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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.CV2026

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

Henglin Liu, Fangyuan Kong, Jing Wang +7

The paper introduces concentrated Implicit Preference Optimization (cIPO), a post‑training method for text‑to‑video diffusion models that derives preference signals from reconstruc…

cs.CV2026

StyleGallery: Training-free and Semantic-aware Personalized Style Transfer from Arbitrary Image References

Boyu He, Yunfan Ye, Chang Liu +3

Despite the advancements in diffusion-based image style transfer, existing methods are commonly limited by 1) semantic gap: the style reference could miss proper content semantics,…

cs.CV2026

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

Henglin Liu, Nisha Huang, Chang Liu +6

The aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC. However, its inherently complex nature, spanning visual per…

cs.CV2025

DiverseGRPO: Mitigating Mode Collapse in Image Generation via Diversity-Aware GRPO

Henglin Liu, Huijuan Huang, Jing Wang +3

Reinforcement learning (RL), particularly GRPO, improves image generation quality significantly by comparing the relative performance of images generated within the same group. How…

cs.CV2025

UniMMVSR: A Unified Multi-Modal Framework for Cascaded Video Super-Resolution

Shian Du, Menghan Xia, Chang Liu +4

Cascaded video super-resolution has emerged as a promising technique for decoupling the computational burden associated with generating high-resolution videos using large foundatio…

cs.CV2025

PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution

Shian Du, Menghan Xia, Chang Liu +5

Pre-trained video generation models hold great potential for generative video super-resolution (VSR). However, adapting them for full-size VSR, as most existing methods do, suffers…