Huang

5 accepted papers

2026

Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment

AAAI 2026technical

The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature—spanning visual perception, cognition, and emotion—poses fundamental challenges. Although aesthetic descriptions offer a viable representati

Cited by 2SourcePDFScholar
2026

S$^2$-Guidance: Stochastic Self-Guidance for Training-Free Enhancement of Diffusion Models

ICLR 2026poster

Classifier-free Guidance (CFG) is a widely used technique in modern diffusion models for generating high-quality samples. However, through an empirical analysis on both Gaussian mixture models with closed-form solutions and real-world data distributions, we observe a discrepancy between the suboptim…

Cited by 0SourcecodeScholar
2026

Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

CVPR 2026

Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, while existing methods achieve high scores on automated reward metrics, they often lead to Preference Mode Collapse (PMC)-a

Cited by 0SourceScholar
2025

Taming Rectified Flow for Inversion and Editing

ICML 2025poster

Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, which could further limit their effectivenes…

2023

Inversion-Based Style Transfer With Diffusion Models

CVPR 2023poster

The artistic style within a painting is the means of expression, which includes not only the painting material, colors, and brushstrokes, but also the high-level attributes, including semantic elements and object shapes. Previous arbitrary example-guided artistic image generation methods often fail…