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Tianhe Wu

7 accepted papers

2026

Diversity-Preserved Distribution Matching Distillation for Fast Visual Synthesis

ICML 2026poster

Distribution matching distillation (DMD) aligns a multi-step generator with its few-step counterpart to enable high-quality generation under low inference cost. However, DMD tends to suffer from mode collapse, as its reverse-KL formulation inherently encourages mode-seeking behavior, for which exist…

Cited by 0SourceScholar
2026

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

ICML 2026poster

We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies primarily increase diversity by injecting more token-level randomness, which may introduce step-wise noise and leads to inco…

Cited by 0SourceScholar
2025

DP²O-SR: Direct Perceptual Preference Optimization for Real-World Image Super-Resolution

NeurIPS 2025poster

Benefiting from pre-trained text-to-image (T2I) diffusion models, real-world image super-resolution (Real-ISR) methods can synthesize rich and realistic details. However, due to the inherent stochasticity of T2I models, different noise inputs often lead to outputs with varying perceptual quality. Al…

Cited by 0SourceScholar
2025

Toward Generalized Image Quality Assessment: Relaxing the Perfect Reference Quality Assumption

CVPR 2025poster

Full-reference image quality assessment (FR-IQA) generally assumes that reference images are of perfect quality. However, this assumption is flawed due to the sensor and optical limitations of modern imaging systems. Moreover, recent generative enhancement methods are capable of producing images of…

2025

VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank

NeurIPS 2025spotlight

DeepSeek-R1 has demonstrated remarkable effectiveness in incentivizing reasoning and generalization capabilities of large language models (LLMs) through reinforcement learning. Nevertheless, the potential of reasoning-induced computation has not been thoroughly explored in the context of image quali…

Cited by 0SourcecodeScholar
2024

A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment

ECCV 2024poster

"While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) remains largely unexplored. In this paper, we condu…

2023

Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment

NeurIPS 2023poster

Blind Omnidirectional Image Quality Assessment (BOIQA) aims to objectively assess the human perceptual quality of omnidirectional images (ODIs) without relying on pristine-quality image information. It is becoming more significant with the increasing advancement of virtual reality (VR) technology. H…